EEG Decoding Using CNN and LSTM Network
Motor imagery (MI) brain--computer interfaces (BCIs) have emerged as a promising approach for establishing flexible communication pathways between the human brain and external devices , particularly for individuals affected by stroke or neurodegenerative disorders. Reliable decoding of motor-imagery electroencephalography (MI-EEG) remains challenging because EEG recordings contain substantial noise and exhibit complex, weakly informative relationships with the underlying brain activity. Although deep learning provides an effective means of learning representations directly from EEG signals, its application to MI-EEG feature learning remains comparatively limited. This study introduces a h...
| Authors | Athanasios Karagounis |
|---|---|
| Primary category | cs.LG |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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