Comparative Study of Data Classification Methods Between EEG and ECoG Used to BCI

Abstract: Effective decoding of the source signal is a key to improve Brain-computer interfaces (BCI) performances. Two groups of motor imagery (MI) data based on electroencephalograms (EEG) and electrocorticograms (ECoG) which provided by International Brain-Computer Interface Competition organization are analyzed, and concluded that ECoG signals processing is more suitable for model-driven approaches. Temporal-frequency features were extracted by model-driven method instead of data-driven method and compared, and classified by support vector machine (SVM). The results show 6 % improvement of motor imagery experiment classification accuracy on ECoG data, compared with of data-driven method