Multiclass Classification of EEG Signal Using a Probabilistic Approach. (2016)

• Record Type: Journal Article
• Authors: Venate, Salini; Sunny, T.D.
• Is Part Of: Procedia technology. Volume 24(2016)
• Page Start: 1002
• Page End: 1007
• Publication Date: 2016
• Subjects: Brain Computer Interfacing -- Common satial pattern -- Multi-class; Technology -- Congresses; Technology -- Periodicals; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Technology; Conference proceedings; Periodicals; 605
• DOI: 10.1016/j.protcy.2016.05.219
• Languages: English
• ISSNs: 2212-0173

Abstract

Abstract: Brain Computer Interfacing (BCI) also called Brain Machine Interfacing (BMI)) is a challenging problem that forms part of a larger research area, called the Human Computer Interfacing (HCI), which interlinks thoughts to action. In BCI systems, the user messages or commands do not depend on the normal output channels of the brain. Therefore the main objective of BCI is to process the electrical signals generated by the neurons in the brain and generate the necessary signals to control some external systems. This paper investigates the feasibility of using Bayesian Spatio Spectral Filter Optimization algorithm for motor imagery classification in a multiclass scenario.