A rigorous leave-one-subject-out benchmark on public auditory EEG data shows five-vowel decoding accuracy of 25.5 percent (chance 20 percent) using differential entropy features and LightGBM, with vowel information present but weak and localized to early auditory transients.
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG
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Unfolding Type-II Bayesian hyperparameter updates into a neural architecture with progressive correction mechanisms (biases to MLP to attention) improves reconstruction and convergence in brain source imaging while retaining algorithmic transparency.
Microstate tokenizer from clustered EEG signals provides universal representations that outperform traditional time- and frequency-domain features across sleep staging, emotion recognition, and motor imagery tasks.
A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.
AttDiCNN reaches 98.56%, 99.66%, and 99.08% accuracy on EDFX, HMC, and NCH sleep datasets via force-directed visibility graph EEG representations and a three-module attentive dilated CNN architecture.
citing papers explorer
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How Well Can We Decode Vowels from Auditory EEG -- A Rigorous Cross-Subject Benchmark with Honest Assessment
A rigorous leave-one-subject-out benchmark on public auditory EEG data shows five-vowel decoding accuracy of 25.5 percent (chance 20 percent) using differential entropy features and LightGBM, with vowel information present but weak and localized to early auditory transients.
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Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout
AttDiCNN reaches 98.56%, 99.66%, and 99.08% accuracy on EDFX, HMC, and NCH sleep datasets via force-directed visibility graph EEG representations and a three-module attentive dilated CNN architecture.