3780576
Evaluating the Influence of Chromatic and Luminance Stimuli on SSVEPs from Behind-the-Ears and Occipital Areas
This work presents a study of chromatic and luminance stimuli in low-, medium-, and high-frequency stimulation to evoke steady-state visual evoked potential (SSVEP) in the behind-the-ears area. Twelve healthy subjects participated in this study. The electroencephalogram (EEG) was measured on occipital (Oz) and left and right temporal (TP9 and TP10) areas. The SSVEP was evaluated in terms of amplitude, signal-to-noise ratio (SNR), and detection accuracy using power spectral density analysis (PSDA), canonical correlation analysis (CCA), and temporally local multivariate synchronization index (TMSI) methods. It was found that stimuli based on suitable color and luminance elicited stronger SSVEP in the behind-the-ears area, and that the response of the SSVEP was related to the flickering frequency and the color of the stimuli. Thus, green-red stimulus elicited the highest SSVEP in medium-frequency range, and green-blue stimulus elicited the highest SSVEP in high-frequency range, reaching detection accuracy rates higher than 80%. These findings will aid in the development of more comfortable, accurate and stable BCIs with electrodes positioned on the behind-the-ears (hairless) areas.
2524914
Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena
Abstract. The individual properties of visual objects, like form or color, are represented in different areas in our visual cortex. In order to perceive one coherent object, its features have to be bound together. This was found to be achieved in cat and monkey brains by temporal correlation of the firing rates of neurons which code the same object. This firing rate is predominantly observed in the gamma frequency range (approx. 30–80 Hz, mainly around 40 Hz). In addition, it has been shown in humans that stimuli which flicker at gamma frequencies are processed faster by our brains than when they flicker at different frequencies. These effects could be due to neural oscillators, which preferably oscillate at certain frequencies, so-called resonance frequencies. It is also known that neurons in visual cortex respond to flickering stimuli at the frequency of the flickering light. If neural oscillators exist with resonance frequencies, they should respond more strongly to stimulation with their resonance frequency. We performed an experiment, where ten human subjects were presented flickering light at frequencies from 1 to 100 Hz in 1-Hz steps. The event-related potentials exhibited steady-state oscillations at all frequencies up to at least 90 Hz. Interestingly, the steady-state potentials exhibited clear resonance phenomena around 10, 20, 40 and 80 Hz. This could be a potential neural basis for gamma oscillations in binding experiments. The pattern of results resembles that of multiunit activity and local field potentials in cat visual cortex.
13062862
Commanding a robotic wheelchair with a high-frequency steady-state visual evoked potential based brain-computer interface.
This work presents a brain-computer interface (BCI) used to operate a robotic wheelchair. The experiments were performed on 15 subjects (13 of them healthy). The BCI is based on steady-state visual-evoked potentials (SSVEP) and the stimuli flickering are performed at high frequency (37, 38, 39 and 40 Hz). This high frequency stimulation scheme can reduce or even eliminate visual fatigue, allowing the user to achieve a stable performance for long term BCI operation. The BCI system uses power-spectral density analysis associated to three bipolar electroencephalographic channels. As the results show, 2 subjects were reported as SSVEP-BCI illiterates (not able to use the BCI), and, consequently, 13 subjects (12 of them healthy) could navigate the wheelchair in a room with obstacles arranged in four distinct configurations. Volunteers expressed neither discomfort nor fatigue due to flickering stimulation. A transmission rate of up to 72.5 bits/min was obtained, with an average of 44.6 bits/min in four trials. These results show that people could effectively navigate a robotic wheelchair using a SSVEP-based BCI with high frequency flickering stimulation.
5622318
A lower limb exoskeleton control system based on steady state visual evoked potentials.
OBJECTIVE
We have developed an asynchronous brain-machine interface (BMI)-based lower limb exoskeleton control system based on steady-state visual evoked potentials (SSVEPs).


APPROACH
By decoding electroencephalography signals in real-time, users are able to walk forward, turn right, turn left, sit, and stand while wearing the exoskeleton. SSVEP stimulation is implemented with a visual stimulation unit, consisting of five light emitting diodes fixed to the exoskeleton. A canonical correlation analysis (CCA) method for the extraction of frequency information associated with the SSVEP was used in combination with k-nearest neighbors.


MAIN RESULTS
Overall, 11 healthy subjects participated in the experiment to evaluate performance. To achieve the best classification, CCA was first calibrated in an offline experiment. In the subsequent online experiment, our results exhibit accuracies of 91.3 ± 5.73%, a response time of 3.28 ± 1.82 s, an information transfer rate of 32.9 ± 9.13 bits/min, and a completion time of 1100 ± 154.92 s for the experimental parcour studied.


SIGNIFICANCE
The ability to achieve such high quality BMI control indicates that an SSVEP-based lower limb exoskeleton for gait assistance is becoming feasible.
64216342
SSVEP-Based BCI for Lower Limb Rehabilitation
SSVEPs are less vulnerable to noise than other kinds of EEG signals and have, therefore, recently become popular in BCI applications. To our knowledge, this chapter is the first to demonstrate an online asynchronous analogue SSVEP-based BCI for lower limb rehabilitation in which the movement of a robotic exoskeleton is continuously controlled by the user’s intent. Such patient participation has proved to be one of the most important factors for rehabilitating the neural system after injury or stroke. Three new and different training protocols were developed specifically for rehabilitation and tested with the ANBF. Results with six healthy participants were extremely good, with an accuracy to within a knee angle of 1° after simple training. These results are promising for the future development of brain controlled rehabilitation devices.
16682661
A High-Speed Brain Speller using steady-State Visual evoked potentials
Implementing a complex spelling program using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a challenge due to difficulties in stimulus presentation and target identification. This study aims to explore the feasibility of mixed frequency and phase coding in building a high-speed SSVEP speller with a computer monitor. A frequency and phase approximation approach was developed to eliminate the limitation of the number of targets caused by the monitor refresh rate, resulting in a speller comprising 32 flickers specified by eight frequencies (8-15 Hz with a 1 Hz interval) and four phases (0°, 90°, 180°, and 270°). A multi-channel approach incorporating Canonical Correlation Analysis (CCA) and SSVEP training data was proposed for target identification. In a simulated online experiment, at a spelling rate of 40 characters per minute, the system obtained an averaged information transfer rate (ITR) of 166.91 bits/min across 13 subjects with a maximum individual ITR of 192.26 bits/min, the highest ITR ever reported in electroencephalogram (EEG)-based BCIs. The results of this study demonstrate great potential of a high-speed SSVEP-based BCI in real-life applications.
23688930
Effect of higher frequency on the classification of steady-state visual evoked potentials.
OBJECTIVE
Most existing brain-computer interface (BCI) designs based on steady-state visual evoked potentials (SSVEPs) primarily use low frequency visual stimuli (e.g., <20 Hz) to elicit relatively high SSVEP amplitudes. While low frequency stimuli could evoke photosensitivity-based epileptic seizures, high frequency stimuli generally show less visual fatigue and no stimulus-related seizures. The fundamental objective of this study was to investigate the effect of stimulation frequency and duty-cycle on the usability of an SSVEP-based BCI system.


