Probabilistic Modeling of Sensorimotor mu-Rhythms for Classification of Imaginary Hand Movements
Brain computer interfaces (BCI) require effective on-line processing of EEG measurements, e.g., as a part of feedback systems. Here we present an algorithm for single trial on-line classification of imaginary left and right hand movements, based on time-frequency information derived from filtering EEG wideband raw data with causal Morlet wavelets which are adapted to individual EEG spectra. Since imaginary hand movements lead to perturbations of the ongoing pericentral mu-rhythm, we estimate probabilistic models for amplitude modulation in lower (10 Hz) and upper (20 Hz) frequency bands over the sensorimotor hand cortices both contra- and ipsilaterally to the imagined movements (i.e., at EEG channels C3 and C4). We use an integrative approach to accumulate over time evidence for the subject's unknown motor intention. Disclosure of test data labels after the competition showed this approach to succeed with an error rate as low as 10.7%.