Decoding motor imagery EEG signals by a deep learning network integrating multiscale filtering and temporal-channel attention mechanisms.

Overview

Authors: Yong Zhu¹, Yongge Shi², Jianhua Wu³, Qingguo Wei³

Department of Information Engineering, Gongqing College of Nanchang University, Jiujiang, China
School of Information Engineering, Jiujiang Polytechnic University of Science and Technology, Jiujiang, China
School of Information Engineering, Nanchang University, Nanchang, China

Institutions: Gongqing College of Nanchang University (China); Jiujiang Polytechnic University of Science and Technology (China); Nanchang University (China)

Journal: PloS one, volume 21, issue 10, article e0361026

Dates: received 28 May 2026; accepted 5 October 2026; published online 23 October 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pone.0361026 · PMID 42912743 · PMCID PMC13693885 · OpenAlex W7223382543

Open access: gold, a free copy (OpenAlex)

Status: data only (how the code is found and verified)

Categories: EEG (modality), human (organism), cellular / molecular (subfield)

Methods: Preprocessing, Machine learning

MeSH: Brain-Computer Interfaces*, Deep Learning*, Electroencephalography*, Convolutional Neural Networks, Humans, Signal Processing, Computer-Assisted (* major topic)

Journal subjects: Research and Analysis Methods, Bioassays and Physiological Analysis, Electrophysiological Techniques, Brain Electrophysiology, Electroencephalography, Biology and Life Sciences, Physiology, Electrophysiology, Neurophysiology, Neuroscience, Brain Mapping, Medicine and Health Sciences, Clinical Medicine, Clinical Neurophysiology, Imaging Techniques, Neuroimaging, Computer and Information Sciences, Network Analysis, Signaling Networks, Engineering and Technology, Signal Processing, Signal Filtering, Mathematical and Statistical Techniques, Mathematical Functions, Convolution, Electronics Engineering, Computer Engineering, Man-Computer Interface, Speech Signal Processing, Cognitive Science, Cognitive Psychology, Attention, Psychology, Social Sciences, Neural Networks

Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: National Natural Science Foundation of China (62066028)

Citations: not cited yet (Europe PMC); 55 references in the paper

Abstract

Due to their low signal-to-noise ratio and non-stationarity, accurately classifying EEG signals is still one of the main hindrances that limit the real-world applications of a brain-computer interface (BCI). Deep learning (DL) is a potential method for addressing the problem. However, how to create a suitable DL network for motor imagery (MI)-based BCIs poses severe challenges in the case of insufficient training data. In this paper, a DL network based on multiscale convolutional neural network (CNN) and temporal and channel attention mechanisms is proposed for decoding MI tasks. Temporal and spatial features are extracted by four parallel CNN branches, each of which is followed by a Transformer block for learning the global correlations of local time features. The outputs from the four Transformer blocks are concatenated along channel dimension and then input into a channel attention module for channel fusion. The subsequent feature map is classified by a CNN, which replaces the commonly used fully connected layer for reducing computational complexity. The proposed network was evaluated on three publicly available BCI datasets and compared with five state-of-the-art DL networks. The training data sets are augmented using the approach segmentation and reconstruction. The experimental results demonstrate that the proposed network achieved an average accuracy of 80.21% on Dataset 1 (4-class tasks), 90.19% on Dataset 2 (2-class tasks), and 97.32% on Dataset 3 (4-class tasks), surpassing the strongest baselines FBCNet on Dataset 1, MSFBCNN on Dataset 2 and MSFBCNN on Dataset 3 by 2.59%, 1.27%, and 1.99% respectively. The network effectively decoded the MI-based EEG signals, providing a potential method for BCI applications.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data and to no code of its authors: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

bbci.de/competition/iv, at bbci.de; found in “Data Availability”
gin.g-node.org/robintibor/high-gamma-dataset, at gin.g-node.org; found in the references

Data Availability

No data was generated by this study. The three publicly available datasets used in the study can be downloaded from the following two websites: http://www.bbci.de/competition/iv/, https://gin.g-node.org/robintibor/high-gamma-dataset/src/master/data/.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 25 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 MeSH terms, 1 funder, 52 references.

Cite

This paper

Zhu, Y., Shi, Y., Wu, J., & Wei, Q. (2026). Decoding motor imagery EEG signals by a deep learning network integrating multiscale filtering and temporal-channel attention mechanisms. PloS one, 21(10), e0361026. https://doi.org/10.1371/journal.pone.0361026

BibTeX

@article{zhu2026decoding, author = {Zhu, Yong and Shi, Yongge and Wu, Jianhua and Wei, Qingguo}, title = {{Decoding motor imagery EEG signals by a deep learning network integrating multiscale filtering and temporal-channel attention mechanisms}}, journal = {PloS one}, year = {2026}, month = oct, volume = {21}, number = {10}, pages = {e0361026}, publisher = {PLOS}, issn = {1932-6203}, doi = {10.1371/journal.pone.0361026}, url = {https://doi.org/10.1371/journal.pone.0361026}, pmid = {42912743}, pmcid = {PMC13693885} }

RIS

TY - JOUR AU - Zhu, Yong AU - Shi, Yongge AU - Wu, Jianhua AU - Wei, Qingguo TI - Decoding motor imagery EEG signals by a deep learning network integrating multiscale filtering and temporal-channel attention mechanisms T2 - PloS one J2 - PLoS One PY - 2026 DA - 2026/10/23 VL - 21 IS - 10 SP - e0361026 SN - 1932-6203 PB - PLOS DO - 10.1371/journal.pone.0361026 UR - https://doi.org/10.1371/journal.pone.0361026 LA - en ER -

CSL-JSON

{ "id": "10.1371/journal.pone.0361026", "type": "article-journal", "title": "Decoding motor imagery EEG signals by a deep learning network integrating multiscale filtering and temporal-channel attention mechanisms", "container-title": "PloS one", "author": [ { "family": "Zhu", "given": "Yong" }, { "family": "Shi", "given": "Yongge" }, { "family": "Wu", "given": "Jianhua" }, { "family": "Wei", "given": "Qingguo" } ], "container-title-short": "PLoS One", "volume": "21", "issue": "10", "page": "e0361026", "DOI": "10.1371/journal.pone.0361026", "PMID": "42912743", "PMCID": "PMC13693885", "ISSN": "1932-6203", "publisher": "PLOS", "URL": "https://doi.org/10.1371/journal.pone.0361026", "language": "en", "issued": { "date-parts": [ [ 2026, 10, 23 ] ] } }

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.