BCI competition 2003-data set III: probabilistic modeling of sensorimotor /spl mu/ rhythms for classification of imaginary hand movements

Abstract

Brain-computer interfaces require effective online processing of electroencephalogram (EEG) measurements, e.g., as a part of feedback systems. We present an algorithm for single-trial online 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%.

Non-negative matrix factorization with α-divergence

“…• Graz dataset: Dataset III in BCI competition II, which was provided by the Laboratory of Brain-Computer Interfaces (BCI-Lab), Graz University of Technology (Blankertz et al, 2004;Lemm et al, 2004); • IDIAP dataset: Dataset V in BCI competition III, which was provided by the IDIAP Research Institute (J. del R. Millán, 2004).…”

“…Feature vectors correspond to the column vectors of the encoding variable matrix S. We use the same probabilistic model-based classifier as used in (Lemm et al, 2004;Lee et al, 2006). The best performance in this experiment was achieved when α = 0.5or1 and n = 5, as summarized in Table 1.…”

Comparative study of band-power extraction techniques for Motor Imagery classification

“…The C3, C4 and Cz electrodes were sampled at 128 Hz. Following [17] we used only the C3 and C4 electrodes, during the trial period 3s to 9s.…”

Signal Processing and Classification Approaches for Brain-Computer Interface

“…EEG) should be taken into account during feature extraction (Wolpaw et al, 2002). To use this temporal information, three main approaches have been proposed (Lotte et al, 2007): • concatenation of features from different time segments: extracting features from several time segments and concatenating them into a single feature vector Haselsteiner & Pfurtscheller, 2000); • combination of classifications at different time segments: it consists in performing the feature extraction and classification steps on several time segments and then combining the results of the different classifiers (Penny & Roberts, 1999;Lemm et al, 2004); • dynamic classification: it consists in extracting features from several time segments to build a temporal sequence of feature vectors. This sequence can be classified using a dynamic classifier (Haselsteiner & Pfurtscheller, 2000;Obermeier et al, 2001).…”