One Transformer Reads Three Brain-Signal Types at Once, Hitting 71% Accuracy
A single Transformer-based decoder trained on the OpenBMI dataset classified eight EEG classes spanning motor imagery, P300, and SSVEP paradigms ...
A single Transformer-based decoder trained on the OpenBMI dataset classified eight EEG classes spanning motor imagery, P300, and SSVEP paradigms ...
A new review finds that safety evidence for implanted brain-computer interfaces remains fragmented across small, hard-to-compare studies, and proposes a ...
A systematic review of 24 studies finds that in-ear EEG devices can monitor sleep, seizures, workload and emotions outside the ...
A network meta-analysis of sixteen randomized trials finds brain-computer interface-triggered functional electrical stimulation shows a clearer motor recovery signal after ...
A new hypothesis paper proposes treating access to high-performance cognitive states as a closed-loop engineering problem, specifying a five-layer architecture ...
Researchers have built a deep learning network combining multiscale convolutional filtering with temporal and channel attention that outperformed five leading ...
A systematic review and meta-analysis of 11 randomized controlled trials involving 559 patients with stroke found that brain-computer interface training ...
A new systematic review and meta-analysis finds that adding non-invasive brain stimulation to brain-computer interface training does not clearly improve ...
Researchers used SHAP-based explainable AI to identify a single EEG electrode and just seven features that detect virtual reality motion ...
A new communication framework called PQ-NeuroLink brings post-quantum cryptography to distributed EEG brain-computer interfaces while adding less than half a ...
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© 2025 Scienmag - Science Magazine