APPROACH
We developed an SSVEP-based BCI speller using multiple LEDs flickering with low frequencies (6-14.9 Hz) with a duty-cycle of 50%, or higher frequencies (26-34.7 Hz) with duty-cycles of 50%, 60%, and 70%. The four different experimental conditions were tested with 26 subjects in order to investigate the impact of stimulation frequency and duty-cycle on performance and visual fatigue, and evaluated with a questionnaire survey. Resting state alpha powers were utilized to interpret our results from the neurophysiological point of view.


MAIN RESULTS
The stimulation method employing higher frequencies not only showed less visual fatigue, but it also showed higher and more stable classification performance compared to that employing relatively lower frequencies. Different duty-cycles in the higher frequency stimulation conditions did not significantly affect visual fatigue, but a duty-cycle of 50% was a better choice with respect to performance. The performance of the higher frequency stimulation method was also less susceptible to resting state alpha powers, while that of the lower frequency stimulation method was negatively correlated with alpha powers.


SIGNIFICANCE
These results suggest that the use of higher frequency visual stimuli is more beneficial for performance improvement and stability as time passes when developing practical SSVEP-based BCI applications.
8935930
Mobile robot navigation with a self-paced brain–computer interface based on high-frequency SSVEP
SUMMARY A brain–computer interface (BCI) is a system for commanding a device by means of brain signals without having to move any muscle. One kind of BCI is based on Steady-State Visual Evoked Potentials (SSVEP), which are evoked visual cortex responses elicited by a twinkling light source. Stimuli can produce visual fatigue; however, it has been well established that high-frequency SSVEP (>30 Hz) does not. In this paper, a mobile robot is remotely navigated into an office environment by means of an asynchronous high-frequency SSVEP-based BCI along with the image of a video camera. This BCI uses only three electroencephalographic channels and a simple processing signal method. The robot velocity control and the avoidance obstacle algorithms are also herein described. Seven volunteers were able to drive the mobile robot towards two different places. They had to evade desks and shelves, pass through a doorway and navigate in a corridor. The system was designed so as to allow the subject to move about without restrictions, since he/she had full robot movement's control. It was concluded that the developed system allows for remote mobile robot navigation in real indoor environments using brain signals. The proposed system is easy to use and does not require any special training. The user's visual fatigue is reduced because high-frequency stimulation is employed and, furthermore, the user gazes at the stimulus only when a command must be sent to the robot.
26605565
Frequency recognition in an SSVEP-based brain computer interface using empirical mode decomposition and refined generalized zero-crossing
This paper presents an empirical mode decomposition (EMD) and refined generalized zero crossing (rGZC) approach to achieve frequency recognition in steady-stated visual evoked potential (SSVEP)-based brain computer interfaces (BCIs). Six light emitting diode (LED) flickers with high flickering rates (30, 31, 32, 33, 34, and 35 Hz) functioned as visual stimulators to induce the subjects' SSVEPs. EEG signals recorded in the Oz channel were segmented into data epochs (0.75 s). Each epoch was then decomposed into a series of oscillation components, representing fine-to-coarse information of the signal, called intrinsic mode functions (IMFs). The instantaneous frequencies in each IMF were calculated by refined generalized zero-crossing (rGZC). IMFs with mean instantaneous frequencies (f(GZC)) within 29.5 Hz and 35.5 Hz (i.e., 29.5≤f(GZC)≤35.5 Hz) were designated as SSVEP-related IMFs. Due to the time-locked and phase-locked characteristics of SSVEP, the induced SSVEPs had the same frequency as the gazing visual stimulator. The LED flicker that contributed the majority of the frequency content in SSVEP-related IMFs was chosen as the gaze target. This study tests the proposed system in five male subjects (mean age=25.4±2.07 y/o). Each subject attempted to activate four virtual commands by inputting a sequence of cursor commands on an LCD screen. The average information transfer rate (ITR) and accuracy were 36.99 bits/min and 84.63%. This study demonstrates that EMD is capable of extracting SSVEP data in SSVEP-based BCI system.
7655220
Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment
This paper presents the application of an effective EEG-based brain-computer interface design for binary control in a visually elaborate immersive 3D game. The BCI uses the steady-state visual evoked potential (SSVEP) generated in response to phase-reversing checkerboard patterns. Two power-spectrum estimation methods were employed for feature extraction in a series of offline classification tests. Both methods were also implemented during real-time game play. The performance of the BCI was found to be robust to distracting visual stimulation in the game and relatively consistent across six subjects, with 41 of 48 games successfully completed. For the best performing feature extraction method, the average real-time control accuracy across subjects was 89%. The feasibility of obtaining reliable control in such a visually rich environment using SSVEPs is thus demonstrated and the impact of this result is discussed.
32387981
Development of a Low-Cost FPGA-Based SSVEP BCI Multimedia Control System
This paper proposes a low-cost field-programmable gate-array (FPGA)-based brain-computer interface (BCI) multimedia control system, different from the BCI system, which uses bulky and expensive electroencephalography (EEG) measurement equipment, personal computer, and commercial real-time signal-processing software. The proposed system combines a customized stimulation panel, a brainwave-acquisition circuit, and an FPGA-based real-time signal processor and allows users to use their brainwave to communicate with or control multimedia devices by themselves. This study also designs a light-emitting diode stimulation panel instead of cathode ray tube or liquid-crystal display used in existing studies, to induce a stronger steady-state visual evoked potential (SSVEP), a kind of EEG, used as the input signal of the proposed BCI system. Implementing a prototype of the SSVEP-based BCI multimedia control system verifies the effectiveness of the proposed system. Experimental results show that the subjects' SSVEP can successfully control the multimedia device through the proposed BCI system with high identification accuracy.
1609931
Measuring Steady-State Visual Evoked Potentials from non-hair-bearing areas
Steady-State Visual Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) applications have been widely applied in laboratories around the world in the recent years. Many studies have shown that the best locations to acquire SSVEPs were from the occipital areas of the scalp. However, for some BCI users such as quadriparetic patients lying face up during ventilation, it is difficult to access the occipital sites. Even for the healthy BCI users, acquiring good-quality EEG signals from the hair-covered occipital sites is inevitably more difficult because it requires skin preparation by a skilled technician and conductive gel usage. Therefore, finding an alternative approach to effectively extract high-quality SSVEPs for BCI practice is highly desirable. Since the non-hair-bearing scalp regions are more accessible by all different types of EEG sensors, this study systematically and quantitatively investigated the feasibility of measuring SSVEPs from non-hair-bearing regions, compared to those measured from the occipital areas. Empirical results showed that the signal quality of the SSVEPs from non-hair-bearing areas was comparable with, if not better than, that measured from hair-covered occipital areas. These results may significantly improve the practicality of a BCI system in real-life applications; especially used in conjunction with newly available dry EEG sensors.
8388011
Toward non-hair-bearing brain-computer interfaces for neurocognitive lapse detection
