Review of Machine Learning Techniques for EEG Based Brain Computer Interface

A brain computer interface (BCI) framework uses computer algorithms to detect mental activity patterns and manipulate external devices. Because of its simplicity and non-invasiveness, one of the most commonly used imaging technologies is electroencephalography (EEG). The evaluative method used in assessing the output of an EEG-based BCI system is classifying EEG signals for particular applications. The growth of artificial intelligence technology inspired researchers to use machine learning (ML) techniques and deep learning (DL) approaches to classify EEG-based BCI. Machine learning techniques enable the brain computer interface to learn from the subject's brain with each new session, adapting the generated rules for classifying thoughts and thus improving the system's efficiency. The authors present a concentrated survey on the use of various ML/DL techniques in EEG-based BCI. Three EEG paradigms for classification are used: motor imagery, p300, and steady state evoked potential. In addition, the challenges that recent EEG-based BCI systems face are addressed based on ideal signal processing methods, BCI functioning, performance assessment and commercialization. The authors hope that the information gathered would aid in application of suitable machine learning techniques, as well as provide a foundation for BCI researchers to enhance future BCI system.