EEG signal classification for real-time brain-computer interface applications: A review
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Date
2011-05-17
Journal Title
Journal ISSN
Volume Title
Publisher
2011/5/17
Abstract
Brain-computer interface (BCI) is linking the brain activity to computer, which allows a
person to control devices directly with his brain waves and without any use of his muscles.
Recent advances in real-time signal processing have made BCI a feasible alternative for
controlling robot and for communication as well. Controlling devices using BCI is a crucial
aid for people suffering from severe disabilities and more than that, BCIs can replace
human to control robots working in dangerous or uncongenial situations. Effective BCIs
demand for accurate and real-time EEG signals processing. This paper is to review the
current state of research and to compare the performance of different algorithms for realtime classification of BCI-based electroencephalogram signals.
Description
Keywords
Electroencephalography, Classification algorithms, Feature extraction, Brain computer interfaces, Hidden Markov models, Real time systems, Support vector machines
Citation
Khorshidtalab, A., & Salami, M. J. E. (2011, May). EEG signal classification for real-time brain-computer interface applications: A review. In 2011 4th International Conference on Mechatronics (ICOM) (pp. 1-7). IEEE.