Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The self-paced Graz brain-computer interface: methods and applications
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Computational Intelligence and Neuroscience - Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications
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Mental task onset detection from the continuous electroencephalogram (EEG) in real time is a critical issue in self-paced brain computer interface (BCI) design. The paper shows that self-paced BCI performance can be significantly improved by combining a range of simple techniques including (1) constant-Q filters with varying bandwidth size depending on the center frequency, instead of constant bandwidth filters for frequency decomposition of the EEG signal in the 6 to 36 Hz band; (2) subjectspecific postprocessing parameter optimization consisting of dwell time and threshold, and (3) debiasing before postprocessing by readjusting the classification output based on the current and previous brain states, to reduce the number of false detections. This debiasing block is shown to be optimal when activated only in special cases which are predetermined during the training phase. Analysis of the data recorded from seven subjects executing foot movement shows a statistically significant 10% (P