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

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

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.

Original language	English
Title of host publication	Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Publisher	IEEE Computer Society
Pages	695-699
Number of pages	5
ISBN (Electronic)	9781665452458
DOIs	https://doi.org/10.1109/SSP53291.2023.10207930
Publication status	Published - 1 Jan 2023
Event	22nd IEEE Statistical Signal Processing Workshop, SSP 2023 - Hanoi, Viet Nam Duration: 2 Jul 2023 → 5 Jul 2023

Publication series

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

Conference

Conference	22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Country/Territory	Viet Nam
City	Hanoi
Period	2/07/23 → 5/07/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Attention level detection
Brain-computer interface
EEG
Tiny ML

Access to Document

10.1109/SSP53291.2023.10207930

Cite this

Wang, Y., Nahon, R., Tartaglione, E., Mozharovskyi, P., & Nguyen, V. T. (2023). Optimized preprocessing and Tiny ML for Attention State Classification. In Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023 (pp. 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. in Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023. IEEE Workshop on Statistical Signal Processing Proceedings, vol. 2023-July, IEEE Computer Society, pp. 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).

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

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

Abstract

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UN SDGs

Keywords

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