The IUP Journal of Computer Sciences
Feature Extraction and Dimensionality Reduction Techniques with Their Advantages and Disadvantages for EEG-Based BCI System: A Review

Article Details
Pub. Date : Jan, 2020
Product Name : The IUP Journal of Computer Sciences
Product Type : Article
Product Code : IJCS20120
Author Name : Abhilasha Nakra, Manoj Duhan
Availability : YES
Subject/Domain : Management
Download Format : PDF Format
No. of Pages : 14



Brain Computer Interface (BCI) is a technique used to build the interface between human brain and computer so that the system can read the signal produced in the brain and interpret it with the best accuracy. It translates the Electroencephalo Graph (EEG) signal produced by the brain and the features of the signals are extracted which are used for classification. Feature extraction is the most significant and one of the initial stages of the BCI system. This stage is the base of the classification. The paper presents an overall analysis of recent methodology of feature extraction methods. It also highlights the advantages and disadvantages of methods by reviewing the literature, books and other related documents. This review helps in choosing a suitable and efficient method of feature extraction which leads to the proper and error-free classification of the signals. Moreover, comparison among various techniques is also done to know about the overall aspects of the techniques so that appropriate use of the technique leads to classification stage precision.


BCI stands for Brain Computer Interface. It refers to interactions between human brains and computers. Tanaka et al. (2005) reported a case of BCI framework: there could be a player who is sitting before a pinball machine with the undertaking to control the flippers of the pinball machine with his thoughts. It offers nonmuscular communication and control channels which do not require any peripheral muscular activity. Although laymen usually consider BCI as another field, the idea and term "Brain-Computer Interface" was introduced by Jacques J Vidal in 1973. The applications of BCI are sensorimotor control, neuromuscular disorders rehabilitation, task-related performance augmentation and target recognition. Figure 1 shows the typical BCI system which depicts the basic requirement of any BCI system.


Electroencephalograph (EEG), Classification, Feature extraction, BCI, Dimensionality reduction

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