Machine learning for biomedical-focus on brain signals

There is a growing interest in machine learning (ML) in this decade. This growing interest is accelerated by cheaper computing power and low-cost memory. Thus, large amount of data can be stored, processed and analyzed efficiently. Machine learning has used in brain machine systems (BMS) system that converts brain impulses into messages or commands. In this paper we propose an EEG-based BMS with the focus on evoked potential. An average classification accuracy of 95% was attained among nine participants. With a rate of 4 flashes per second implemented, selecting one of four possibilities takes 5 s, resulting in an information transfer rate of 24 bits/min. Also, brain computer interfacing using oscillatory activity was measured. The results show that after around 5 h of co-adaptive training over many days, the average 3-class accuracy of the Linear Discriminant Analysis committee classifier reached about 80%, with a false positive rate for motor imagery recognition of around 17%.