ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Brain Imaging Methods
High Accuracy EEG Signal Classification for Brain Computer Interfaces Using Advanced Neural Architectures
Provisionally accepted
Daicheng Lin1Qi Zhang2Huan Chen1Yanjie Lu3Haiting Chen4Lianfeng Li5
Abdulilah Mayet6*
Guodao Zhang3Xinjun Miao1Xianke Qiu1- 1Wenzhou Central Hospital, Wenzhou, China
- 2Wenzhou Medical University, Wenzhou, China
- 3Hangzhou Dianzi University, Hangzhou, China
- 4Wenzhou University of Technology, Wenzhou, China
- 5Jiangbin Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
- 6King Khalid University, Abha, Saudi Arabia
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This study proposes advanced neural network architectures for classifying specific motor-related electroencephalography (EEG) tasks, employing deep feature extraction techniques. We analyzed EEG data from the MILimbEEG dataset, consisting of recordings from 60 individuals as they performed eight distinct motor movements: baseline with eyes open, left-hand closing, right-hand closing, dorsiflexion and plantarflexion of both the left and right feet, as well as rest periods between tasks. For each of the 16 electrodes used in the recordings, 10 critical features were extracted, resulting in a comprehensive set of 160 features per sample that encapsulate the intricate brain activities associated with each task. A Group Method of Data Handling (GMDH) neural network, structured with eight hidden layers and a decremental arrangement of neurons from 40 in the first to 5 in the last, was utilized to classify these tasks. This network configuration achieved an impressive classification accuracy of approximately 96%, demonstrating a robust capability to accurately decode EEG signals tied to specific motor actions. The high precision achieved in this study underscores the efficacy of sophisticated computational models like the GMDH network in enhancing the interpretation of EEG signals for the development of brain-computer interfaces (BCIs). This research significantly advances the potential of EEG as a reliable modality for BCIs, effectively translating brain activity into actionable commands suitable for neurorehabilitation and assistive technologies. Our findings contribute substantially to the BCI field, promising to improve clinical outcomes by enabling more precise and effective interaction with neurorehabilitation devices.
Keywords: Brain-computer interface, EEG, feature extraction, GMDH neural networks, motor movement classification
Received: 28 Nov 2025; Accepted: 30 Jan 2026.
Copyright: © 2026 Lin, Zhang, Chen, Lu, Chen, Li, Mayet, Zhang, Miao and Qiu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Abdulilah Mayet
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