Motor Imaginary Task Classification using Statistically Significant Time Domain and Frequency Domain EEG features

Motor Imaginary (MI) electroencephalography (EEG) signals are obtained when a subject imagines a task without essentially applying it. The accurate decoding of MI EEG signals plays an important role in the design of brain-computer interface (BCI) systems due to the use of these signals in the rehabilitation process of paralyzed patients in recent studies. In this study, two different MI tasks were tried to be differentiated by extracting time-domain and frequency-domain features from 22 channel EEG signals and determining best combination of important and distinctive features based on statistical significance. MI EEG signals were supplied from BCI Competition IV Dataset-IIa. These features were differentiated using 25 different classification algorithms and 5-fold cross-validation method. The repeatability of the results was examined testing each algorithm 10 times. As a result, the highest average accuracy rate of 60.69% was calculated in the Quadratic Support Vector Machine (SVM) using all features and 62.52% in the Ensemble Subspace Discriminant (ESD) algorithm using only the selected features by the independent t-test. The results showed that the independent t-test based feature selection increased the performance in 20 classifiers, and decreased the performance in 5 classifiers. Also, the effectiveness of the feature selection method examined using the paired-sample t-test which is known as repeated measures t-test. The significance value, p-value was found as 0.04. Therefore, the independent t-test based feature selection method is an effective feature selection method and is providing the significant improvement in classifier performance.