Linear Classifier Approach to Detect Alpha Parietal Modulation for Brain Computer Interface

Brain computer interaction (BCI) based on electroencephalographic (EEG) signal helps people who suffering from disability to carry out their daily life. However, the numerus number of researches have done in this field, there are problems of high variance in accuracy and in efficiency among individuals. This paper presents a recognition method for eyes open (EO) and eyes closed(EC) of EEG signal using one channel P5. The model has tested to control a servo motor. A two types of feature are investigated; Energy of alpha power spectrum (EPSD) and relative alpha power (RAP). Further a linear discriminate analysis (LDA) and a nonlinear support vector machine (SVM) classifier are used. The used data are offline signals of 10 children age (4-5) years old. Results indicate the efficiency of EPSD hence the accuracy reaches to 95% for 2 sec time interval and 93.4% for 1 sec time interval. The RAP feature accuracy is 78.7%. The LDA has a significant performance compare to SVM. Both classifiers show high performance to detect EO event better than EC event. This study shows the ability of build EEG-BCI using one channel and with less computation process which can be affordable to most people with disability.