Signal recognition methods in motor imagery BCI

Abstract

Brain computer interface constructs the direct connection between human brain and external devices, which is becoming a promising method in reconstructing human’s motor abilities who suffered from body disability. Among several kinds of BCI technologies, Motor Imagery based Brain-computer Interface (MI-BCI) has attracted more and more attentions since it’s a more intuitive method. In the procedure of MI-BCI, the signal recognition methods play a significant role. Therefore, this paper would search into the classification techniques utilized in the processing procedure in MI-BCI systems, including machine learning techniques, naïve bayes classifier (NB), support vector machines (SVM) and linear discriminant analysis (LDA). For deep learning techniques, sparse autoencoder (SAE), convolutional neural network (CNN), recurrent neural network (RNN) was introduced. Then the paper would compare them in terms of accuracy, classification speed and data requirement. This paper would give an overview on the commonly seen classification method used in MI-BCI, and also present researchers who are selecting classification methods the most suitable choice.