Classification of Multi-Class Motor Imaginary Tasks using Poincare Measurements Extracted from EEG Signals

Motor Imaginary (MI) electroencephalography (EEG) signals are generated with the recording of brain activities when a participant imagines a movement without physically performing it. The correct decoding of MI signals have been became an important task due to the application of these signals in the rehabilitation process of paralyzed patients in recent studies. However, the decoding of the these signals is still an evolving challenge in the design of a brain-computer interface (BCI) system. In this study, a machine learning based approach using Poincare measurements from non-linear measurements of MI EEG signals is proposed for classification of four-class MI tasks. The m-lagged Poincare plots were used to extract non-linear features and m is set to be values from 1 to 10. The performances of feature vectors which are extracted from 10 lag values and feature vector which is the combinations of these vectors were investigated separately in experimental evaluation section. The 24 different typical classification algorithms were tested in differentiating MI tasks using 5-fold cross-validation. Each of the these algorithms tested 10 times to analyzed the repeatability of the experimental results. The highest classifier performance of 47.08% among these 11 feature vectors was achieved over the combination feature vector that includes all lag values features using Quadratic Support Vector Machine (SVM). According to average accuracy value of 24 classifiers in 11 feature vector, the most discriminative feature set is 9th vector that consists of features extracted when lag value defined as 9. As a result, the innovative aspect of this study is the application of Poincare plots, one of the nonlinear feature extraction methods, in motor imaginary task classification.