Maestro: A Robust Multi-Head Attention Enhanced CNN Architecture for Heat-Induced Stress Recognition Using EEG Signals

Heat-induced stress impacts various physiological parameters in the body. Elevated temperature can cause tachycardia (an increase in heart rate), as the body attempts to dissipate heat through vasodilation, leading to dehydration and electrolyte imbalances. In addition, the hypothalamus triggers sweating in order to regulate body temperature; that can culminate in fluid and electrolyte loss, which could impact metabolic processes and blood pressure. A prolonged exposure to high temperatures can cause heat stroke, heat exhaustion, and other ailments including organ damage and systemic dysfunction. Existing electroencephalography (EEG)-based heat-induced stress detection often considers the entire EEG frequency range (delta to gamma), concealing redundant and lossy information and increasing the likelihood of false detection rates. To address the limitations of conventional handcrafted feature engineering approaches in heat stress detection, this paper introduces MAESTRO, a novel model comprising two blocks: Convolutional and Multi-head Attention. The Convolutional block extracts precise information from individual EEG frequency bands, while the Multi-head Attention block enhances feature representation through attention mechanism. Finally, two dense layers are employed to classify heat stress into three classes: Acute, Chronic, and Control. The proposed framework undergoes validation using EEG data obtained from 40 rodents in a simulated laboratory environment. The outcomes illustrate the viability of the method in classifying heat-induced stress, yielding remarkable results for overall accuracy, precision, recall, and F1 score of 98.88 %, 98.54 %, 98. 67 %, and 98.60 %, respectively.