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June 20, 2026Computer Methods in Biomechanics and Biomedical Engineering Imaging & VisualizationOpen Access

From perception to imagination: a robust deep learning architecture for real-time EEG decoding and neurophysiological validation

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Authors

YGYu GaoJDJosé Miguel Diniz

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Overview

Randomized trial evaluates real-time EEG decoding efficacy in subjects, suggesting improved BCI usability.

Key Points

  • This research aims to overcome BCI clinical challenges by enhancing EEG decoding accuracy and reducing latency.
  • Developed CSOANet2026, a lightweight CNN model with FIR/ICA preprocessing.
  • Evaluated performance on 46 subjects with accuracy metrics and latency measurements.
  • Used Grad-CAM and gate-weight analyses to validate neurophysiological relevance.
  • Achieved 98.67% ± 2.46% within-subject accuracy and 96.73% ± 6.31% LOSO accuracy.
  • Real-time processing with latencies of 3.99 ± 0.60 ms (CPU) and 0.81 ± 0.16 ms (GPU) per trial.
  • Demonstrated reliance on occipital Alpha-band modulation during perception and imagery.

Cite This Study

Gao et al. (2026) studied this question.

synapsesocial.com/papers/6a363123db0793dc1a538239https://doi.org/10.1080/21681163.2026.2684109
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Also Consider

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  4. 4Subject-Adaptive EEG Decoding via Filter-Bank Neural Architecture Search for BCI Applications2026
  5. 5Integrated EEG acquisition and modeling system for neurofunctional biomarkers of covert motor intent2026