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Thoughts on Neurophysiological Signal Analysis and Classification

Li, Junhua (2021) 'Thoughts on Neurophysiological Signal Analysis and Classification.' Brain Science Advances, 6 (3). 210 - 223. ISSN 2096-5958

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Abstract

Neurophysiological signal is crucial intermediary, through which brain activity can be quantitatively measured and brain mechanisms are able to be revealed. In particular, those non-invasive neurophysiological signals, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), are welcome and frequently utilised in a variety of studies because those signals can be non-invasively recorded without harms to the human brain while they are conveying 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 signal, but scholars are not feeling complacent. Consistent effort ought to be paid to advance the research of analysis and classification based on neurophysiological signal. In this paper, I express my thoughts about promising future directions in neurophysiological signal analysis and classification based on the current developments and achievements. I will elucidate the thoughts after brief summaries of relevant backgrounds, achievements, 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 analysis and classification of neurophysiological signal in some way.

Item Type: Article
Uncontrolled Keywords: electroencephalogram (EEG), magnetic resonance imaging (MRI), tensor, brain connectivity, multiple tasks, multi‐modality, classification, big data, deep learning, naturalistic experiment
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 15 Jul 2021 14:20
Last Modified: 15 Jul 2021 14:20
URI: http://repository.essex.ac.uk/id/eprint/29089

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