Psychophysiology and Autonomic Responses Perspective for Advancing Hybrid Brain-Computer Interface Systems

R. K. Sinha, G Prasad

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The primary focus of this article was to review the current understanding of motor imagery (MI) as a cognitive process and thereby explore correlated biosignals for devising advanced brain-computer interfaces (BCIs) helping toward overcoming the limitations of current systems.To this end, the article first outlines the process of performing MI, ensuring kinesthetic experiences, and explores the literature to ascertain main brain areas that are activated during MI. Mental chronometry as a conventional method of assessing MI is then discussed, and its main limitation of not being able to measure MI vividness is highlighted. Neurophysiology and neuroelectrophysiology of MI are then analyzed to explain the phenomena of event-related desynchronization and event-related synchronization occurring in the sensorimotor rhythms, used as the principal feature in electroencephalogram-based BCIs.The current understanding on the effect of MI on regional cerebral blood flow and neural metabolism is then reviewed with the objective of explaining how functional magnetic resonance imaging and near infrared spectroscopy can be used to devise a metabolic BCI system.The literature is then reviewed to explore the current understanding on the effect of MI on peripheral nervous system causing variations in autonomic responses. Most importantly, the review identifies a range of biosignals including oxygen consumption, respiratory rate, heart rate, and skin resistance, which have strong potential for developing enhanced BCI devices either alone or in combination with other signals including electroencephalogram and near-infrared spectroscopy, through multisensor integration.
LanguageEnglish
Pages196-209
JournalJournal of Clinical Engineering
Volume38
Issue number4
DOIs
Publication statusPublished - 1 Oct 2013

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Hybrid Computers
Psychophysiology
Brain-Computer Interfaces
Imagery (Psychotherapy)
Computer Systems
Near-Infrared Spectroscopy
Electroencephalography
Cerebrovascular Circulation
Neurophysiology
Regional Blood Flow
Peripheral Nervous System
Respiratory Rate
Oxygen Consumption
Heart Rate
Magnetic Resonance Imaging

Cite this

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abstract = "The primary focus of this article was to review the current understanding of motor imagery (MI) as a cognitive process and thereby explore correlated biosignals for devising advanced brain-computer interfaces (BCIs) helping toward overcoming the limitations of current systems.To this end, the article first outlines the process of performing MI, ensuring kinesthetic experiences, and explores the literature to ascertain main brain areas that are activated during MI. Mental chronometry as a conventional method of assessing MI is then discussed, and its main limitation of not being able to measure MI vividness is highlighted. Neurophysiology and neuroelectrophysiology of MI are then analyzed to explain the phenomena of event-related desynchronization and event-related synchronization occurring in the sensorimotor rhythms, used as the principal feature in electroencephalogram-based BCIs.The current understanding on the effect of MI on regional cerebral blood flow and neural metabolism is then reviewed with the objective of explaining how functional magnetic resonance imaging and near infrared spectroscopy can be used to devise a metabolic BCI system.The literature is then reviewed to explore the current understanding on the effect of MI on peripheral nervous system causing variations in autonomic responses. Most importantly, the review identifies a range of biosignals including oxygen consumption, respiratory rate, heart rate, and skin resistance, which have strong potential for developing enhanced BCI devices either alone or in combination with other signals including electroencephalogram and near-infrared spectroscopy, through multisensor integration.",
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Psychophysiology and Autonomic Responses Perspective for Advancing Hybrid Brain-Computer Interface Systems. / Sinha, R. K.; Prasad, G.

In: Journal of Clinical Engineering, Vol. 38, No. 4, 01.10.2013, p. 196-209.

Research output: Contribution to journalArticle

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