Recent advances in mobile electroencephalogram (EEG) acquisition based on dry electrodes have started moving Brain-Computer Interface (BCI) applications from well-controlled laboratory settings to real-world environments. However, the application mechanisms and high impedance of dry electrodes over the hair-covered areas remain challenging for everyday use of BCI. In addition, whole-scalp recordings are not always necessary or applicable due to various practical constrains. Therefore, alternative montages for EEG recordings to meet the everyday needs are in-demand. Inspired by our previous work on measuring non-hair-bearing steady state visual evoked potentials for BCI applications, this study explores the feasibility and efficacy of detecting cognitive lapses of participants based on EEG signals collected from the non-hair-bearing areas. Study results suggest that informative EEG features associated with lapses could be assessed from non-hair-bearing areas with comparable accuracy obtained from the whole-scalp EEG. The design principles, validation processes and promising findings reported in this study may enable and/or facilitate numerous BCI applications in real-world environments.
9632389
Human Cerebral Activation during Steady-State Visual-Evoked Responses
Flicker stimuli of variable frequency (2-90 Hz) elicit a steady-state visual-evoked response (SSVER) in the electroencephalogram (EEG) with the same frequency as the stimulus. In humans, the amplitude of this response peaks at ∼15 Hz, decreasing at higher stimulation frequencies. It was not known whether this peak response corresponds to increased synaptic activity in the visual cortex or to other mechanisms [for instance, the temporal coherence (phase summation) of evoked responses]. We studied the SSVER in 16 normal volunteers by means of visual stimulation at 14 different frequencies (from 5 to 60 Hz) while recording the EEG. In nine subjects of the group, we measured regional cerebral blood flow (rCBF) with positron emission tomography (PET)-H215O at rest and during visual stimulation at five different frequencies: 5, 10, 15, 25, and 40 Hz. We confirmed that the amplitude of the SSVER in occipital regions peaks at 15 Hz stimulation. Applying to the PET rCBF data a contrast weighted by the amplitude of the SSVER, we determined that the primary visual cortex rCBF follows an activation pattern similar to the SSVER. This finding suggests that the amplitude of the SSVER corresponds to increased synaptic activity, specifically in Brodmann's area 17. Additionally, this study showed that visual stimulation at 40 Hz causes selective activation of the macular region of the visual cortex, and that a region in the dorsal aspect of the Crus I lobule of the left cerebellar hemisphere is activated during repetitive visual stimulation.
13753799
fMRI responses in medial frontal cortex that depend on the temporal frequency of visual input
Functional networks in the human brain have been investigated using electrophysiological methods (EEG/MEG, LFP, and MUA) and steady-state paradigms that apply periodic luminance or contrast modulation to drive cortical networks. We have used this approach with fMRI to characterize a cortical network driven by a checkerboard reversing at a fixed frequency. We found that the fMRI signals in voxels located in occipital cortex were increased by checkerboard reversal at frequencies ranging from 3 to 14 Hz. In contrast, the response of a cluster of voxels centered on basal medial frontal cortex depended strongly on the reversal frequency, consistently exhibiting a peak in the response for specific reversal frequencies between 3 and 5 Hz in each subject. The fMRI signals at the frontal voxels were positively correlated indicating a homogeneous cluster. Some of the occipital voxels were positively correlated to the frontal voxels apparently forming a large-scale functional network. Other occipital voxels were negatively correlated to the frontal voxels, suggesting a functionally distinct network. The results provide preliminary fMRI evidence that during visual stimulation, input frequency can be varied to engage different functional networks.
14988144
Topography of cortical activation differs for fundamental and harmonic frequencies of the steady-state visual-evoked responses. An EEG and PET H215O study.
In humans, visual flicker stimuli of graded frequency (2-90 Hz) elicit an electroencephalographic (EEG) steady-state visual-evoked response (SSVER) with the same fundamental frequency as the stimulus and, in addition, a series of harmonic responses. The fundamental component of the SSVER is generated by increased synaptic activity in primary visual cortex (V1). We set out to determine the cortical origin of the harmonic responses in humans. For this purpose, we recorded the SSVERs at 5 different frequencies (5, 10, 15, 25, and 40 Hz) and measured regional cerebral blood flow (rCBF) with positron emission tomography-H(2)(15)O at rest and during visual stimulation at the same frequencies. The rCBF contrast weighted by the amplitude of the SSVERs first harmonics showed activation of a swath of cortex perpendicular to V1, including mostly the inferior half of the parieto-occipital sulcus. This area overlapped minimally with the primary visual cortex activated by the fundamental frequency. A different method, estimating EEG cortical source current density with low-resolution brain electromagnetic tomography, gave the same results. Our finding suggests that the inferior portion of the banks of the parieto-occipital sulci contains association visual cortex involved in the processing of stimuli that can be as simple as a flickering light source.
6304520
Steady-State Visual Evoked Potentials: Distributed Local Sources and Wave-Like Dynamics Are Sensitive to Flicker Frequency
Summary:Steady-state visual evoked potentials (SSVEPs) are used in cognitive and clinical studies of brain function because of excellent signal-to-noise ratios and relative immunity to artifacts. SSVEPs also provide a means to characterize preferred frequencies of neocortical dynamic processes. In this study, SSVEPs were recorded with 110 electrodes while subjects viewed random dot patterns flickered between 3 and 30 Hz. Peaks in SSVEP power were observed at delta (3 Hz), lower alpha (7 and 8 Hz), and upper alpha band (12 and 13 Hz) frequencies; the spatial distribution of SSVEP power is also strongly dependent on the input frequency suggesting cortical resonances. We characterized the cortical sources that generate SSVEPs at different input frequencies by applying surface Laplacians and spatial spectral analysis. Laplacian SSVEPs recorded are sensitive to small changes (1–2 Hz) in the input frequency at occipital and parietal electrodes indicating distinct local sources. At 10 Hz, local source activity occurs in multiple cortical regions; Laplacian SSVEPs are also observed in lateral frontal electrodes. Laplacian SSVEPs are negligible at many frontal electrodes that elicit strong potential SSVEPs at delta, lower alpha, and upper alpha bands. One-dimensional (anterior-posterior) spatial spectra indicate that distinct large-scale source distributions contribute SSVEP power in these frequency bands. In the upper alpha band, spatial spectra indicate the presence of long-wavelength (> 15 cm) traveling waves propagating from occipital to prefrontal electrodes. In the delta and lower alpha band, spatial spectra indicate that long-wavelength source distributions over posterior and anterior regions form standing-wave patterns. These results suggest that the SSVEP is generated by both (relatively stationary) localized sources and distributed sources that exhibit characteristics of wave phenomena.
31350937
Acquisition of typical EEG waveforms during fMRI: SSVEP, LRP, and frontal theta
Recent work has demonstrated the feasibility of simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Virtually no systematic comparisons between EEG recorded inside and outside the MR scanner have been conducted, and it is unknown if different kinds of frequency mix, topography, and domain-specific processing are uniformly recordable within the scanner environment. The aim of the study was to investigate several typical EEG waveforms in the same subjects inside the magnet during fMRI and outside the MR examination room. We examined whether uniform artifact subtraction allows the extraction of these different EEG waveforms inside the scanner during EPI scanning to the same extent as outside the scanner. Three well-established experiments were conducted, eliciting steady state visual evoked potentials (SSVEP), lateralized readiness potentials (LRP), and frontal theta enhancement induced by mental addition. All waveforms could be extracted from the EEG recorded during fMRI. Substantially no differences in these waveforms of interest were found between gradient-switching and intermediate epochs during fMRI (only the SSVEP-experiment was designed for a comparison of gradient-with intermediate epochs), or between waveforms recorded inside the scanner during EPI scanning and outside the MR examination room (all experiments). However, non-specific amplitude differences were found between inside and outside recorded EEG at lateral electrodes, which were not in any interaction with the effects of interest. The source of these differences requires further exploration. The high concordance of activation patterns with published results demonstrates that EPI-images could be acquired during EEG recording without significant distortion.
