Tracks & data.

EEG epoch · 32 ch Alljoined-1.6M

rank

Held-out gallery

DINOv2 embedding space

Train and test images do not overlap Top-5 acc ↑

Visual decoding is most useful when it recognizes novel content rather than a fixed catalogue learned during training. Given a single EEG epoch recorded during natural-image viewing, identify the viewed image by ranking held-out candidates in a frozen DINOv2 embedding space. Training and test images do not overlap, so the cross-stimulus shift probes whether neural representations transfer beyond memorized concepts. Research-grade and consumer-grade datasets add source and hardware variability while keeping the task anchored to time-locked visual responses.Metric: Top-5 retrieval accuracy against the held-out candidate set, with higher scores indicating better performance.Warm-up evaluation: THINGS-EEG2 test set · Start kit and baseline: THINGS-EEG2

A practical BCI must remain reliable as the same user returns on different days without requiring a new calibration each time. Decode one of three cued mental commands: kinesthetic motor imagery, mental calculation, or word association. The longitudinal corpus follows participants across six sessions and varied Graz and BrainHero contexts. Models train on earlier labeled sessions and predict later sessions. This within-user, cross-session design isolates drift from time of day, electrode replacement, physiology, and mental strategy while preserving subject-specific calibration.Metric: Balanced accuracy averaged across subject, session, and context cells, with higher scores indicating better performance.Warm-up evaluation and start kit: Dreyer 2023 test set · Published baseline: Stieger 2021Dataset release: New Graz and BrainHero data for training and warm-up evaluation are coming soon. Until then, Dreyer 2023 is the start-kit and warm-up proxy. Stieger 2021 remains the published baseline reference.

Reliable sleep-onset estimates from lightweight home headbands could make longitudinal sleep monitoring less dependent on full laboratory polysomnography. The task is to predict, at each point in a continuous four-channel home EEG recording, the number of seconds remaining until the first N2 epoch. Because recordings are collected in everyday settings, models must remain robust to night-to-night variability, motion artifacts, changes in electrode impedance, and occasional channel dropout. Evaluation uses new recordings from participants represented in training as well as recordings from completely unseen participants, testing robustness to both night-to-night and inter-person variability.Metric: Weighted binned mean absolute error (W-bMAE) in seconds, with greater weight on errors closer to sleep onset. Seen-subject and unseen-subject W-bMAE are macro-averaged for the final ranking. Lower scores indicate better performance.Warm-up evaluation: Sleep-EDF test set · Start kit and baseline: Sleep-EDFDataset release: New Muse data for training and warm-up evaluation are coming soon. Until then, Sleep-EDF is the proxy for both.

Translating wrist muscle activity into hand motion is a core challenge for wearable control systems that must work beyond a calibrated laboratory setup. Predict trajectories of 20 hand-joint angles from 16-channel wrist sEMG recorded during everyday movement. Evaluation includes users and movement stages never seen during training, plus unseen combinations of known users and stages. This tests robustness to anatomy, wristband placement, and kinematic context, and asks whether a daily wearable interface can transfer to a new user without person-specific retraining.Metric: Mean absolute angular error across the predicted joint trajectories, reported in degrees, with lower scores indicating better performance.Warm-up evaluation: EMG2Pose user-stage test set · Start kit and baseline: EMG2Pose