An analytic spatial filter and a hidden Markov model for enhanced information transfer rate in EEG-based brain computer interfaces

• Published in: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing pp. 602 - 605
• Main Authors: McCormick, M, Rui Ma, Coleman, T P
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2010
• Subjects: Belief Propagation; Blind source separation; Brain computer interfaces; Brain modeling; Common Spatial Pattern; Electroencephalography; Hidden Markov Model; Hidden Markov models; Humans; Information analysis; Pattern analysis; Signal analysis; Spatial filters
• ISBN: 9781424442959; 1424442958
• ISSN: 1520-6149
• DOI: 10.1109/ICASSP.2010.5495544

We propose a new classification method, termed the Common Spatial Analytic Pattern, for brain-computer interfaces based on a simple EEG signal source and channel model. This blind source separation procedure recovers underlying source signals near the motor cortex which are indicative of motor imagery. A hidden Markov source model is applied to the evolution of the source signals and is used to estimate the type (left or right) of motor imagery performed by a subject. As a whole, the resulting asynchronous classifier offers significant improvement upon the current prevailing techniques in classification. Experiments show information transfer rates between subject and computer as high as 60.9 bits/minute.