"Cognitive Load Estimation from Simulated EEG Using Band-Power Features and SVM Classification"
收藏DataCite Commons2025-09-06 更新2026-05-03 收录
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https://ieee-dataport.org/documents/cognitive-load-estimation-simulated-eeg-using-band-power-features-and-svm-classification
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资源简介：
"This work presents a transparent, low-complexity framework for cognitive load classification using synthetic EEG signals. We simulate multichannel EEG data representing low and high cognitive load states, extract frequency-domain features using Welch\u2019s method, and train a support vector machine (SVM) classifier. The proposed model achieves 100% accuracy, F1-score, and ROC AUC under moderate noise (\u03c3\u202f=\u202f0.5), with inference latency under 2\u202fms. Compared to deep learning approaches, our pipeline offers interpretability, rapid prototyping, and computational efficiency. All code, data, and reproducibility assets are publicly released."
提供机构：
IEEE DataPort
创建时间：
2025-09-06