7703109
The temporal frequency tuning of human visual cortex investigated using synthetic aperture magnetometry
Using synthetic aperture magnetometry (SAM) analyses of magnetoencephalographic (MEG) data, we investigated the variation in cortical response magnitude and frequency as a function of stimulus temporal frequency. In two separate experiments, a reversing checkerboard stimulus was used in the right or left lower visual field at frequencies from 0 to 21 Hz. Average temporal frequency tuning curves were constructed for regions-of-interest located within medial visual cortex and V5/MT. In medial visual cortex, it was found that both the frequency and magnitude of the steady-state response varied as a function of the stimulus frequency, with multiple harmonics of the stimulus frequency being found in the response. The maximum fundamental response was found at a stimulus frequency of 8 Hz, whilst the maximum broadband response occurred at 4 Hz. In contrast, the magnitude and frequency content of the evoked onset response showed no dependency on stimulus frequency. Whilst medial visual cortex showed a power increase during stimulation, extra-striate areas such as V5/MT exhibited a bilateral event-related desynchronisation (ERD). The frequency content of this ERD did not depend on the stimulus frequency but was a broadband power reduction across the 5-20 Hz frequency range. The magnitude of this ERD within V5/MT was strongly low-pass tuned for stimulus frequency, and showed only a moderate preference for stimuli in the contralateral visual field.
5137796
Asynchronous BCI control using high-frequency SSVEP
BackgroundSteady-State Visual Evoked Potential (SSVEP) is a visual cortical response evoked by repetitive stimuli with a light source flickering at frequencies above 4 Hz and could be classified into three ranges: low (up to 12 Hz), medium (12-30) and high frequency (> 30 Hz). SSVEP-based Brain-Computer Interfaces (BCI) are principally focused on the low and medium range of frequencies whereas there are only a few projects in the high-frequency range. However, they only evaluate the performance of different methods to extract SSVEP.MethodsThis research proposed a high-frequency SSVEP-based asynchronous BCI in order to control the navigation of a mobile object on the screen through a scenario and to reach its final destination. This could help impaired people to navigate a robotic wheelchair. There were three different scenarios with different difficulty levels (easy, medium and difficult). The signal processing method is based on Fourier transform and three EEG measurement channels.ResultsThe research obtained accuracies ranging in classification from 65% to 100% with Information Transfer Rate varying from 9.4 to 45 bits/min.ConclusionsOur proposed method allows all subjects participating in the study to control the mobile object and to reach a final target without prior training.
20265661
High Frequency SSVEP-BCI With Hardware Stimuli Control and Phase-Synchronized Comb Filter
We present an efficient implementation of brain–computer interface (BCI) based on high-frequency steady state visually evoked potentials (SSVEP). Individual shape of the SSVEP response is extracted by means of a feedforward comb filter, which adds delayed versions of the signal to itself. Rendering of the stimuli is controlled by specialized hardware (BCI Appliance). Out of 15 participants of the study, nine were able to produce stable response in at least eight out of ten frequencies from the 30–39 Hz range. They achieved on average 96±4% accuracy and 47±5 bit/min information transfer rate (ITR) for an optimized simple seven-letter speller, while generic full-alphabet speller allowed in this group for 89±9% accuracy and 36±9 bit/min ITR. These values exceed the performances of high-frequency SSVEP-BCI systems reported to date. Classical approach to SSVEP parameterization by relative spectral power in the frequencies of stimulation, implemented on the same data, resulted in significantly lower performance. This suggests that specific shape of the response is an important feature in classification. Finally, we discuss the differences in SSVEP responses of the participants who were able or unable to use the interface, as well as the statistically significant influence of the layout of the speller on the speed of BCI operation.
9776095
The steady-state visual evoked potential in vision research: A review.
Periodic visual stimulation and analysis of the resulting steady-state visual evoked potentials were first introduced over 80 years ago as a means to study visual sensation and perception. From the first single-channel recording of responses to modulated light to the present use of sophisticated digital displays composed of complex visual stimuli and high-density recording arrays, steady-state methods have been applied in a broad range of scientific and applied settings.The purpose of this article is to describe the fundamental stimulation paradigms for steady-state visual evoked potentials and to illustrate these principles through research findings across a range of applications in vision science.
12187240
Brain Computer Interfaces, a Review
A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.
17171671
Steady-State Visual Evoked Potential-Based Computer Gaming on a Consumer-Grade EEG Device
In this paper, we introduce a game in which the player navigates an avatar through a maze by using a brain-computer interface (BCI) that analyzes the steady-state visual evoked potential (SSVEP) responses recorded with electroencephalography (EEG) on the player's scalp. The four-command control game, called The Maze, was specifically designed around an SSVEP BCI and validated in several EEG setups when using a traditional electrode cap with relocatable electrodes and a consumer-grade headset with fixed electrodes (Emotiv EPOC). We experimentally derive the parameter values that provide an acceptable tradeoff between accuracy of game control and interactivity, and evaluate the control provided by the BCI during gameplay. As a final step in the validation of the game, a population study on a broad audience was conducted with the EPOC headset in a real-world setting. The study revealed that the majority (85%) of the players enjoyed the game in spite of its intricate control (mean accuracy 80.37%, mean mission time ratio 0.90). We also discuss what to take into account while designing BCI-based games.
2233024
Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives
After 40 years of investigation, steady-state visually evoked potentials (SSVEPs) have been shown to be useful for many paradigms in cognitive (visual attention, binocular rivalry, working memory, and brain rhythms) and clinical neuroscience (aging, neurodegenerative disorders, schizophrenia, ophthalmic pathologies, migraine, autism, depression, anxiety, stress, and epilepsy). Recently, in engineering, SSVEPs found a novel application for SSVEP-driven brain-computer interface (BCI) systems. Although some SSVEP properties are well documented, many questions are still hotly debated. We provide an overview of recent SSVEP studies in neuroscience (using implanted and scalp EEG, fMRI, or PET), with the perspective of modern theories about the visual pathway. We investigate the steady-state evoked activity, its properties, and the mechanisms behind SSVEP generation. Next, we describe the SSVEP-BCI paradigm and review recently developed SSVEP-based BCI systems. Lastly, we outline future research directions related to basic and applied aspects of SSVEPs.
10148039
BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI?
Brain-computer interface (BCI) systems enable communication without movement. It is unclear why some BCI approaches or parameters are less effective with some users. This study elucidates BCI demographics by exploring correlations among BCI performance, personal preferences, and different subject factors such as age or gender. Results showed that most people, despite having no prior BCI experience, could use the Bremen SSVEP BCI system in a very noisy field setting. Performance tended to be better in both young and female subjects. Most subjects stated that they did not consider the flickering stimuli annoying and would use or recommend this BCI system. These and other demographic analyses may help identify the best BCI for each user.
62058403
Brain-computer Interface Based on Visual Evoked Potentials to Command Autonomous Robotic Wheelchair
