…learning (analogous to automatic speech recognition) - High-bandwidth neuromotor interfaces ## Dataset Details…
…for advancing cross-context emotion recognition and enhancing the real-world application…
EEG recordings from 30 healthy participants performing a left-hand grasping motor imagery task versus rest, cued by auditory stimuli, with simultaneous pupillometry. The dataset was recorded with a 32-channel BioSemi ActiveTwo system at 512 Hz across two runs of 25 trials per subject. It supports evaluating whether task-evoked pupil diameter changes can improve EEG-based motor imagery brain-computer interface classification.
This dataset contains 64-channel EEG recorded from 31 healthy participants performing a two-class (left vs. right hand) motor imagery task. Each participant completed up to three sessions of 40 trials. The recordings are part of a multimodal collection that also includes eye-tracking and high-speed video, intended for analyzing ocular activity across BCI paradigms. Events are annotated with HED, and the data are organized in BIDS format via MOABB.
This dataset contains magnetoencephalography (MEG) recordings from participants performing a cued recall task involving personally familiar people and places. It was collected to examine sustained neural representations of familiar people and places during recall. The data are organized in BIDS format.
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.
A large EEG database from 87 healthy participants recorded during a single-day motor imagery brain-computer interface (BCI) experiment. The data are organized into three datasets (A, B, C) that share one Graz-protocol task, left- versus right-hand motor imagery, with 240 trials per participant (over 20,800 trials in total). It also provides demographic, personality and cognitive-trait information, which supports studies of how user profiles relate to BCI performance and cross-user classification algorithms.
The Multimodal Resource for Studying Information Processing in the Developing Brain (MIPDB) provides high-density 128-channel EEG and eye-tracking recordings from a developmental cohort spanning childhood to adulthood. Participants completed a battery of cognitive and perceptual paradigms, including resting state, surround suppression, naturalistic viewing, contrast change detection, sequence learning, and symbol search. This BIDS conversion contains EEG data from 111 participants, with source eye-tracking and behavioural data preserved in sourcedata.
An EEG and behavioural dataset in which participants viewed images of scenes (stairs) and rated either their aesthetic properties (beauty, complexity, interestingness) or their appropriateness for a reading or social activity. The dataset investigates cortical responses to environmental affordances and the intention to act. It includes raw EEG, behavioural ratings extracted from EEG events, and the full stimulus set with identifiable file names.
Stereo-EEG (intracerebral) recordings from 28 patients with refractory epilepsy during fast periodic visual stimulation, with natural object images presented at 6 Hz and faces as every fifth image (1.2 Hz) in the periodic condition, or at random positions in a non-periodic control condition. The dataset is a derivative release of the authors' Letswave files, low-pass filtered at 30 Hz and segmented into sequences, repackaged in BIDS (BrainVision) format. It supports mapping of face-selective responses in the human ventral occipito-temporal cortex.