ICA-based classification scheme for EEG-based brain-computer interface: the role of mental practice and concentration skills

This article explores the use of independent component analysis (ICA) approach to design a new EEG-based brain-computer interface (BCI) for natural control of prosthetic hand grasp. ICA is a useful technique that allows blind separation of sources, linearly mixed, assuming only the statistical independence of these sources. This suggests the possibility of using ICA to separate different independent brain activities during motor imagery into separate components. This work provides a natural basis for developing an efficient BCI based on single-source data obtained by independent component analysis of multi-channel EEG. The tasks to be discriminated are the imagination of hand grasping and opening and the resting state. The results indicate that the proposed scheme can improve the classification accuracy of the EEG patterns. Imagery is the essential part of the most EEG-based communication systems. Thus, the quality of mental rehearsal, the degree of imagined effort, and mind controllability should have a major effect on the performance of EEG-based BCI. We are going to examine the role of mental practice and concentration skills on the performance of BCI. The surprising results indicate that mental training has a significant effect on the performance of BCI over the primary motor cortex, temporal, and frontal areas. This supports the hypothesis that mental practice is an effective method for performance enhancement and motor learning skill.