A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

Motor imagery electroencephalography (MI-EEG), which is an important subfield of active brain-computer interface (BCI) systems, can be applied to help disabled people to consciously and directly control prosthesis or external devices, aiding them in certain daily activities. However, the low signal-...

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Published in:	IEEE access Vol. 8; pp. 202100 - 202110
Main Authors:	Yang, Jun, Ma, Zhengmin, Wang, Jin, Fu, Yunfa
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Artificial neural networks Biological neural networks Brain–computer interface Convolution convolution neural network Datasets Decoding Deep learning discriminative and representative deep learning Electroencephalography feature fusion Human-computer interface Imagery Multilayers People with disabilities Prostheses Representations Signal to noise ratio Spatial resolution stacked sparse autoencoder Task analysis Training
ISSN:	2169-3536, 2169-3536
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Abstract	Motor imagery electroencephalography (MI-EEG), which is an important subfield of active brain-computer interface (BCI) systems, can be applied to help disabled people to consciously and directly control prosthesis or external devices, aiding them in certain daily activities. However, the low signal-to-noise ratio and spatial resolution make MI-EEG decoding a challenging task. Recently, some deep neural approaches have shown good improvements over state-of-the-art BCI methods. In this study, an end-to-end scheme that includes a multi-layer convolution neural network is constructed for an accurate spatial representation of multi-channel grouped MI-EEG signals, which is employed to extract the useful information present in a multi-channel MI signal. Then the invariant spatial representations are captured from across-subjects training for enhancing the generalization capability through a stacked sparse autoencoder framework, which is inspired by representative deep learning models. Furthermore, a quantitative experimental analysis is conducted on our private dataset and on a public BCI competition dataset. The results show the effectiveness and significance of the proposed methodology.
AbstractList	Motor imagery electroencephalography (MI-EEG), which is an important subfield of active brain-computer interface (BCI) systems, can be applied to help disabled people to consciously and directly control prosthesis or external devices, aiding them in certain daily activities. However, the low signal-to-noise ratio and spatial resolution make MI-EEG decoding a challenging task. Recently, some deep neural approaches have shown good improvements over state-of-the-art BCI methods. In this study, an end-to-end scheme that includes a multi-layer convolution neural network is constructed for an accurate spatial representation of multi-channel grouped MI-EEG signals, which is employed to extract the useful information present in a multi-channel MI signal. Then the invariant spatial representations are captured from across-subjects training for enhancing the generalization capability through a stacked sparse autoencoder framework, which is inspired by representative deep learning models. Furthermore, a quantitative experimental analysis is conducted on our private dataset and on a public BCI competition dataset. The results show the effectiveness and significance of the proposed methodology.
Author	Fu, Yunfa Wang, Jin Yang, Jun Ma, Zhengmin
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Snippet	Motor imagery electroencephalography (MI-EEG), which is an important subfield of active brain-computer interface (BCI) systems, can be applied to help disabled... Motor imagery electroencephalography (MI-EEG), which is an important subfield of active brain–computer interface (BCI) systems, can be applied to help disabled...
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SubjectTerms	Artificial neural networks Biological neural networks Brain–computer interface Convolution convolution neural network Datasets Decoding Deep learning discriminative and representative deep learning Electroencephalography feature fusion Human-computer interface Imagery Multilayers People with disabilities Prostheses Representations Signal to noise ratio Spatial resolution stacked sparse autoencoder Task analysis Training
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Title	A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion
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