Deep Learning-Based Assessment of Stress Levels Using EEG Signals

Stress, as an inherent physiological and psychological response to perceived challenges, induces the secretion of hormones such as adrenaline and cortisol. This response readies the body for immediate action through the “fight or flight” reaction. Stress manifests in diverse mental and emotional responses, varying among individuals and situations. The evolutionary purpose of stress lies in its adaptive function for heightened survival, preparing the body to confront imminent threats. Deep learning, a subdivision of machine learning characterized by neural networks featuring multiple layers, replicates the intricate structure of the human brain. This replication facilitates intricate decision-making based on data and has demonstrated significant success in tasks such as image and speech recognition, natural language processing, and strategic gaming. In our ongoing project, we are employing concepts from deep learning to scrutinize Electroencephalogram (EEG) signals. EEG signals, imperative for the examination of brain function, cognitive processes, and neurological disorders, represent recordings of the brain’s electrical activity measured via electrodes on the scalp. The integration of techniques from deep learning in the scrutiny of EEG signals holds potential applications in brain-computer interfaces, comprehension of cognitive processes, and the diagnosis of neurological conditions. The association between stress and observable impacts on EEG signals underscores the significance of our project. We are implementing this project using LSTM algorithm. The architecture widely employed in Deep Learning for sequence prediction tasks is the Long Short-Term Memory (LSTM) recurrent neural network. Renowned for its proficiency in capturing long-term dependencies, LSTM stands out by incorporating feedback connections, enabling it to process complete sequences of data rather than individual data points. This characteristic renders it highly adept at discerning and forecasting patterns within sequential data domains such as time series, text, and speech.