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Mental Workload Classification using Bidirectional LSTM Networks with Multi-Feature Fusion

Authors :

Fatemeh Farokhshad¹ Sepideh Bahri Hampa² Amirhesam Ghasri³ Sara Bagherzadeh⁴

1- Islamic azad University, Dezful Branch 2- Department of Biological Sciences, Tarbiat Modares University, Tehran, Iran 3- Department of Biological Sciences, Tarbiat Modares University, Tehran, Iran 4- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

Keywords :

Mental workload،EEG،BiLSTM،Deep learning،Feature extraction،Temporal classification

Abstract :

Mental workload assessment is crucial for optimizing human performance in various domains including brain-computer interfaces (BCI) and cognitive load monitoring systems. This paper presents a novel approach for mental workload classification using Bidirectional Long Short-Term Memory (BiLSTM) networks with comprehensive feature extraction from EEG signals. Our method incorporates multiple feature types including spectral power analysis, entropy measures, and Hjorth parameters to create rich temporal sequences for classification. We evaluate two BiLSTM architectures - basic and advanced - on the STEW dataset containing high and low mental workload conditions from 48 subjects. The advanced BiLSTM model achieves exceptional performance with 99.97% 5-fold accuracy and 99.84% test accuracy, demonstrating the effectiveness of temporal sequence modeling for mental workload classification. The proposed approach shows significant improvements over traditional machine learning methods and provides robust classification suitable for real-time applications.