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June 19, 2026ComputersOpen Access

EEGNet achieves a top F1-score of 0.81 for classifying motor activities in stroke patients.

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Population

8 stroke patients

Design

Other

Key result

EEGNet achieved the highest descriptive average F1-score of 0.810 for classifying normal and abnormal motor activities in stroke patients.

Authors

SKSarunya KanjanawattanaISIsaman SangbamrungDWDulyawat Wiriyaphong

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Overview

Should not yet guide stroke rehabilitation decisions; leaves open EEGNet utility for gait classification pending larger prospective validation.

Key Points

  • This study evaluates deep learning methods to classify normal and abnormal gait activities in stroke patients using EEG data.
  • Used EEG signals from eight stroke patients to train and evaluate models: DeepConvNet and EEGNet.
  • Investigated different channel configurations (32, 22, 15 channels) for optimal performance.
  • Employing Leave-One-Out Cross-Validation (LOOCV) for model evaluation with seven patients.
  • EEGNet achieved an average F1-score of 0.810 and an F1-score of 0.915 on an unseen patient.
  • Exhibited a low false positive rate of 0.175, reducing false alarms.
  • The 32-channel setup demonstrated highest consistency in gait classification.

Structured PICO

P
Population
8 stroke patients evaluated using multi-class deep learning frameworks for classifying normal and abnormal motor activities using EEG data.
E
Exposure
Multi-class deep learning frameworks (customized Convolutional Neural Network [CNN], DeepConvNet, and EEGNet) using EEG data with varying channel reduction configurations (32, 22, and 15 channels)
O
Outcome
Classification performance of eight distinct normal and abnormal motor activities (measured by F1-score and false positive rate)

EEGNet demonstrated superior performance in differentiating complex gait patterns from EEG signals in stroke patients, highlighting its potential for real-time, non-invasive monitoring in neurorehabilitation.

Limitations

  • limited cohort

Cite This Study

Kanjanawattana et al. (2026) studied Stroke (n=8). EEGNet vs. Customized CNN and DeepConvNet was evaluated on Classification of eight distinct normal and abnormal motor activities (F1-score). EEGNet achieved the highest descriptive average F1-score of 0.810 for classifying normal and abnormal motor activities in stroke patients.

synapsesocial.com/papers/6a35982fdd3be7785e70ecd8https://doi.org/10.3390/computers15060392
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  5. 5EEG-based motor execution classification of upper and lower extremities using machine learning2025