Beijing University of Technology

Researchers from Beijing University of Technology and collaborating institutions proposed REFS to select EEG features for multi-dimensional emotion recognition when some emotional labels are missing. The method reconstructs incomplete labels while reducing redundancy among selected features, addressing modeling challenges posed by high-dimensional features and relatively few high-quality samples. Tests used DREAMER, DEAP and HDED, three datasets with different EEG channel configurations, at varying label-missing ratios. REFS consistently outperformed the 13 advanced feature-selection methods compared in robustness under these conditions. These dataset findings do not establish performance in deployed affective BCIs.

The study recorded hippocampal CA1 population activity in rats performing a sequential spatial decision task in a modified T-maze, dividing the decision into initiation, running and approach phases. Local theta sequences consistently over-represented the actual choice, while prospective representations driven by choice-arm place cells shifted from predicting the actual choice during running to representing potential paths more evenly at the choice point. Integrating local and prospective features improved decoding, reaching 74.4% accuracy for future choice prediction and 78.2% for upcoming trajectory decoding.