Optimized preprocessing and Tiny ML for Attention State Classification

Yinghao Wang; Remi Nahon; Enzo Tartaglione; Pavlo Mozharovskyi; Van Tam Nguyen

doi:10.1109/SSP53291.2023.10207930

Optimized preprocessing and Tiny ML for Attention State Classification

Yinghao Wang
, Remi Nahon
, Enzo Tartaglione
, Pavlo Mozharovskyi
, Van Tam Nguyen

Institut Polytechnique de Paris

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

Electroencephalography has been widely used to study mental processes such as attention, perception, and emotion. This is because mental state classification has important applications in many fields, including healthcare, human-computer interaction, and education.In this paper, we present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning (ML) algorithms. We evaluate the performance of the proposed method on a dataset of EEG recordings collected during a cognitive load task. The results show that the proposed method achieves high accuracy in classifying mental states and outperforms state-of-the-art methods in terms of classification accuracy and computational efficiency.

langue originale	Anglais
titre	Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Editeur	IEEE Computer Society
Pages	695-699
Nombre de pages	5
ISBN (Electronique)	9781665452458
Les DOIs	https://doi.org/10.1109/SSP53291.2023.10207930
état	Publié - 1 janv. 2023
Evénement	22nd IEEE Statistical Signal Processing Workshop, SSP 2023 - Hanoi, Viet-Nam Durée: 2 juil. 2023 → 5 juil. 2023

Série de publications

Nom	IEEE Workshop on Statistical Signal Processing Proceedings
Volume	2023-July

Une conférence

Une conférence	22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Pays/Territoire	Viet-Nam
La ville	Hanoi
période	2/07/23 → 5/07/23

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 3 Bonne santé et bien-être

Accès au document

10.1109/SSP53291.2023.10207930

Autres fichiers et liens

Lien vers la publication dans Scopus

Contient cette citation

Wang, Y., Nahon, R., Tartaglione, E., Mozharovskyi, P., & Nguyen, V. T. (2023). Optimized preprocessing and Tiny ML for Attention State Classification. Dans Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023 (p. 695-699). (IEEE Workshop on Statistical Signal Processing Proceedings; Vol 2023-July). IEEE Computer Society. https://doi.org/10.1109/SSP53291.2023.10207930

@inproceedings{54c36a57c326418e97c245a849783154,

title = "Optimized preprocessing and Tiny ML for Attention State Classification",

abstract = "Electroencephalography has been widely used to study mental processes such as attention, perception, and emotion. This is because mental state classification has important applications in many fields, including healthcare, human-computer interaction, and education.In this paper, we present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning (ML) algorithms. We evaluate the performance of the proposed method on a dataset of EEG recordings collected during a cognitive load task. The results show that the proposed method achieves high accuracy in classifying mental states and outperforms state-of-the-art methods in terms of classification accuracy and computational efficiency.",

keywords = "Attention level detection, Brain-computer interface, EEG, Tiny ML",

author = "Yinghao Wang and Remi Nahon and Enzo Tartaglione and Pavlo Mozharovskyi and Nguyen, \{Van Tam\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 22nd IEEE Statistical Signal Processing Workshop, SSP 2023 ; Conference date: 02-07-2023 Through 05-07-2023",

year = "2023",

month = jan,

day = "1",

doi = "10.1109/SSP53291.2023.10207930",

language = "English",

series = "IEEE Workshop on Statistical Signal Processing Proceedings",

publisher = "IEEE Computer Society",

pages = "695--699",

booktitle = "Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023",

}

Wang, Y, Nahon, R, Tartaglione, E , Mozharovskyi, P & Nguyen, VT 2023, Optimized preprocessing and Tiny ML for Attention State Classification. Dans Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023. IEEE Workshop on Statistical Signal Processing Proceedings, VOL. 2023-July, IEEE Computer Society, p. 695-699, 22nd IEEE Statistical Signal Processing Workshop, SSP 2023, Hanoi, Viet-Nam, 2/07/23. https://doi.org/10.1109/SSP53291.2023.10207930

Optimized preprocessing and Tiny ML for Attention State Classification. / Wang, Yinghao; Nahon, Remi; Tartaglione, Enzo et al.
Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023. IEEE Computer Society, 2023. p. 695-699 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol 2023-July).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Optimized preprocessing and Tiny ML for Attention State Classification

AU - Wang, Yinghao

AU - Nahon, Remi

AU - Tartaglione, Enzo

AU - Mozharovskyi, Pavlo

AU - Nguyen, Van Tam

PY - 2023/1/1

Y1 - 2023/1/1

N2 - Electroencephalography has been widely used to study mental processes such as attention, perception, and emotion. This is because mental state classification has important applications in many fields, including healthcare, human-computer interaction, and education.In this paper, we present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning (ML) algorithms. We evaluate the performance of the proposed method on a dataset of EEG recordings collected during a cognitive load task. The results show that the proposed method achieves high accuracy in classifying mental states and outperforms state-of-the-art methods in terms of classification accuracy and computational efficiency.

AB - Electroencephalography has been widely used to study mental processes such as attention, perception, and emotion. This is because mental state classification has important applications in many fields, including healthcare, human-computer interaction, and education.In this paper, we present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning (ML) algorithms. We evaluate the performance of the proposed method on a dataset of EEG recordings collected during a cognitive load task. The results show that the proposed method achieves high accuracy in classifying mental states and outperforms state-of-the-art methods in terms of classification accuracy and computational efficiency.

KW - Attention level detection

KW - Brain-computer interface

KW - EEG

KW - Tiny ML

UR - https://www.scopus.com/pages/publications/85168905097

U2 - 10.1109/SSP53291.2023.10207930

DO - 10.1109/SSP53291.2023.10207930

M3 - Conference contribution

AN - SCOPUS:85168905097

T3 - IEEE Workshop on Statistical Signal Processing Proceedings

SP - 695

EP - 699

BT - Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023

PB - IEEE Computer Society

T2 - 22nd IEEE Statistical Signal Processing Workshop, SSP 2023

Y2 - 2 July 2023 through 5 July 2023

ER -

Optimized preprocessing and Tiny ML for Attention State Classification

Résumé

Série de publications

Une conférence

SDG des Nations Unies

Accès au document

Autres fichiers et liens

Empreinte digitale

Contient cette citation