This paper proposes the integration of two systems: a brain computer interface (BCI) based on steady-state visual evoked potentials (SSVFPs) and an autonomous robotic wheelchair, with the former being used to command the latter. The signals used in this work come from individuals who are visually stimulated. The stimuli are black-and-white checkerboards, like stripes, flickering at different frequencies. Four experiments were performed for the BCI development. For all experiments, the volunteers were asked to watch a stimulation screen with a central stripe or to watch a stimulation screen with four stripes presented simultaneously. one at each side, one at the top and one at the bottom. The FEG signal analysis consists of two steps: feature extraction, performed using a statistic test, and classification, performed by a rule-based classifier, This kind of classifier obtains good results in a short lime and does not demand any training. The result is a system with high classification rate (up to %), high information transfer rate ITR) (up to 1.01.66 bits/min), and processing time of about 120ms for each incremental analysis. Each frequency value can he associated to a user command or a user feeling. The user, who is seated on the wheelchair, can thus choose a specific place to move to. Upon such choice, the control system onboard the wheelchair generates reference paths with low risk of collision, connecting the current position to the chosen one. Therefore, a system to allow people with severe motor disfunction to have the quality of their lives improved is the proposal of the work.
5996098
Developing an online steady-state visual evoked potential-based brain-computer interface system using EarEEG
The purpose of this study is to demonstrate an online steady-state visual evoked potential (SSVEP)-based BCI system using EarEEG. EarEEG is a novel recording concept where electrodes are embedded on the surface of earpieces customized to the individual anatomical shape of users' ear. It has been shown that the EarEEG can be used to record SSVEPs in previous studies. However, a long distance between the visual cortex and the ear makes the signal-to-noise ratio (SNR) of SSVEPs acquired by the EarEEG relatively low. Recently, filter bank- and training data-based canonical correlation analysis algorithms have shown significant performance improvement in terms of accuracy of target detection and information transfer rate (ITR). This study implemented an online four-class SSVEP-based BCI system using EarEEG. Four subjects participated in offline and online BCI experiments. For the offline classification, an average accuracy of 82.71±11.83 % was obtained using 4 sec-long SSVEPs acquired from earpieces. In the online experiment, all subjects successfully completed the tasks with an average accuracy of 87.92±12.10 %, leading to an average ITR of 16.60±6.55 bits/min. The results suggest that EarEEG can be used to perform practical BCI applications. The EarEEG has the potential to be used as a portable EEG recordings platform, that could enable real-world BCI applications.
8352015
Soft, curved electrode systems capable of integration on the auricle as a persistent brain–computer interface
Significance Conventional electroencephalogram (EEG) recording systems, particularly the hardware components that form the physical interfaces to the head, have inherent drawbacks that limit the widespread use of continuous EEG measurements for medical diagnostics, sleep monitoring, and cognitive control. Here we introduce soft electronic constructs designed to intimately conform to the complex surface topology of the auricle and the mastoid, to provide long-term, high-fidelity recording of EEG data. Systematic studies reveal key aspects of the extreme levels of bending and stretching that are involved in mounting on these surfaces. Examples in persistent brain–computer interfaces, including text spellers with steady-state visually evoked potentials and event-related potentials, with viable operation over periods of weeks demonstrate important advances over alternative brain–computer interface technologies. Recent advances in electrodes for noninvasive recording of electroencephalograms expand opportunities collecting such data for diagnosis of neurological disorders and brain–computer interfaces. Existing technologies, however, cannot be used effectively in continuous, uninterrupted modes for more than a few days due to irritation and irreversible degradation in the electrical and mechanical properties of the skin interface. Here we introduce a soft, foldable collection of electrodes in open, fractal mesh geometries that can mount directly and chronically on the complex surface topology of the auricle and the mastoid, to provide high-fidelity and long-term capture of electroencephalograms in ways that avoid any significant thermal, electrical, or mechanical loading of the skin. Experimental and computational studies establish the fundamental aspects of the bending and stretching mechanics that enable this type of intimate integration on the highly irregular and textured surfaces of the auricle. Cell level tests and thermal imaging studies establish the biocompatibility and wearability of such systems, with examples of high-quality measurements over periods of 2 wk with devices that remain mounted throughout daily activities including vigorous exercise, swimming, sleeping, and bathing. Demonstrations include a text speller with a steady-state visually evoked potential-based brain–computer interface and elicitation of an event-related potential (P300 wave).
834786
Evaluate the Feasibility of Using Frontal SSVEP to Implement an SSVEP-Based BCI in Young, Elderly and ALS Groups
This paper studies the amplitude-frequency characteristic of frontal steady-state visual evoked potential (SSVEP) and its feasibility as a control signal for brain computer interface (BCI). SSVEPs induced by different stimulation frequencies, from 13 ~ 31 Hz in 2 Hz steps, were measured in eight young subjects, eight elders and seven ALS patients. Each subject was requested to participate in a calibration study and an application study. The calibration study was designed to find the amplitude-frequency characteristics of SSVEPs recorded from Oz and Fpz positions, while the application study was designed to test the feasibility of using frontal SSVEP to control a two-command SSVEP-based BCI. The SSVEP amplitude was detected by an epoch-average process which enables artifact-contaminated epochs can be removed. The seven ALS patients were severely impaired, and four patients, who were incapable of completing our BCI task, were excluded from calculation of BCI performance. The averaged accuracies, command transfer intervals and information transfer rates in operating frontal SSVEP-based BCI were 96.1%, 3.43 s/command, and 14.42 bits/min in young subjects; 91.8%, 6.22 s/command, and 6.16 bits/min in elders; 81.2%, 12.14 s/command, and 1.51 bits/min in ALS patients, respectively. The frontal SSVEP could be an alternative choice to design SSVEP-based BCI.
741046
An Online Brain-Computer Interface Based on SSVEPs Measured From Non-Hair-Bearing Areas
Steady state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has gained a lot of attention due to its robustness and high information transfer rate (ITR). However, transitioning well-controlled laboratory-oriented BCI demonstrations to real-world applications poses severe challenges for this exciting field. For instance, conducting BCI experiments usually requires skilled technicians to abrade the area of skin underneath each electrode and apply an electrolytic gel or paste to acquire high-quality SSVEPs from hair-covered areas. Our previous proof-of-concept study has proposed an alternative approach that employed electroencephalographic signals collected from easily accessible non-hair-bearing areas including neck, behind the ears, and face to realize an SSVEP-based BCI. The study results showed that, with proper electrode placements and advanced signal-processing algorithms, the SSVEPs measured from non-hair-bearing areas in off-line SSVEP experiments could achieve comparable SNR to that obtained from the hair-bearing occipital areas. This study extended the previous work to systematically investigate the costs and benefits of non-hair SSVEPs. Furthermore, this study developed and evaluated an online BCI system based solely on non-hair EEG signals. A 12-target identification task was employed to quantitatively assess the performance of the online SSVEP-based BCI system. All subjects successfully completed the tasks using non-hair SSVEPs with 84.08 ± 15.60% averaged accuracy and 30.21 ± 10.61 bits/min averaged ITR. The empirical results of this study demonstrated the practicality of implementing an SSVEP-based BCI based on signals from non-hair-bearing areas, significantly improving the feasibility and practicality of real-world BCIs.
44718357
Visual stimuli for the P300 brain–computer interface: A comparison of white/gray and green/blue flicker matrices
OBJECTIVE
The white/gray flicker matrix has been used as a visual stimulus for the so-called P300 brain-computer interface (BCI), but the white/gray flash stimuli might induce discomfort. In this study, we investigated the effectiveness of green/blue flicker matrices as visual stimuli.


