Spatiooral analysis of EEG signal during consciousness using convolutional neural network

Minji Lee; Seul Ki Yeom; Benjamin Baird; Olivia Gosseries; Jakko O. Nieminen; Giulio Tononi; Seong Whan Lee

doi:10.1109/IWW-BCI.2018.8311489

Spatiooral analysis of EEG signal during consciousness using convolutional neural network

Minji Lee, Seul Ki Yeom, Benjamin Baird, Olivia Gosseries, Jakko O. Nieminen, Giulio Tononi, Seong Whan Lee

Department of Brain and Cognitive Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2018 6th International Conference on Brain-Computer Interface, BCI 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-3
Number of pages	3
ISBN (Electronic)	9781538625743
DOIs	https://doi.org/10.1109/IWW-BCI.2018.8311489
Publication status	Published - 2018 Mar 9
Event	6th International Conference on Brain-Computer Interface, BCI 2018 - GangWon, Korea, Republic of Duration: 2018 Jan 15 → 2018 Jan 17

Publication series

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

Other

Other	6th International Conference on Brain-Computer Interface, BCI 2018
Country/Territory	Korea, Republic of
City	GangWon
Period	18/1/15 → 18/1/17

Keywords

classification
consciousness
sedation
sleep
transcranial magnetic stimulation

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Behavioral Neuroscience

Access to Document

10.1109/IWW-BCI.2018.8311489

Cite this

Lee, M., Yeom, S. K., Baird, B., Gosseries, O., Nieminen, J. O., Tononi, G., & Lee, S. W. (2018). Spatiooral analysis of EEG signal during consciousness using convolutional neural network. In 2018 6th International Conference on Brain-Computer Interface, BCI 2018 (pp. 1-3). (2018 6th International Conference on Brain-Computer Interface, BCI 2018; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2018.8311489

Spatiooral analysis of EEG signal during consciousness using convolutional neural network. / Lee, Minji; Yeom, Seul Ki; Baird, Benjamin; Gosseries, Olivia; Nieminen, Jakko O.; Tononi, Giulio; Lee, Seong Whan.

2018 6th International Conference on Brain-Computer Interface, BCI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-3 (2018 6th International Conference on Brain-Computer Interface, BCI 2018; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, M, Yeom, SK, Baird, B, Gosseries, O, Nieminen, JO, Tononi, G & Lee, SW 2018, Spatiooral analysis of EEG signal during consciousness using convolutional neural network. in 2018 6th International Conference on Brain-Computer Interface, BCI 2018. 2018 6th International Conference on Brain-Computer Interface, BCI 2018, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-3, 6th International Conference on Brain-Computer Interface, BCI 2018, GangWon, Korea, Republic of, 18/1/15. https://doi.org/10.1109/IWW-BCI.2018.8311489

Lee M, Yeom SK, Baird B, Gosseries O, Nieminen JO, Tononi G et al. Spatiooral analysis of EEG signal during consciousness using convolutional neural network. In 2018 6th International Conference on Brain-Computer Interface, BCI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-3. (2018 6th International Conference on Brain-Computer Interface, BCI 2018). https://doi.org/10.1109/IWW-BCI.2018.8311489

Lee, Minji ; Yeom, Seul Ki ; Baird, Benjamin ; Gosseries, Olivia ; Nieminen, Jakko O. ; Tononi, Giulio ; Lee, Seong Whan. / Spatiooral analysis of EEG signal during consciousness using convolutional neural network. 2018 6th International Conference on Brain-Computer Interface, BCI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-3 (2018 6th International Conference on Brain-Computer Interface, BCI 2018).

@inproceedings{f4aa6095a55247dcb2efc4141acf5bb1,

title = "Spatiooral analysis of EEG signal during consciousness using convolutional neural network",

abstract = "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.",

keywords = "classification, consciousness, sedation, sleep, transcranial magnetic stimulation",

author = "Minji Lee and Yeom, {Seul Ki} and Benjamin Baird and Olivia Gosseries and Nieminen, {Jakko O.} and Giulio Tononi and Lee, {Seong Whan}",

note = "Funding Information: This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Publisher Copyright: {\textcopyright} 2018 IEEE.; 6th International Conference on Brain-Computer Interface, BCI 2018 ; Conference date: 15-01-2018 Through 17-01-2018",

year = "2018",

month = mar,

day = "9",

doi = "10.1109/IWW-BCI.2018.8311489",

language = "English",

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

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--3",

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

}

TY - GEN

T1 - Spatiooral analysis of EEG signal during consciousness using convolutional neural network

AU - Lee, Minji

AU - Yeom, Seul Ki

AU - Baird, Benjamin

AU - Gosseries, Olivia

AU - Nieminen, Jakko O.

AU - Tononi, Giulio

AU - Lee, Seong Whan

N1 - Funding Information: This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Publisher Copyright: © 2018 IEEE.

PY - 2018/3/9

Y1 - 2018/3/9

N2 - 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.

AB - 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.

KW - classification

KW - consciousness

KW - sedation

KW - sleep

KW - transcranial magnetic stimulation

UR - http://www.scopus.com/inward/record.url?scp=85050795936&partnerID=8YFLogxK

U2 - 10.1109/IWW-BCI.2018.8311489

DO - 10.1109/IWW-BCI.2018.8311489

M3 - Conference contribution

AN - SCOPUS:85050795936

T3 - 2018 6th International Conference on Brain-Computer Interface, BCI 2018

SP - 1

EP - 3

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th International Conference on Brain-Computer Interface, BCI 2018

Y2 - 15 January 2018 through 17 January 2018

ER -

Spatiooral analysis of EEG signal during consciousness using convolutional neural network

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this