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Brain Science Advances 2020, 6(3): 210-223 https://doi.org/10.26599/BSA.2020.9050020
Review Article | Open Access | Issue | Published: 04 February 2021

Thoughts on neurophysiological signal analysis and classification

Show Author's Information Junhua Li1,2,3( )
Laboratory for Brain-Bionic Intelligence and Computational Neuroscience, Wuyi University, Jiangmen 529020, Guangdong, China
Centre for Multidisciplinary Convergence Computing, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China
School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK
Keywords:
electroencephalogram (EEG), magnetic resonance imaging (MRI), tensor, brain connectivity, multiple tasks, multi-modality, classification, big data, deep learning, naturalistic experiment
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Li J. Thoughts on neurophysiological signal analysis and classification. Brain Science Advances, 2020, 6(3): 210-223. https://doi.org/10.26599/BSA.2020.9050020
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Abstract Full text About this article

Neurophysiological signals are crucial intermediaries, through which brain activity can be quantitatively measured and brain mechanisms are able to be revealed. In particular, non-invasive neurophysiological signals, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), are welcomed and frequently utilised in various studies since these signals can be non-invasively recorded without harming the human brain while they convey abundant information pertaining to brain activity. The recorded neurophysiological signals are analysed to mine meaningful information for the understanding of brain mechanisms or are classified to distinguish different patterns (e.g., different cognitive states, brain diseases versus healthy controls). To date, remarkable progress has been made in both the analysis and classification of neurophysiological signals, but scholars are not feeling complacent. Consistent effort ought to be paid to advance the research of analysis and classification based on neurophysiological signals. In this paper, I express my thoughts regarding promising future directions in neurophysiological signal analysis and classification based on current developments and accomplishments. I will elucidate the thoughts after brief summaries of relevant backgrounds, accomplishments, and tendencies. According to my personal selection and preference, I mainly focus on brain connectivity, multidimensional array (tensor), multi-modality, multiple task classification, deep learning, big data, and naturalistic experiment. Hopefully, my thoughts could give a little help to inspire new ideas and contribute to the research of the analysis and classification of neurophysiological signals in some way.


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About this article

Thoughts on neurophysiological signal analysis and classification

Show Author's information Junhua Li1,2,3( )
Laboratory for Brain-Bionic Intelligence and Computational Neuroscience, Wuyi University, Jiangmen 529020, Guangdong, China
Centre for Multidisciplinary Convergence Computing, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China
School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK

Abstract

Neurophysiological signals are crucial intermediaries, through which brain activity can be quantitatively measured and brain mechanisms are able to be revealed. In particular, non-invasive neurophysiological signals, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), are welcomed and frequently utilised in various studies since these signals can be non-invasively recorded without harming the human brain while they convey abundant information pertaining to brain activity. The recorded neurophysiological signals are analysed to mine meaningful information for the understanding of brain mechanisms or are classified to distinguish different patterns (e.g., different cognitive states, brain diseases versus healthy controls). To date, remarkable progress has been made in both the analysis and classification of neurophysiological signals, but scholars are not feeling complacent. Consistent effort ought to be paid to advance the research of analysis and classification based on neurophysiological signals. In this paper, I express my thoughts regarding promising future directions in neurophysiological signal analysis and classification based on current developments and accomplishments. I will elucidate the thoughts after brief summaries of relevant backgrounds, accomplishments, and tendencies. According to my personal selection and preference, I mainly focus on brain connectivity, multidimensional array (tensor), multi-modality, multiple task classification, deep learning, big data, and naturalistic experiment. Hopefully, my thoughts could give a little help to inspire new ideas and contribute to the research of the analysis and classification of neurophysiological signals in some way.

Keywords: electroencephalogram (EEG), magnetic resonance imaging (MRI), tensor, brain connectivity, multiple tasks, multi-modality, classification, big data, deep learning, naturalistic experiment

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Publication history
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Publication history

Received: 09 June 2020
Revised: 02 July 2020
Accepted: 27 July 2020
Published: 04 February 2021
Issue date: September 2020

Copyright

© The authors 2020

Acknowledgements

I would like to thank Shen Ren and Shu Gong for their help in the preparation of figures.

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