METHODS
Ten able-bodied, non-trained subjects performed Alphabet Spelling (Japanese Alphabet: Hiragana) using an 8 x 10 matrix with three types of intensification/rest flicker combinations (L, luminance; C, chromatic; LC, luminance and chromatic); both online and offline performances were evaluated.


RESULTS
The accuracy rate under the online LC condition was 80.6%. Offline analysis showed that the LC condition was associated with significantly higher accuracy than was the L or C condition (Tukey-Kramer, p < 0.05). No significant difference was observed between L and C conditions.


CONCLUSIONS
The LC condition, which used the green/blue flicker matrix was associated with better performances in the P300 BCI.


SIGNIFICANCE
The green/blue chromatic flicker matrix can be an efficient tool for practical BCI application.
358480
Effect of the Green/Blue Flicker Matrix for P300-Based Brain–Computer Interface: An EEG–fMRI Study
The visual P300-brain–computer interface, a popular system for EEG-based BCI, utilizes the P300 event-related potential to select an icon arranged in a flicker matrix. In the conventional P300-BCI speller paradigm, white/gray luminance intensification of each row/column in the matrix is used. In an earlier study, we applied green/blue luminance and chromatic change in the P300-BCI system and reported that this luminance and chromatic flicker matrix was associated with better performance and greater subject comfort compared with the conventional white/gray luminance flicker matrix. In this study, we used simultaneous EEG-functional magnetic resonance imaging (fMRI) recordings to identify brain areas that were more enhanced in the green/blue flicker matrix than in the white/gray flicker matrix, as these may highlight areas devoted to improved P300-BCI performance. The peak of the positive wave in the EEG data was detected under both conditions, and the peak amplitudes were larger at the parietal and occipital electrodes, particularly in the late components, under the green/blue condition than under the white/gray condition. fMRI data showed activation in the bilateral parietal and occipital cortices, and these areas, particularly those in the right hemisphere, were more activated under the green/blue condition than under the white/gray condition. The parietal and occipital regions more involved in the green/blue condition were part of the areas devoted to conventional P300s. These results suggest that the green/blue flicker matrix was useful for enhancing the so-called P300 responses.
2075131
Chromatic SSVEP BCI paradigm targeting the higher frequency EEG responses
A novel approach to steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) is presented in the paper. To minimize possible side-effects of the monochromatic light SSVEP-based BCI we propose to utilize chromatic green-blue flicker stimuli in higher, comparing to the traditionally used, frequencies. The developed safer SSVEP responses are processed an classified with features drawn from EEG power spectra. Results obtained from healthy users support the research hypothesis of the chromatic and higher frequency SSVEP. The feasibility of proposed method is evaluated in a comparison of monochromatic versus chromatic SSVEP responses. We also present preliminary results with empirical mode decomposition (EMD) adaptive filtering which resulted with improved classification accuracies.
35081789
Use of high-frequency visual stimuli above the critical flicker frequency in a SSVEP-based BMI
OBJECTIVE
This study presents a new steady-state visual evoked potential (SSVEP)-based brain-machine interface (BMI) using flickering visual stimuli at frequencies greater than the critical flicker frequency (CFF).


METHODS
We first asked participants to fixate on a green/blue flicker (30-70Hz), and SSVEP amplitude was evaluated. Participants were asked to indicate whether the stimulus was visibly flickering and to report their subjective level of discomfort. We then assessed visibly (41, 43, and 45Hz) vs. invisibly (61, 63, and 65Hz) flickering stimulus in an SSVEP-based BMI. Visual fatigue was assessed via the flicker test before and after operation of the BMI.


RESULTS
Higher frequency stimuli reduced participants' subjective discomfort. Participants successfully controlled the SSVEP-based BMI using both the visibly and invisibly flickering stimuli (93.1% and 88.0%, respectively); the flicker test revealed a decrease in CFF (i.e., visual fatigue) under the visible condition only (-5.7%, P<0.001).


CONCLUSIONS
The use of high-frequency visual stimuli above the CFF led to high classification accuracy and decreased visual fatigue in an SSVEP-based BMI.


SIGNIFICANCE
High-frequency flicker stimuli above the CFF were able to induce SSVEPs and may prove useful in the development of BMI-based assistive products.
20378872
A novel stimulation method for multi-class SSVEP-BCI using intermodulation frequencies.
OBJECTIVE
Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has been widely investigated because of its easy system configuration, high information transfer rate (ITR) and little user training. However, due to the limitations of brain responses and the refresh rate of a monitor, the available stimulation frequencies for practical BCI application are generally restricted.


APPROACH
This study introduced a novel stimulation method using intermodulation frequencies for SSVEP-BCIs that had targets flickering at the same frequency but with different additional modulation frequencies. The additional modulation frequencies were generated on the basis of choosing desired flickering frequencies. The conventional frame-based 'on/off' stimulation method was used to realize the desired flickering frequencies. All visual stimulation was present on a conventional LCD screen. A 9-target SSVEP-BCI based on intermodulation frequencies was implemented for performance evaluation. To optimize the stimulation design, three approaches (C: chromatic; L: luminance; CL: chromatic and luminance) were evaluated by online testing and offline analysis.


MAIN RESULTS
SSVEP-BCIs with different paradigms (C, L, and CL) enabled us not only to encode more targets, but also to reliably evoke intermodulation frequencies. The online accuracies for the three paradigms were 91.67% (C), 93.98% (L), and 96.41% (CL). The CL condition achieved the highest classification performance.


