A Multimodal Machine Learning Framework for Class- Imbalanced Cognitive State Classification from High-Density EEG and Physiological Signals

DOI

https://www.doi.org/10.59256/indjcst.20250403036

Published In

Volume 4, Issue 3 (2025)

Published Online

September-December 2025

Pages

230-238

Article Type

Original Article

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The physiological data presented by classifying cognitive states in safety critical settings is difficult because of the harsh class imbalance of real-world events. Based on a large-scale multimodal database (N > 21M) of EEG, ECG, respiration and GSR, we designed a baseline machine learning pipeline, which utilized a Light Gradient Boosting Machine (LGBM) classifier. The model scored 93. 02% accuracy on average, although the model showed a critical failure to identify minority classes, and the recall scores were as low as 0.25. This illustrates the fact that standard accuracy is a highly deceiving indicator of this field. It is this benchmark that we utilize in establ ishing a required direction to come up with reliable systems and hence the urgency of the methods that directly tackle the issue of class imbalance in safety-based physiological monitoring.