Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Learning the deep structures and unknown cor- relations is important for the detection of motor imagery of EEG signals (MI-EEG). This study investigates the use of convolutional neural networks (CNNs) for the classification of multi-class MI-EEG signals. Augmented common spatial pattern (ACSP) features are generated based on pair-wise projection matrices, which covers various frequency ranges.  propose a frequency complementary feature map selection (FCMS) scheme by constraining the dependency among fre- quency bands. Experiments are conducted on BCI competition IV dataset IIa with 9 subjects. Averaged cross-validation ac- curacy of 68.45% and 69.27% is achieved for FCMS and all feature maps, respectively, which is significantly higher (4.53% and 5.34%) than random map selection and higher (1.44% and 2.26%) than filter-bank CSP (FBCSP). The results demonstrate that the CNNs are capable of learning discriminant, deep structure features for EEG classification without relying on the handcrafted features. Index Terms— multi-class motor imagery of EEG, deep learning, convolutional neural network, augmented CSP.
['Huijuan Yang', 'Siavash Sakhavi', 'Kai Keng Ang', 'Cuntai Guan']
I am writing a research paper on a idea On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification.
[{"from": "human", "value": "I am writing a research paper on a idea On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Learning the deep structures and unknown cor- relations is important for the detection of motor imagery of EEG signals (MI-EEG). This study investigates the use of convolutional neural networks (CNNs) for the classification of multi-class MI-EEG signals. Augmented common spatial pattern (ACSP) features are generated based on pair-wise projection matrices, which covers various frequency ranges.  propose a frequency complementary feature map selection (FCMS) scheme by constraining the dependency among fre- quency bands. Experiments are conducted on BCI competition IV dataset IIa with 9 subjects. Averaged cross-validation ac- curacy of 68.45% and 69.27% is achieved for FCMS and all feature maps, respectively, which is significantly higher (4.53% and 5.34%) than random map selection and higher (1.44% and 2.26%) than filter-bank CSP (FBCSP). The results demonstrate that the CNNs are capable of learning discriminant, deep structure features for EEG classification without relying on the handcrafted features. Index Terms\u2014 multi-class motor imagery of EEG, deep learning, convolutional neural network, augmented CSP."}]