100 results for "motor imagery classification" · page 7 of 10 · ranked by relevance
…categories through single-trial EEG classification and representational similarity analysis. ## Dataset Summary…
…training participants): - Offline CER: >90% classification accuracy on held-out participants - Online…
…Motor execution (motorFoot/motorRHand/motorLHand) — 20 trials each Run 04: Transient sensory…
…learning approaches to imagined speech classification. ## Overview of the experiment Participants performed…
…Unlike classification-based motor imagery datasets, this dataset contains continuous trajectory labels…
…unsupervised, and easy-to-implement classification approach for ERP-based BCIs. The…
…from moabb.paradigms import MotorImagery paradigm = MotorImagery() dataset = Nguyen2017_V() X, y…
…motor_imagery - **Imagery tasks**: dog, bird, fish, pentagram, square, circle, scissor, watch…
THINGS-EEG2 is a large-scale EEG dataset comprising recordings from 10 subjects viewing 16,540 distinct training images and 200 test images presented via rapid serial visual presentation at 5 Hz. The dataset includes 63-channel EEG data sampled at 1000 Hz across 4 sessions per subject, with approximately 32,540 training trials and 16,000 test trials, designed to support computational modeling of human visual object recognition. Stimuli are drawn from the THINGS database, and the dataset includes resting-state recordings and behavioral annotations for each trial.