research-article

Exploring BCI Control in Smart Environments: Intention Recognition Via EEG Representation Enhancement Learning

Authors:

Xiaowei ZhaoAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 5

Article No.: 90, Pages 1 - 20

https://doi.org/10.1145/3450449

Published: 29 May 2021 Publication History

Abstract

The brain–computer interface (BCI) control technology that utilizes motor imagery to perform the desired action instead of manual operation will be widely used in smart environments. However, most of the research lacks robust feature representation of multi-channel EEG series, resulting in low intention recognition accuracy. This article proposes an EEG2Image based Denoised-ConvNets (called EID) to enhance feature representation of the intention recognition task. Specifically, we perform signal decomposition, slicing, and image mapping to decrease the noise from the irrelevant frequency bands. After that, we construct the Denoised-ConvNets structure to learn the colorspace and spatial variations of image objects without cropping new training images precisely. Toward further utilizing the color and spatial transformation layers, the colorspace and colored area of image objects have been enhanced and enlarged, respectively. In the multi-classification scenario, extensive experiments on publicly available EEG datasets confirm that the proposed method has better performance than state-of-the-art methods.

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Cited By

Alwi Alkaff ZHashimah Ahamed Hassain Malim NSumari PMalin Abdullah J(2024)Applications of Brain Computer Interface in Present Healthcare SettingNew Insights in Brain-Computer Interface Systems10.5772/intechopen.112353Online publication date: 11-Dec-2024
https://doi.org/10.5772/intechopen.112353
Sadeghi SMaleki A(2024)A Modified Hybrid Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potentials and ElectromyogramJournal of Integrative Neuroscience10.31083/j.jin230407323:4Online publication date: 7-Apr-2024
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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 5

October 2021

508 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3461317

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

Issue’s Table of Contents

Copyright © 2021 ACM.

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Association for Computing Machinery

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Publication History

Published: 29 May 2021

Accepted: 01 January 2021

Revised: 01 January 2021

Received: 01 April 2020

Published in TKDD Volume 15, Issue 5

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Funding Sources

National Natural Science Foundation of China (NSFC)
Outstanding Sino-foreign Youth Exchange Program of China Association for Science and Technology, and the Fundamental Research Funds for the Central Universities
RBWH IP-MAL Project
China Postdoctoral Science Foundation
UQ, RBWH and UTS, and the ARC Project

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Cited By

Alwi Alkaff ZHashimah Ahamed Hassain Malim NSumari PMalin Abdullah J(2024)Applications of Brain Computer Interface in Present Healthcare SettingNew Insights in Brain-Computer Interface Systems10.5772/intechopen.112353Online publication date: 11-Dec-2024
https://doi.org/10.5772/intechopen.112353
Sadeghi SMaleki A(2024)A Modified Hybrid Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potentials and ElectromyogramJournal of Integrative Neuroscience10.31083/j.jin230407323:4Online publication date: 7-Apr-2024
https://doi.org/10.31083/j.jin2304073
Zhou WJi JCui WWang YYi Y(2024)Unsupervised Domain Adaptation Fundus Image Segmentation via Multi-Scale Adaptive Adversarial LearningIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.334242228:10(5792-5803)Online publication date: Oct-2024
https://doi.org/10.1109/JBHI.2023.3342422
Zhu HHieu NHoang DNguyen DLin C(2024)A Human-Centric Metaverse Enabled by Brain-Computer Interface: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2024.338712426:3(2120-2145)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/COMST.2024.3387124
Saibene AGhaemi HDagdevir E(2024)Deep learning in motor imagery EEG signal decoding: A Systematic ReviewNeurocomputing10.1016/j.neucom.2024.128577610(128577)Online publication date: Dec-2024
https://doi.org/10.1016/j.neucom.2024.128577
Bian JDai CLi G(2024)Adaptive channel-weight dual-constrained semi-supervised EEG clusteringBiomedical Signal Processing and Control10.1016/j.bspc.2024.10672098(106720)Online publication date: Dec-2024
https://doi.org/10.1016/j.bspc.2024.106720
Al-qaysi ZAlbahri AAhmed MSalih M(2024)Dynamic decision-making framework for benchmarking brain–computer interface applications: a fuzzy-weighted zero-inconsistency method for consistent weights and VIKOR for stable rankNeural Computing and Applications10.1007/s00521-024-09605-136:17(10355-10378)Online publication date: 16-Mar-2024
https://dl.acm.org/doi/10.1007/s00521-024-09605-1
Mustafa YElmahallawy MLuo TEldawlatly S(2023)A Brain-Computer Interface Augmented Reality Framework with Auto-Adaptive SSVEP Recognition2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)10.1109/MetroXRAINE58569.2023.10405810(799-804)Online publication date: 25-Oct-2023
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Lee PJeon SHwang SShin MByun H(2023)Source-free Subject Adaptation for EEG-based Visual Recognition2023 11th International Winter Conference on Brain-Computer Interface (BCI)10.1109/BCI57258.2023.10078570(1-6)Online publication date: 20-Feb-2023
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Sun XGong LHan ZZhao PYu JWang S(2022)Neural Metric Factorization for RecommendationMathematics10.3390/math1003050310:3(503)Online publication date: 4-Feb-2022
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