TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Which classifier goes with which kind of features ? <s> The effects of sample size on feature selection and error estimation for several types of classifiers are discussed. The focus is on the two-class problem. Classifier design in the context of small design sample size is explored. The estimation of error rates under small test sample size is given. Sample size effects in feature selection are discussed. Recommendations for the choice of learning and test sample sizes are given. In addition to surveying prior work in this area, an emphasis is placed on giving practical advice to designers and users of statistical pattern recognition systems. > <s> BIB001 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Which classifier goes with which kind of features ? <s> The existence of characteristic changes in the EEG of a subject preparing for the execution of a voluntary movement is reviewed. Some of those changes, i.e., the "readiness potentials" (RPs), are suggested as a possible link between the internal processes associated with different forms of motion preparation and a digital signal processing system designed to classify the different intended movements. Such a system can potentially be used to provide input to a specialized graphic user interface, constituting a brain-computer interface (BCI). Our current work on neural-network classification of RPs is summarized and the results are presented. <s> BIB002 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Which classifier goes with which kind of features ? <s> We compare the use of two Markovian models, HMMs and IOHMMs, to discriminate between three mental tasks for brain computer interface systems using an asynchronous protocol. We show that IOHMMs outperform HMMs but that, probably due to the lack of any prior information on the state dynamics, no practical advantage in the use of these models over their static counterparts is obtained. <s> BIB003 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Which classifier goes with which kind of features ? <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> BIB004 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Which classifier goes with which kind of features ? <s> This paper addresses the problem of signal responses variability within a single subject in P300 speller Brain-Computer Interfaces. We propose here a method to cope with these variabilities by considering a single learner for each acquisition session. Each learner consists of a channel selection procedure and a classifier. Our algorithm has been benchmarked with the data and the results of the BCI 2003 competition dataset and we clearly show that our approach yields to state-of-the art results. <s> BIB005 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Which classifier goes with which kind of features ? <s> Mental task classification using brain signals, mostly electroencephalogram (EEG), is an approach to understand human brain functions. As EEG seems to be chaotic, it is important to verify the capability of probabilistic and statistical processing tools (such as HMM-based classifiers) in working with chaotic signals. At first, we study the performance of HMM's in classification of different classes of synthetically generated chaotic signals. Then performance of such classifiers in EEG-based mental task classification is studied. Results show good performance in both cases. <s> BIB006
To propose another point of view, this section compares classifiers considering their ability to cope with specific problems of BCI features (see Section 2.1.1): • noise and outliers: regularized classifiers, such as SVM, seem appropriate to deal with outliers. Muller et al even recommanded to systematically regularize the inria-00134950, version 1 -6 Mar 2007 classifiers used in BCI systems in order to cope with outliers  . It is also argued that discriminative classifiers perform better than generative ones in presence of noise or outliers BIB003 ; • high dimensionality: SVM probably are the most appropriate classifier to deal with feature vectors of high dimensionality. If the high dimensionality is due to the use of a large number of time segments, dynamic classifiers can also solve the problem by considering sequence of feature vectors instead of a single vector of very high dimensionality. For instance, SVM [10] BIB005 and dynamic classifiers such as HMM BIB006 or TDNN BIB002 are perfectly able to classify raw EEG. The kNN should not be used in such a case as they are very sensitive to the curseof-dimensionality. Nevertheless, it is always preferable to have a small number of features  . Therefore, it is highly recommended to use dimensionality reduction techniques and/or features selection  ; • time information: For synchronous experiments, dynamic classifiers seem to be the most efficient method to exploit the temporal information contained in features. Similarly, integrating classifiers over time can efficiently utilize the time information BIB004 . For asynchronous experiments, no clear superiority could be observed (see previous section); • non-stationarity: A combination of classifiers may solve this problem as it reduces the Variance. Stable classifiers such as LDA or SVM can also be used but would probably be outperformed by combinations of LDA or SVM; • small training sets: If the training set is small, simple techniques with few parameters should be used, such as LDA BIB001 .