A Single-trial Multi-class Classification of Various Motor Imagery Tasks for EEG-based Brain-computer Interface Communication

In this chapter, a nearest neighbor (k-NN)-based method for efficient classification of motor imagery using EEG for brain-computer interfacing (BCI) applications has been proposed. Electroencephalogram (EEG) signals are obtained from multiple channels from brain. These EEG signals are taken as input features and given to the k-NN-based classifier to classify motor imagery. More specifically, the chapter gives an outline of the Berlin brain-computer interface that can be operated with minimal subject change. All the design and simulation works are carried out with MATLAB software. k-NN-based classifier is trained with data from continuous signals of EEG channels. After the network is trained, it is tested with various test cases. Performance of the network is checked in terms of percentage accuracy, which is found to be 99.25%. The result suggested that the proposed method is accurate for BCI applications.

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A brain-computer interface system for smart home control based on single trial motor imagery EEG

International Journal of Sensor Networks ◽

10.1504/ijsnet.2020.111780 ◽

2020 ◽

Vol 34 (4) ◽

pp. 214

Author(s):

Wei Zhuang ◽

Yixian Shen ◽

Lu Li ◽

Chunming Gao ◽

Dong Dai

Keyword(s):

Motor Imagery ◽

Smart Home ◽

Brain Computer Interface ◽

Computer Interface ◽

Single Trial ◽

Interface System

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Temporal and Spatial Features of Single-Trial EEG for Brain-Computer Interface

Computational Intelligence and Neuroscience ◽

10.1155/2007/37695 ◽

2007 ◽

Vol 2007 ◽

pp. 1-14 ◽

Cited By ~ 5

Author(s):

Qibin Zhao ◽

Liqing Zhang

Keyword(s):

Brain Computer Interface ◽

Hand Movement ◽

Event Related Potentials ◽

Computer Interface ◽

Single Trial ◽

Spatial Filters ◽

Classification Rate ◽

Temporal Features ◽

Related Potentials

Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.

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