SIGNIFICANCE
These results demonstrated the efficacy of three approaches (C, L, and CL) for eliciting intermodulation frequencies for multi-class SSVEP-BCIs. The combination of chromatic and luminance characteristics of the visual stimuli is the most efficient way for the intermodulation frequency coding method.
11724926
Specialized Color Modules in Macaque Extrastriate Cortex
Imaging studies are consistent with the existence of brain regions specialized for color, but electrophysiological studies have produced conflicting results. Here we address the neural basis for color, using targeted single-unit recording in alert macaque monkeys, guided by functional magnetic resonance imaging (fMRI) of the same subjects. Distributed within posterior inferior temporal cortex, a large region encompassing V4, PITd, and posterior TEO that some have proposed functions as a single visual complex, we found color-biased fMRI hotspots that we call "globs," each several millimeters wide. Almost all cells located in globs showed strong luminance-invariant color tuning and some shape selectivity. Cells in different globs represented distinct visual field locations, consistent with the coarse retinotopy of this brain region. Cells in "interglob" regions were not color tuned, but were more strongly shape selective. Neither population was direction selective. These results suggest that color perception is mediated by specialized neurons that are clustered within the extrastriate brain.
25217839
Effects of task demands on the responses of color-selective neurons in the inferior temporal cortex
Categorization and fine discrimination are two different functions in visual perception, and we can switch between these two functions depending on the situation or task demands. To explore how visual cortical neurons behave in such situations, we recorded the activities of color-selective neurons in the inferior temporal (IT) cortex of two monkeys trained to perform a color categorization task, a color discrimination task and a simple fixation task. Many IT neurons changed their activity depending upon the task, although color selectivity was well conserved. A majority of neurons showed stronger responses during the categorization task. Moreover, for the population of IT neurons as a whole, signals contributing to performing the categorization task were enhanced. These results imply that judgment of color category by color-selective IT neurons is facilitated during the categorization task and suppressed during the discrimination task as a consequence of task-dependent modulation of their activities.
33359587
Contribution of striate inputs to the visuospatial functions of parieto-preoccipital cortex in monkeys
In Experiments 1 and 2, monkeys received 3-stage operations intended to serially disconnect parieto-preoccipital from striate cortex. At each stage (unilateral parieto-preoccipital removal, contralateral striate removal and posterior callosal transection) the monkeys were tested for retention of the landmark task, a visuospatial discrimination sensitive to the effects of bilateral parieto-preoccipital damage. To check the effectiveness of the disconnection, the monkeys were also tested after removal of the remaining parieto-preoccipital cortex. The results demonstrated that corticocortical inputs from striate cortex are crucial for the visuospatial functions of parieto-preoccipital cortex, just as they had been shown earlier to be crucial for the pattern discrimination functions of inferior temporal cortex. Relative to inferior temporal cortex, however, parieto-preoccipital cortex was found to be especially dependent on ipsilateral (as compared with contralateral) striate inputs. In Experiment 3, monkeys received bilateral lesions of either lateral on medial striate cortex and were tested on both a pattern discrimination task, to assess residual inferior temporal function, and the landmark task, to assess residual parieto-preoccipital function. The results indicated that the pattern discrimination functions of inferior temporal cortex are especially dependent on inputs from lateral striate cortex, whereas the visuospatial functions of parieto-preoccipital cortex are equally dependent on inputs from lateral and medial striate cortex. The relatively greater contribution to parieto-preoccipital than to inferior temporal cortex made by ipsilateral and medial striate inputs (representing contralateral and peripheral visual fields, respectively) can also be seen in the receptive field properties of parieto-preoccipital and inferior temporal neurons. The differences in the organization of striate inputs to these two cortical association areas presumably reflect differences in the processing required for spatial vs object vision.
793980
Separate visual pathways for perception and action
Accumulating neuropsychological, electrophysiological and behavioural evidence suggests that the neural substrates of visual perception may be quite distinct from those underlying the visual control of actions. In other words, the set of object descriptions that permit identification and recognition may be computed independently of the set of descriptions that allow an observer to shape the hand appropriately to pick up an object. We propose that the ventral stream of projections from the striate cortex to the inferotemporal cortex plays the major role in the perceptual identification of objects, while the dorsal stream projecting from the striate cortex to the posterior parietal region mediates the required sensorimotor transformations for visually guided actions directed at such objects.
16201720
Is colour modulation an independent factor in human visual photosensitivity?
Considering that the role of colour in photosensitive epilepsy (PSE) remains unclear, we designed a study to determine the potential of different colours, colour combinations and white light to trigger photoparoxysmal responses (PPRs) under stringent controlled conditions. After assessing their photosensitivity to stroboscopic white light and black and white patterns, we studied 43 consecutive PSE patients (mean age 19 years, 34 women), using a specially designed colour stimulator. Stimuli included: pulse trains between 10 and 30 Hz of white light and of all primary colours, and also isoluminant alternating time-sequences of colours. Illuminance was kept constant at 100 lux. A progressive stepwise increase of the modulation-depth (MD) of the stimuli was used to determine PPRs threshold. Whereas all the 43 patients were found to be sensitive during the stroboscopic and pattern protocol, only 25 showed PPRs (Waltz's score >2) at least in one session when studied with the colour stimulator. Coloured stimuli elicited PPRs in all these patients, whereas white light did so only in 17 patients. Of the primary colours, red elicited more PPRs (54 in 22 patients) and at a lower MD (max Z-score 0.93 at 10 Hz). Of the alternating sequences, the red-blue was the most provocative stimulus, especially below 30 Hz (100% of patients, max Z-score: 1.65 at 15 Hz). Blue-green was the least provocative stimulus, since it elicited only seven PPRs in seven (28%) patients (max Z-score 0.44 at 10 Hz). Sensitivity to alternating colours was not correlated to sensitivity to individual colours. We conclude that colour sensitivity follows two different mechanisms: one, dependent on colour modulation, plays a role at lower frequencies (<30 Hz). Another, dependent on single-colour light intensity modulation correlates to white light sensitivity and is activated at higher frequencies. Our results suggest that the prescription of spectacles with coloured lenses, tailored to the patient, can be an effective preventative measure against visually induced seizures.
56932816
A practical VEP-based brain-computer interface.
This paper introduces the development of a practical brain-computer interface at Tsinghua University. The system uses frequency-coded steady-state visual evoked potentials to determine the gaze direction of the user. To ensure more universal applicability of the system, approaches for reducing user variation on system performance have been proposed. The information transfer rate (ITR) has been evaluated both in the laboratory and at the Rehabilitation Center of China, respectively. The system has been proved to be applicable to > 90% of people with a high ITR in living environments.
61120811
A high-ITR SSVEP-based BCI speller
Spelling is an important application of brain-computer interfaces (BCIs). Previous BCI spellers were not suited for widespread use due to their low information transfer rate (ITR). In this study, we constructed a high-ITR BCI speller based on the steady-state visual evoked potential (SSVEP). A 45-target BCI speller was implemented with a frequency resolution of 0.2 Hz. A sampled sinusoidal stimulation method was used to present visual stimuli on a conventional LCD screen. The online results revealed that the proposed BCI speller had a good performance, reaching a high average accuracy (84.1% for 2 s stimulation time; 90.2% for 3 s stimulation time) and the corresponding high ITR (105 bits/min for 2 s stimulation time, 82 bits/min for 3 s stimulation time) during the low-frequency stimuli, while 88.7% and 61 bits/min were achieved for a 4 s time window during the high-frequency stimuli.
10645269
Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs
Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. Among the existing solutions the systems relying on electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. However, the process of translating EEG signals into computer commands is far from trivial, since it requires the optimization of many different parameters that need to be tuned jointly. In this report, we focus on the category of EEG-based BCIs that rely on Steady-State-Visual-Evoked Potentials (SSVEPs) and perform a comparative evaluation of the most promising algorithms existing in the literature. More specifically, we define a set of algorithms for each of the various different parameters composing a BCI system (i.e. filtering, artifact removal, feature extraction, feature selection and classification) and study each parameter independently by keeping all other parameters fixed. The results obtained from this evaluation process are provided together with a dataset consisting of the 256-channel, EEG signals of 11 subjects, as well as a processing toolbox for reproducing the results and supporting further experimentation. In this way, we manage to make available for the community a state-of-the-art baseline for SSVEP-based BCIs that can be used as a basis for introducing novel methods and approaches.
22824553
Polychromatic SSVEP stimuli with subtle flickering adapted to brain-display interactions.
OBJECTIVE
Interactive displays armed with natural user interfaces (NUIs) will likely lead the next breakthrough in consumer electronics, and brain-computer interfaces (BCIs) are often regarded as the ultimate NUI-enabling machines to respond to human emotions and mental states. Steady-state visual evoked potentials (SSVEPs) are a commonly used BCI modality due to the ease of detection and high information transfer rates. However, the presence of flickering stimuli may cause user discomfort and can even induce migraines and seizures. With the aim of designing visual stimuli that can be embedded into video images, this study developed a novel approach to induce detectable SSVEPs using a composition of red/green/blue flickering lights.


