TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Bayes quadratic <s> The feasibility of establishing an alternative mode of communication between man and his surroundings was studied. The form of communication proposed uses only the subject's brain waves, with no overt physical action required. The subject's electroencephalograms (EEG) were recorded while various mental tasks designed to elicit hemispheric responses were performed. Features formed from the EEG recording were then used as inputs into a Bayes quadratic classifier to test classification accuracy between the various tasks. The results obtained indicate that it is possible to accurately distinguish between any two of the five tasks investigated. A comparison between three different methods for creating the feature sets is also presented. > <s> BIB001 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Bayes quadratic <s> 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 /spl 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%. <s> BIB002
Bayesian classification aims at assigning to a feature vector the class it belongs to with the highest probability   . The Bayes rule is used to compute the so-called a posteriori probability that a feature vector has of belonging to a given class  . Using the MAP (Maximum A Posteriori) rule and these probabilities, the class of this feature vector can be estimated. Bayes quadratic consists in assuming a different normal distribution of data. This leads to quadratic decision boundaries, which explains the name of the classifier. Even though this classifier is not widely used for BCI, it has been applied with success to motor imagery BIB002 [51] and mental task classification BIB001 [64].