A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI

Accurate binary classification of electroencephalography (EEG) signals is a challenging task for the development of motor imagery (MI) brain computer interface (BCI) systems. In this study two sliding window techniques are proposed to enhance binary classification of motor imagery (MI). The first one calculates the longest consecutive repetition (LCR) of the sequence of prediction of all the sliding windows which is named as SW-LCR. The second calculates the mode of the sequence of prediction of all the sliding windows and is named SW-Mode. Common spatial pattern (CSP) is used for extracting features with linear discriminant analysis (LDA) used for classification of each time window. Both the SW-LCR and SW-Mode are applied on publicly available BCI Competition IV-2a dataset of healthy individuals and on a stroke patients dataset. As compared to the existing state-of-the-art the SW-LCR performed better in the case of healthy individuals and SW-Mode performed better on stroke patients dataset for left vs. right hand MI with lower standard deviation. For both the datasets the classification accuracy (CA) was approximately 80% and kappa (κ) was 0.6. The results show that the sliding window based prediction of MI using SW-LCR and SW-Mode is robust against inter-trial and inter-session inconsistencies in the time of activation within a trial and thus can lead to reliable performance in a neurorehabilitative BCI setting.