Multimodal Brain-Computer Interface Grand Challenge

This competition focuses on EEG-fNIRS based imagined handwriting trajectory classification. Participants are required to classify each trial into one of four logographic character classes using synchronized EEG and fNIRS recordings. The input data include EEG signals, fNIRS signals, trial IDs, and metadata provided by the organizers. The goal is to develop robust multimodal brain-computer interface models that can effectively integrate neural electrical activity and hemodynamic responses for imagined handwriting recognition. Submissions will be evaluated based on classification performance on the hidden test set. Each submitted file should contain one predicted label for each test trial.