Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification

Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has shown that nonlinear classification algorithms perform better than their linear counterparts, but most of them cannot extract sufficient significant infor...

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Vydané v:	Computational intelligence and neuroscience Ročník 2018; číslo 2018; s. 1 - 9
Hlavní autori:	Nguyen, Thinh, Ma, Yuliang, Chen, Kang, She, Qingshan, Zhang, Yingchun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cairo, Egypt Hindawi Publishing Corporation 01.01.2018 Hindawi John Wiley & Sons, Inc
Predmet:	Algorithms Analysis Brain Brain-Computer Interfaces Classification Competition Computer applications Datasets Datasets as Topic Dictionaries EEG Electroencephalography Evoked Potentials, Motor - physiology Human-computer interface Humans Image classification Imagery (Psychotherapy) - methods Implants Information processing Learning - physiology Learning algorithms Machine Learning Mathematical problems Mental task performance Motor skill learning Motors Neural coding Neural networks Neural Networks (Computer) Neurons Pattern recognition Representations Signal processing Spatial filtering
ISSN:	1687-5265, 1687-5273, 1687-5273
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Shrnutí:	Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has shown that nonlinear classification algorithms perform better than their linear counterparts, but most of them cannot extract sufficient significant information which leads to a less efficient classification. In this paper, we propose a novel approach called FDDL-ELM, which combines the discriminative power of extreme learning machine (ELM) with the reconstruction capability of sparse representation. Firstly, the common spatial pattern (CSP) algorithm is adopted to perform spatial filtering on raw EEG data to enhance the task-related neural activity. Secondly, the Fisher discrimination criterion is employed to learn a structured dictionary and obtain sparse coding coefficients from the filtered data, and these discriminative coefficients are then used to acquire the reconstructed feature representations. Finally, a nonlinear classifier ELM is used to identify these features in different MI tasks. The proposed method is evaluated on 2-class Datasets IVa and IIIa of BCI Competition III and 4-class Dataset IIa of BCI Competition IV. Experimental results show that our method achieved superior performance than the other existing algorithms and yielded the accuracies of 80.68%, 87.54%, and 63.76% across all subjects in the above-mentioned three datasets, respectively.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Academic Editor: João M. R. S. Tavares
ISSN:	1687-5265 1687-5273 1687-5273
DOI:	10.1155/2018/9593682