Decoding Working Memory Load from EEG and Behavioral Dynamics

Working memory (WM) is critical for cognitive tasks such as reasoning and problem-solving, and its performance is heavily influenced by cognitive load. This study explores the use of EEG, combined with Common Spatial Pattern (CSP) and Support Vector Machine (SVM) classification, to predict cog- nitive states under different memory load conditions (easy, medium, and hard). Behavioral performance, including accuracy and reaction time (RT), was assessed across memory load levels, with RT increasing and accuracy decreasing as memory load became more difficult. EEG data were preprocessed to isolate probe-related activity, and CSP was employed for spatial feature extraction, followed by SVM classifica- tion to decode cognitive states. Our results demonstrated robust binary and multi-class classification per- formance, with average test accuracies consistently exceeding 80% across subjects. Notably, the model achieved high accuracy in distinguishing between memory load conditions, with minimal misclassifi- cations and strong consistency between test and cross-validation results. These findings highlight the potential of the CSP-SVM framework for real-time cognitive state monitoring, offering a non-invasive method for tracking cognitive load in both healthy and clinical populations. Future work will explore further applications in neuroadaptive technologies and cognitive impairment monitoring.