…Additionally, we provide embeddings of the Chinese text materials encoded from BERT…
…to large language model (LLM) embeddings. We also include tutorials replicating previous…
…Text embeddings computed by different pre-trained language models (including Word2Vec, BERT…
A BIDS conversion of a multicenter intracranial EEG (iEEG) dataset of 3-second single-channel clips for classifying graphoelements (physiological and pathological activity) and artifactual signals. Clips were recorded from stereo-EEG contacts at 5000 Hz and concatenated into runs per subject and contact. Events preserve the original labels, segment IDs, anatomy, reviewer IDs, seizure onset zone flags, and electrode types.
…per epoch ### Corresponding Feature Embeddings Feature (embeddings) are stored in: `./derivatives/Features…
This dataset contains independent 3-second single-channel intracranial EEG (stereo-EEG) clips from St. Anne's University Hospital (FNUSA), annotated for classification of physiological and pathological graphoelements and artifactual signals. It is a BIDS conversion of the segmented .mat archives released with Nejedly et al. 2020. Clips from one subject and one SEEG contact are concatenated per run at 5000 Hz, with events preserving labels, anatomy, reviewer, seizure onset zone flag, and electrode type.
Intracranial EEG and behavioral event data from a paired associates learning task in which participants studied pairs of visually presented words, solved arithmetic distractor problems, and then performed cued recall. Data were collected from clinical sites in collaboration with the Computational Memory Lab at the University of Pennsylvania. Recordings include monopolar and bipolar iEEG with subject-specific electrode montages, MNI/Talairach coordinates, and brain region annotations.
[ in response to auditory and visual cues. The dataset was re-packaged in BIDS format from the MOABB collection and supports decoding of imagined speech with Riemannian manifold features and relevance vector machine classification.
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