APPROACH
Based on the opponent theory of colour vision, this study used 32 Hz/40 Hz rectangular red-green or red-blue LED light pulses with a 50% duty cycle, balanced/equal luminance and 0°/180° phase shifts as the stimulating light sources and tested their efficacy in producing SSVEP responses with high signal-to-noise ratios (SNRs) while reducing the perceived flickering sensation.


MAIN RESULTS
The empirical results from ten healthy subjects showed that dual-colour lights flickering at 32 Hz/40 Hz with a 50% duty cycle and 180° phase shift achieved a greater than 90% detection accuracy with little or no flickering sensation.


SIGNIFICANCE
As a first step in developing an embedded SSVEP stimulus in commercial displays, this study provides a foundation for developing a combination of three primary colour flickering backlights with adjustable luminance proportions to create a subtle flickering polychromatic light that can elicit SSVEPs at the basic flickering frequency.
18596727
Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs
Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used FFT (fast Fourier transform)-based spectrum estimation method
19457787
Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index
Multivariate synchronization index (MSI) has been proved to be an efficient method for frequency recognition in SSVEP-BCI systems. It measures the correlation according to the entropy of the normalized eigenvalues of the covariance matrix of multichannel signals. In the MSI method, the estimation of covariance matrix omits the temporally local structure of samples. In this study, a new spatio-temporal method, termed temporally local MSI (TMSI), was presented. This new method explicitly exploits temporally local information in modelling the covariance matrix. In order to evaluate the performance of the TMSI, we performs a comparison between the two methods on the real SSVEP datasets from eleven subjects. The results show that the TMSI outperforms the standard MSI. TMSI benefits from exploiting the temporally local structure of EEG signals, and could be a potential method for robust performance of SSVEP-based BCI.
46312670
Photic‐ and Pattern‐induced Seizures: A Review for the Epilepsy Foundation of America Working Group
Summary:  Purpose: This report summarizes background material presented to a consensus conference on visually provoked seizures, convened by the Epilepsy Foundation of America.
9977720
BCI Demographics II: How Many (and What Kinds of) People Can Use a High-Frequency SSVEP BCI?
Brain-computer interface (BCI) systems use brain activity as an input signal and enable communication without movement. This study is a successor of our previous study (BCI demographics I) and examines correlations among BCI performance, personal preferences, and different subject factors such as age or gender for two sets of steady-state visual evoked potential (SSVEP) stimuli: one in the medium frequency range (13, 14, 15 and 16 Hz) and another in the high-frequency range (34, 36,38, 40 Hz). High-frequency SSVEPs (above 30 Hz) diminish user fatigue and risk of photosensitive epileptic seizures. Results showed that most people, despite having no prior BCI experience, could use the SSVEP-based Bremen-BCI system in a very noisy field setting at a fair. Results showed that demographic parameters as well as handedness, tiredness, alcohol and caffeine consumption, etc., have no significant effect on the performance of SSVEP-based BCI. Most subjects did not consider the flickering stimuli annoying, only five out of total 86 participants indicated change in fatigue during the experiment. 84 subjects performed with a mean information transfer rate of 17.24 ± 6.99 bit/min and an accuracy of 92.26 ± 7.82% with the medium frequency set, whereas only 56 subjects performed with a mean information transfer rate of 12.10 ± 7.31 bit/min and accuracy of 89.16 ± 9.29% with the high-frequency set. These and other demographic analyses may help identify the best BCI for each user.
33213587
Brain-computer interface based on the high-frequency steady-state visual evoked potential
Low-frequency steady-state visual evoked potentials (SSVEPs) are used as the input signal in the present SSVEP-based brain-computer interface (BCI). This prototype system has a high information transfer rate. On the other hand, it has some limitations including visual fatigue, false positive, and some possibility of causing a seizure. These drawbacks can be largely eliminated when using high-frequency stimulations. In this paper, we study the amplitude versus stimulation frequency response of SSVEPs. The signal-to-noise ratio versus frequency curve suggests that the high-frequency SSVEP (>20Hz) could help to construct a practical BCI system.
124076840
High-frequency polychromatic visual stimuli for new interactive display systems
Brain–computer interfaces (BCIs) are intuitive operation modes that use electrical brain activity to communicate with external electronic devices. Over the past decade, BCI systems have been used for assistive living applications.1 In addition, 3D technologies are now widely available and are frequently used for virtual reality and augmented reality applications. As vision is the most dominant sense for humans, it is thought that BCI-enabled interactive displays (especially 3D displays) will also have a broad range of applications in gaming and e-learning. The steady state visual evoked potential (SSVEP) is an example of a BCI modality that can be induced by visual stimuli. The SSVEP is the natural response of the brain to repetitive stimuli that are modulated at a constant frequency. It is thought that the SSVEP may be the most suitable modality in brain–display interactions (BDIs) because of its non-intrusive, easy detection, and high information transfer rates.2 The functional architecture of BDI systems is illustrated in Figure 1. SSVEP-based BCI systems have been developed in recent years because of their attractive features. To induce strong SSVEP responses, however, most of these systems use visual stimuli in a low-frequency band (less than 20Hz).3 Unfortunately, bright lights that flicker in this frequency range can be distracting to viewers, and they can cause visual fatigue, migraine headaches, and even photosensitive epilepsy attacks. Our group is making the first steps in the development of a display-embedded SSVEP stimulus. As part of this work, we have proposed using a combination of flickering red-green lights to create an imperceptible flickering visual stimulus that can elicit an SSVEP at a basic flickering frequency.4 We have thus conducted a series of experiments to investigate whether the Figure 1. Architecture of a typical brain–display interactive system. SSVEP: Steady-state visual evoked potential.