Deep Learning-Based Classification of Cognitive and Resting States Using Electroencephalography Signals

arXiv:2609.20467 · cs.LG, cs.AI · Submitted 2026-09-17 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-17

Updated: 2026-09-17

Comments: 16 pages, 19 figures, 7 tables, and 1 algorithm

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Feature Extraction — 47× in this paper · explained in 2 episodes
• Accuracy — 32× in this paper · explained in 1 episode
• Convolutional Neural Network — 7× in this paper
• alpha — 7× in this paper · explained in 1 episode
• 1D CNN — 6× in this paper · explained in 2 episodes
• Gated Recurrent Unit — 6× in this paper
• Wavelet Transform — 5× in this paper · explained in 2 episodes
• Discrete Wavelet Transform — 4× in this paper
• Independent Component Analysis — 4× in this paper
• Short-Time Fourier Transform — 4× in this paper
• Transformer — 4× in this paper · explained in 2 episodes
• Cognitive Load — 3× in this paper · explained in 2 episodes
• Continual Learning — 3× in this paper · explained in 2 episodes
• Decision Tree — 3× in this paper
• Fast Fourier Transform — 3× in this paper
• Adam — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Feature Selection — 1× in this paper · explained in 4 episodes
• Frequency-Domain Analysis — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes

Sources

• Mental Task Classification Using Electroencephalogram Signal

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