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Brain-computer interface (BCI) is a hardware and software communication system that enables humans to interact with their surrounding without the involvement of peripheral nerves and muscles by using control signals generated from electroencephalographic activity. In this paper, we report on results of developing motor imagery feature extraction method for BCI. The wavelet coefficients were used to extract the features from the motor imagery EEG and the Bayes Net, SVM and RBFN were utilized to classify the pattern of left, right hand movement and forward imagery. The performance was tested using dataset from BCI competition III and satisfactory results are obtained with accuracy rate as high as 99.0674%.

A brain–computer interface (BCI), as a solution to disabled people’s concerns, has drawn attention in biomedical engineering over the last decade. However, the most existing brain–computer interface systems are based on the time or frequency domain of feature extraction, and it is associated with inaccurate detection of event-related desynchronization (ERD). In this study, a new algorithm relating to subject-specific regions of interest (ROIs) with intrinsic time-scale decomposition (ITD) was investigated to achieve satisfactory classification accuracy. ROI-based discrete wavelet transform (DWT) combined with an artificial neural network was used to validate the ROI-based ITD method. Experimentally recorded data of motor imagery movement tasks (right hand, left hand, both hands and both feet) were collected from 15 subjects. The parameters of the subject-specific regions of interest were investigated and optimized. An optimal condition was observed at a specific region of interest and the accuracy increased by 12.76 to 15.17% compared to that without ROI estimation. ITD showed higher classification accuracy, sensitivity, specificity and Kappa coefficient of 9.47%, 8.99%, 9.79% and 12.09%, respectively, for the four classes of motor imagery movements compared to DWT. The developed ITD model was validated using the dataset from BCI Competition IV. On average, ITD with ROIs showed 8.56% and 7.32% higher classification accuracy compared to common spatial patents (CSP) and DWT with ROIs.

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