Asynchronous brain computer interface using hidden semi-Markov models.
Ideal Brain Computer Interfaces need to perform asynchronously and at real time. We propose Hidden Semi-Markov Models (HSMM) to better segment and classify EEG data. The proposed HSMM method was tested against a simple windowed method on standard datasets. We found that our HSMM outperformed the simple windowed method. Furthermore, due to the computational demands of the algorithm, we adapted the HSMM algorithm to an online setting and demonstrate that this faster version of the algorithm can run in real time.
Research School of Computer Science at the Australian National University. gareth.oliver at anu.edu.au
Gareth
G
Oliver
Peter
P
Sunehag
Tom
T
Gedeon
D000465:Algorithms; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008390:Markov Chains