Signal Selection Technique based on Statistical Approach for Enhanced Detection of Single-trial Auditory Evoked Potentials

C. M. Jijomon; A. P. Vinod

doi:10.1109/EMBC40787.2023.10340031

Signal Selection Technique based on Statistical Approach for Enhanced Detection of Single-trial Auditory Evoked Potentials

C. M. Jijomon^*, A. P. Vinod

^*Corresponding author for this work

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

Abstract

Volume conduction from insignificant neuronal sources in the brain poses a challenge to the detection and classification of single-trial low amplitude evoked potentials in electroencephalograph (EEG). This work presents a statistical signal selection method for enhanced detection of single-trial EEG auditory evoked potential (AEP) elicited in the brain in response to subjects' own name audio stimulus. The proposed method comprises of a signal selection stage based on a statistical analysis followed by a support vector machine (SVM)-based classifier. The EEG signals recorded from the Fp1 electrode of 24 subjects are used to generate a classifier-dependent feature vector. With the selected one-quarter of AEP signals, a single-trial classification accuracy of 70.59% is obtained. To the best of our knowledge, this is the first study that reports the classification of single-trial AEPs evoked by subjects' own-name audio stimulus versus familiar-name audio stimulus.

Original language	English
Title of host publication	2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350324471
DOIs	https://doi.org/10.1109/EMBC40787.2023.10340031
Publication status	Published - 2023
Externally published	Yes
Event	45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Sydney, Australia Duration: 24 Jul 2023 → 27 Jul 2023

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023
Country/Territory	Australia
City	Sydney
Period	24/07/23 → 27/07/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Access to Document

10.1109/EMBC40787.2023.10340031

Cite this

Jijomon, C. M., & Vinod, A. P. (2023). Signal Selection Technique based on Statistical Approach for Enhanced Detection of Single-trial Auditory Evoked Potentials. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC40787.2023.10340031

Jijomon, C. M. ; Vinod, A. P. / Signal Selection Technique based on Statistical Approach for Enhanced Detection of Single-trial Auditory Evoked Potentials. 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{55844ce2893b4a7895e32d7e0923d517,

title = "Signal Selection Technique based on Statistical Approach for Enhanced Detection of Single-trial Auditory Evoked Potentials",

abstract = "Volume conduction from insignificant neuronal sources in the brain poses a challenge to the detection and classification of single-trial low amplitude evoked potentials in electroencephalograph (EEG). This work presents a statistical signal selection method for enhanced detection of single-trial EEG auditory evoked potential (AEP) elicited in the brain in response to subjects' own name audio stimulus. The proposed method comprises of a signal selection stage based on a statistical analysis followed by a support vector machine (SVM)-based classifier. The EEG signals recorded from the Fp1 electrode of 24 subjects are used to generate a classifier-dependent feature vector. With the selected one-quarter of AEP signals, a single-trial classification accuracy of 70.59\% is obtained. To the best of our knowledge, this is the first study that reports the classification of single-trial AEPs evoked by subjects' own-name audio stimulus versus familiar-name audio stimulus.",

author = "Jijomon, \{C. M.\} and Vinod, \{A. P.\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 ; Conference date: 24-07-2023 Through 27-07-2023",

year = "2023",

doi = "10.1109/EMBC40787.2023.10340031",

language = "English",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

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

booktitle = "2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings",

}

Jijomon, CM & Vinod, AP 2023, Signal Selection Technique based on Statistical Approach for Enhanced Detection of Single-trial Auditory Evoked Potentials. in 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023, Sydney, Australia, 24/07/23. https://doi.org/10.1109/EMBC40787.2023.10340031

Signal Selection Technique based on Statistical Approach for Enhanced Detection of Single-trial Auditory Evoked Potentials. / Jijomon, C. M.; Vinod, A. P.
2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Signal Selection Technique based on Statistical Approach for Enhanced Detection of Single-trial Auditory Evoked Potentials

AU - Jijomon, C. M.

AU - Vinod, A. P.

PY - 2023

Y1 - 2023

N2 - Volume conduction from insignificant neuronal sources in the brain poses a challenge to the detection and classification of single-trial low amplitude evoked potentials in electroencephalograph (EEG). This work presents a statistical signal selection method for enhanced detection of single-trial EEG auditory evoked potential (AEP) elicited in the brain in response to subjects' own name audio stimulus. The proposed method comprises of a signal selection stage based on a statistical analysis followed by a support vector machine (SVM)-based classifier. The EEG signals recorded from the Fp1 electrode of 24 subjects are used to generate a classifier-dependent feature vector. With the selected one-quarter of AEP signals, a single-trial classification accuracy of 70.59% is obtained. To the best of our knowledge, this is the first study that reports the classification of single-trial AEPs evoked by subjects' own-name audio stimulus versus familiar-name audio stimulus.

AB - Volume conduction from insignificant neuronal sources in the brain poses a challenge to the detection and classification of single-trial low amplitude evoked potentials in electroencephalograph (EEG). This work presents a statistical signal selection method for enhanced detection of single-trial EEG auditory evoked potential (AEP) elicited in the brain in response to subjects' own name audio stimulus. The proposed method comprises of a signal selection stage based on a statistical analysis followed by a support vector machine (SVM)-based classifier. The EEG signals recorded from the Fp1 electrode of 24 subjects are used to generate a classifier-dependent feature vector. With the selected one-quarter of AEP signals, a single-trial classification accuracy of 70.59% is obtained. To the best of our knowledge, this is the first study that reports the classification of single-trial AEPs evoked by subjects' own-name audio stimulus versus familiar-name audio stimulus.

UR - https://www.webofscience.com/wos/woscc/full-record/WOS:001133788300088

U2 - 10.1109/EMBC40787.2023.10340031

DO - 10.1109/EMBC40787.2023.10340031

M3 - Conference Paper published in a book

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

BT - 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023

Y2 - 24 July 2023 through 27 July 2023

ER -

Jijomon CM, Vinod AP. Signal Selection Technique based on Statistical Approach for Enhanced Detection of Single-trial Auditory Evoked Potentials. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC40787.2023.10340031

Signal Selection Technique based on Statistical Approach for Enhanced Detection of Single-trial Auditory Evoked Potentials

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this