الگوریتم های دسته بندی رابط مغز و رایانه (BCI) بر پایه الکتروانسفالوگرافی (EEG)

ABSTRACT A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces

A Brain-Computer Interface (BCI) is a communication system that does not require any peripheral muscular activity [1]. Indeed, BCI systems enable a subject to send commands to an electronic device only by means of brain activity [2]. Such interfaces can be considered as being the only way of communication for people affected by a number of motor disabilities [3].

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 [4], i.e., an algorithm that aims at automatically estimating the class of data as represented by a feature vector [5]. 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 [1] [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.