TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Main classification problems in BCI research <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> Main classification problems in BCI research <s> The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field. <s> BIB002
While performing a pattern recognition task, classifiers may be facing several problems related to the features properties such as outliers, overtraining, etc. In the field of BCI, two main problems need to be underlined: the curse-of-dimensionality and the Bias-Variance tradeoff. The curse-of-dimensionality: the amount of data needed to properly describe the different classes increases exponentially with the dimensionality of the feature vectors BIB002  . Actually, if the number of training data is small compared to the size of the feature vectors, the classifier will most probably give poor results. It is recommended to use, at least, five to ten times as many training samples per class as the dimensionality BIB001  . Unfortunatly this cannot be applied in all BCI systems as generally, the dimensionality is high and the training set small (see section 2.1.1). Therefore this "curse" is a major concern in BCI design. The Bias-Variance tradeoff: