Spatiotemporal analysis of EEG signal during consciousness using convolutional neural network

Brain; Classification (of information); Convolution; Electroencephalography; Functional electric stimulation; Interfaces (computer); Neural networks; Neurophysiology; Signal analysis; Behavioral response; Convolutional neural network; Electro-encephalogram (EEG); Level of consciousness; Transcranial magnetic stimulation; Brain computer interface

Abstract :

[en] Electroencephalogram (EEG) measurement could help to distinguish the level of consciousness in an individual without requiring a behavioral response, which could be useful as a diagnostic aid in patients with disorders of consciousness. In this study, we explored the EEG-evoked perturbation and analyzed consciousness using event-related spectral perturbation and convolutional neural network. We observed a novel EEG neurophysiological signature that can be used to monitor brain activity during unconsciousness. Also, the performance accuracy in the parietal region was higher than in the frontal region. The sensitivity for conscious experience was 90.9%, whereas sensitivity for unconscious experience was at the chance level in the parietal region. These results could be evidence for the importance of the posterior hot zone and could help shed light on the internal neural dynamics related to conscious experience. © 2018 IEEE.

Disciplines :

Neurosciences & behavior

Author, co-author :

Lee, Minji; Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea

Yeom, S.-K.; Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea

Baird, Benjamin; Department of Psychiatry, University of Wisconsin, Madison, United States

Gosseries, Olivia ; Université de Liège - ULiège > GIGA : Coma Group

Nieminen, Jaakko O.; Department of Psychiatry, University of Wisconsin, Madison, United States, Department of Neuroscience and Biomedical Engineering, Aalto University, School of Science, Espoo, Finland

Tononi, Giulio; Department of Psychiatry, University of Wisconsin, Madison, United States

Lee, S.-W.; Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea

Language :

English

Title :

Spatiotemporal analysis of EEG signal during consciousness using convolutional neural network

Publication date :

2018

Event name :

6th International Conference on Brain-Computer Interface, BCI 2018

Event date :

15 January 2018 through 17 January 2018

Main work title :

2018 6th International Conference on Brain-Computer Interface, BCI 2018

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Peer reviewed :

Peer reviewed

Funders :

IITP - Institute for Information and communications Technology Promotion [KR]
BCI - Bat Conservation International [US-TX] [US-TX]

Commentary :

135230 9781538625743

Available on ORBi :

since 22 August 2019

Statistics

Number of views

72 (6 by ULiège)

Number of downloads

235 (5 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

Bibliography

T. L. T. da Silveira, A. J. Kozakevicius, C. R. Rodrigues, "Single?channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain," Med. Biol. Eng. Comput. Heidelberg, vol. 55, pp. 343-352, Feb. 2017.
F. Siclari et al., "The neural correlates of dreaming," Nat. Neurosci. New York, vol. 20, pp. 872-881, June 2017.
F. Siclari, J. J. LaRocque, B. R. Postle, G. Tononi, "Assessing sleep consciousness within subjects using a serial awakening paradigm," Front. Psychol. Lausanne, vol. 4, pp. 1-9, Aug. 2013.
C. Koch, M. Massimini, M. Boly, G. Tononi, "Posterior and anterior cortex-where is the difference that makes the difference?," Nat. Rev. Neurosci. London, vol. 17, pp. 105, Oct. 2016.
M. Massimini, F. Ferrarelli, S. Sarasso, G. Tononi, "Cortical mechanisms of loss of consciousness: Insight from TMS/EEG studies," Arch. Ital. Biol. Pisa, vol. 150, pp. 44-55, June 2012.
S. Sarasso et al., "Quantifying cortical EEG responses to TMS in (un)consciousness," Clin. EEG Neurosci. Thousand Oaks, vol. 45, pp. 40-49. Jan. 2014.
J. O. Nieminen et al., "Conciousness and cortical responsiveness: A within-state study during non-rapid eye movement sleep," Sie. Rep. London, vol. 6, pp. 1-10, Aug. 2016.
H. Cecotti and A. Graser, "Convolutional neural networks for P300 detection with application to brain-computer interfaces," IEEE Trans. Pattern Anal. Mach. Intell. Los Alamitos, vol. 33, pp. 433-435, Mar. 2011.
A. Delorme, S. Makeig, "EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis," J. Neurosci. Methods. Amsterdam, vol. 134, pp. 9-21, Mar. 2004.
R. B. Berry et al., "Rules for scoring respiratory events in sleep: Update of the 2007 AASM manual for the scoring of sleep and associated events," J. Clin. Sleep Med. Westchester, vol. 8, pp. 597-619, Oct. 2012.
J. Yoo, J. Kwon, Y. Choe, "Predictable internal brain dynamics in EEG and its relation to conscious states," Front. Neurorobotics. Lausanne, vol. 8, pp. 1-7, June 2014.