TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Conclusion <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> BIB001 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Conclusion <s> This paper introduces the use of a Fuzzy Inference System (FIS) for classification in EEG-based Brain-Computer Interfaces (BCI) systems. We present our FIS algorithm and compare it, on motor imagery signals, with three other popular classifiers, widely used in the BCI community. Our results show that FIS outperformed a Linear Classifier and reached the same level of accuracy as Support Vector Machine and neural networks. Thus, FIS-based classification is suitable for BCI design. Furthermore, FIS algorithms have two additionnal advantages: they are readable and easily extensible. <s> BIB002
A great variety of classifiers has been tried in BCI research. Their properties are summarized in Table 1 . It should be stressed that some famous kinds of classifiers have not been attempted in BCI research. The two most relevant ones are decision trees BIB001 and the whole category of fuzzy classifiers  . Furthermore, different combination schemes of classifiers have been used, but several other efficient and famous ones can be found in the literature such as Bagging or Arcing  BIB001 . Such algorithms could prove useful as they all succeeded in several other pattern recognition problems. As an example, preliminary results using a fuzzy classifier for BCI purposes are promising BIB002 . This paper has surveyed classification algorithms used to design Brain-Computer Interfaces (BCI). These algorithms were divided into five categories: linear classifiers, neural networks, nonlinear Bayesian classifiers, nearest neighbor classifiers and combinations of classifiers. The results they obtained, in a BCI context, have been analysed and compared in order to provide the readers with guidelines to choose or design a classifier for a BCI system. In a nutshell, it seems that SVM are particularly efficient for synchronous BCI. This probably is due to their regularization property and their immunity to the curse-of-dimensionality. Furthermore, combinations of classifiers and dynamic classifiers also seem very efficient in synchronous experiments. This paper focused on reviewing classifiers used in BCI research, i.e., related to published online or offline studies. However, other existing classification techniques, not currently used for BCI purposes, could be explored and may prove to be rewarding. Furthermore, it should be noted that once BCI will be more widely used in clinical practice, new properties will have to be taken into consideration, such as the availability of large data sets or long term variability of EEG signals.