Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning

In recent years, the accurate and real-time classification of electroencephalogram (EEG) signals has drawn increasing attention in the application of brain-computer interface technology (BCI). Supervised methods used to classify EEG signals have gotten satisfactory results. However, unlabeled sample...

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Bibliographic Details
Published in:Electronics (Basel) Vol. 8; no. 11; p. 1273
Main Authors: She, Qingshan, Zhou, Yukai, Gan, Haitao, Ma, Yuliang, Luo, Zhizeng
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2019
Subjects:
Accuracy
Algorithms
Classification
Datasets
Deep learning
Electroencephalography
Human-computer interface
Learning
Neural networks
Signal classification
Support vector machines
ISSN:2079-9292, 2079-9292
Online Access:Get full text
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Summary:In recent years, the accurate and real-time classification of electroencephalogram (EEG) signals has drawn increasing attention in the application of brain-computer interface technology (BCI). Supervised methods used to classify EEG signals have gotten satisfactory results. However, unlabeled samples are more frequent than labeled samples, so how to simultaneously utilize limited labeled samples and many unlabeled samples becomes a research hotspot. In this paper, we propose a new graph-based semi-supervised broad learning system (GSS-BLS), which combines the graph label propagation method to obtain pseudo-labels and then trains the GSS-BLS classifier together with other labeled samples. Three BCI competition datasets are used to assess the GSS-BLS approach and five comparison algorithms: BLS, ELM, HELM, LapSVM and SMIR. The experimental results show that GSS-BLS achieves satisfying Cohen’s kappa values in three datasets. GSS-BLS achieves the better results of each subject in the 2-class and 4-class datasets and has significant improvements compared with original BLS except subject C6. Therefore, the proposed GSS-BLS is an effective semi-supervised algorithm for classifying EEG signals.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics8111273