Toward reliable signals decoding for electroencephalogram

A benchmark study to EEGNeX

verfasst von: Xia Chen, Xiangbin Teng, Han Chen, Yafeng Pan, Philipp Geyer
Abstract: This study examines the efficacy of various neural network (NN) models in interpreting mental constructs via electroencephalogram (EEG) signals. Through the assessment of 16 prevalent NN models and their variants across four brain-computer interface (BCI) paradigms, we gauged their information representation capability. Rooted in comprehensive literature review findings, we proposed EEGNeX, a novel, purely ConvNet-based architecture. We pitted it against both existing cutting-edge strategies and the Mother of All BCI Benchmarks (MOABB) involving 11 distinct EEG motor imagination (MI) classification tasks and revealed that EEGNeX surpasses other state-of-the-art methods. Notably, it shows up to 2.1%–8.5% improvement in the classification accuracy in different scenarios with statistical significance (p < 0.05) compared to its competitors. This study not only provides deeper insights into designing efficient NN models for EEG data but also lays groundwork for future explorations into the relationship between bioelectric brain signals and NN architectures. For the benefit of broader scientific collaboration, we have made all benchmark models, including EEGNeX, publicly available at (https://github.com/chenxiachan/EEGNeX).
Organisationseinheit(en): Abteilung Gebäudetechnik
Externe Organisation(en): The Chinese University of Hong Kong
Zhejiang University
Typ: Artikel
Journal: Biomedical Signal Processing and Control
Band: 87
ISSN: 1746-8094
Publikationsdatum: 01.2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Signalverarbeitung, Biomedizintechnik, Gesundheitsinformatik
Elektronische Version(en): https://doi.org/10.48550/arXiv.2207.12369 (Zugang: Offen)
https://doi.org/10.1016/j.bspc.2023.105475 (Zugang: Geschlossen)

BibTeX

@article{b66bb44c5d9343c982e1837d7f70d495,
title = "Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX",
abstract = "This study examines the efficacy of various neural network (NN) models in interpreting mental constructs via electroencephalogram (EEG) signals. Through the assessment of 16 prevalent NN models and their variants across four brain-computer interface (BCI) paradigms, we gauged their information representation capability. Rooted in comprehensive literature review findings, we proposed EEGNeX, a novel, purely ConvNet-based architecture. We pitted it against both existing cutting-edge strategies and the Mother of All BCI Benchmarks (MOABB) involving 11 distinct EEG motor imagination (MI) classification tasks and revealed that EEGNeX surpasses other state-of-the-art methods. Notably, it shows up to 2.1%–8.5% improvement in the classification accuracy in different scenarios with statistical significance (p < 0.05) compared to its competitors. This study not only provides deeper insights into designing efficient NN models for EEG data but also lays groundwork for future explorations into the relationship between bioelectric brain signals and NN architectures. For the benefit of broader scientific collaboration, we have made all benchmark models, including EEGNeX, publicly available at (https://github.com/chenxiachan/EEGNeX).",
keywords = "Computer-Assisted, Decoding, Electroencephalography, Feature extraction, Machine learning, Representation learning, Signal Processing",
author = "Xia Chen and Xiangbin Teng and Han Chen and Yafeng Pan and Philipp Geyer",
year = "2024",
month = jan,
doi = "10.48550/arXiv.2207.12369",
language = "English",
volume = "87",
journal = "Biomedical Signal Processing and Control",
issn = "1746-8094",
publisher = "Elsevier BV",
}