CRNN-Based Modeling of Mental Fatigue from Multimodal Signals
Volume: 14 - Issue: 06 - Date: 01-06-2025
Approved ISSN: 2278-1412
Published Id: IJAECESTU455 | Page No.: 101-107
Author: Salunke Apurvaa Annasaheb
Co- Author: Mr. Jeetendra Singh Yadav
Abstract:- Reliable, real-time detection of mental fatigue from EEG is critical in safety-sensitive settings. We
present LogMel-CRNN, a convolutional–recurrent framework that couples a one-dimensional convolution–
based Short-Time Fourier Transform (STFT) with a Mel-scale filter bank to produce log-Mel spectrograms
explicitly tailored to the spectral characteristics of fatigue. The convolutional front-end enhances frequency–
time resolution and noise tolerance, while the recurrent back-end captures temporal dependencies associated
with the gradual onset of fatigue. We evaluate multiple architectural variants and compare against
traditional machine-learning pipelines and contemporary deep baselines. Across experiments, LogMelCRNN consistently delivers superior classification performance, with notably high recall and F1 scores,
demonstrating robust sensitivity to fatigue-related EEG fluctuations. Ablation analyses indicate that the logMel transformation is pivotal—aligning feature emphasis with perceptual relevance—whereas the recurrent
component is essential for modeling temporal dynamics beyond frame-level cues. These findings establish
LogMel-CRNN as an accurate and resilient approach to EEG-based fatigue detection, with practical
potential for low-latency deployment in high-risk environments such as driver monitoring and industrial
operations.
Key Words:-EEG, mental fatigue, log-Mel spectrogram, CRNN, STFT, temporal modeling, real-time monitoring
Area:-Engineering
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