TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Introduction <s> With the increasing efficiency of life-support systems and better intensive care, more patients survive severe injuries of the brain and spinal cord. Many of these patients experience locked-in syndrome: The active mind is locked in a paralyzed body. Consequently, communication is extremely restricted or impossible. A muscle-independent communication channel overcomes this problem and is realized through a brain-computer interface, a direct connection between brain and computer. The number of technically elaborated brain-computer interfaces is in contrast with the number of systems used in the daily life of locked-in patients. It is hypothesized that a profound knowledge and consideration of psychological principles are necessary to make brain-computer interfaces feasible for locked-in patients. <s> BIB001 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Introduction <s> This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback. <s> BIB002 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Introduction <s> Abstract For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world – a brain–computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and control technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or ‘locked in’, with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have maximum information transfer rates up to 10–25 bits/min. This limited capacity can be valuable for people whose severe disabilities prevent them from using conventional augmentative communication methods. At the same time, many possible applications of BCI technology, such as neuroprosthesis control, may require higher information transfer rates. Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to control independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that control; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and control capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances. <s> BIB003
A Brain-Computer Interface (BCI) is a communication system that does not require any peripheral muscular activity BIB003 . Indeed, BCI systems enable a subject to send commands to an electronic device only by means of brain activity  . Such interfaces can be considered as being the only way of communication for people affected by a number of motor disabilities BIB001 . In order to control a BCI, the user must produce different brain activity patterns that will be identified by the system and translated into commands. In most existing BCI, this identification relies on a classification algorithm BIB002 , i.e., an algorithm that aims at automatically estimating the class of data as represented by a feature vector  . Due to the rapidly growing interest for EEG-based BCI, a considerable number of published results is related to the investigation and evaluation of classification algorithms. To date, very interesting reviews of BCI have been published BIB003 [6] but none has been specifically dedicated to the review of classification algorithms used for BCI, their properties and their evaluation. This paper aims at filling this lack. Therefore, one of the main objectives of this paper is to survey the different classification algorithms used in EEG-based BCI research and to identify their critical properties. Another objective is to provide guidelines in order to help the reader with choosing the most appropriate classification algorithm for a given BCI experiment. This amounts to comparing the algorithms and assessing their performances according to the context. This paper is organized as follows: Section 2 depicts a BCI as a pattern recognition system and emphasizes the role of classification. Section 3 surveys the classification algorithms used for BCI and finally, Section 4 assesses them and identifies their usability depending on the context.