The CPA-PA metric approximates ground-truth neural activity via CCA alignment and participant averaging, yielding 300-1000% better single-participant evaluations than conventional scores on synthetic and real MEEG data.
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
3 Pith papers cite this work. Polarity classification is still indexing.
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EEG functional connectivity via mutual information enables random forest regression to predict reaction times with RMSE of 23.75 ms immediately and 24.07 ms at 20-second lead times in a psychomotor vigilance test.
A modular EEG-based BCI with S4D deep learning classifier achieves 84% offline accuracy and enables real-time control for a tetraplegic user, with 73% success in post-competition validation.
citing papers explorer
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Robust Evaluation of Neural Encoding Models via ground-truth approximation
The CPA-PA metric approximates ground-truth neural activity via CCA alignment and participant averaging, yielding 300-1000% better single-participant evaluations than conventional scores on synthetic and real MEEG data.
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Fatigue-Related Reaction Time Forecasting via EEG Functional Connectivity in Sustained Attention Task
EEG functional connectivity via mutual information enables random forest regression to predict reaction times with RMSE of 23.75 ms immediately and 24.07 ms at 20-second lead times in a psychomotor vigilance test.
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Improving motor imagery decoding methods for an EEG-based mobile brain-computer interface in the context of the 2024 Cybathlon
A modular EEG-based BCI with S4D deep learning classifier achieves 84% offline accuracy and enables real-time control for a tetraplegic user, with 73% success in post-competition validation.