Self-regulating interval type-2 neuro-fuzzy inference system for non-stationary EEG signal processing - Ankit Kumar Das (2017)
Motor-imagery based Brain Computer Interface (BCI) provides a direct communication pathway between the brain and a computer based on the neural activities generated by the brain. Such a technology enables people with physical disabilities to communicate with the external world without using their peripheral nerves and muscles. Moreover, BCI systems can also be used in field of gaming, robotics and human-computer interaction, in general. A typical motor-imagery based BCI system consists of a brain activity acquisition phase to obtain ElectroEncephaloGram (EEG) data using multiple channels, a brain signal processing phase to decode the user intentions and a translation phase for transforming the decoded information to control external devices. However, the usage of multiple channels may lead to several issues: a) Longer preparation time; b) Redundant channels with noisy information. In addition, the thoughts of users vary, which may change the data distribution over time (leads to non-stationary nature of EEG). Some of the trials are also affected by the presence of artifacts in EEG data. Further, the frequency response for a motor-imagery task varies for different individuals. These issues can render the BCI system inaccurate with deteriorated performance. The main goal of this thesis is to develop a unified BCI system that address the above mentioned issues.