Abhi Patel Presents Study on Efficiently Detecting and Measuring Mental Stress Using Recurrent Deep Learning

How do medical professionals accurately detect and measure emotional stress to understand its impact on a person's life? A paper presented at the recently held IEEE Applied Sensing Conference (IEEE Apscon 2023) at Bangalore proposed using electroencephalogram (EEG) and Deep Learning (DL) to efficiently detect stress with greater accuracy. Abhi Patel, BTech in Computer Science and Engineering, Class of 2023, was one among only five undergraduates who presented at the Conference. He presented his study on Mental Stress Detection Using EEG & Recurrent Deep Learning under the mentorship of Akhand Rai, Assistant Professor, School of Engineering and Applied Science. 

Abhi says, "Mental and emotional stress is a serious health issue that impairs a person's capacity to function daily. My research proposes a unique DL-based artificial intelligence (AI) technique that uses EEG data to build an emotional stress state detection model. This project involved signal processing first and then developed a DL model that requires fewer computation resources and results in higher accuracy. Additionally, we have incorporated this model as a tool so that the doctors may directly feed brain signals of the patient into it and obtain the stress value." 

IEEE Apscon 2023 witnessed participation from researchers and PhD Scholars from top Indian and global institutions.