Stress Detection with Machine Learning and Deep Learning Using Multi-Model Physiological Data

Authors: Sandip Sahane, Vishal Nilwarn, Harshad Gholap, Amol Tile, Prof. Mr.M.S.Khan

Country: India

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Abstract: The project scope of the project involves the analysis of offline EEG data to develop a machine learning model for the detection of anxiety and depression. The project will encompass data preprocessing, feature extraction, model development, ethical considerations, and reporting of findings. The project aims to develop a machine learning-based system that analyzes brainwave signals, specifically (EEG) data, to identify patterns and biomarkers associated with anxiety and depression. The system's primary objective is to provide an objective and quantifiable assessment of mental health status, leading to early detection and intervention.

Keywords: Stress Detection using Machine Learing And Deep Learning

Paper Id: 230604

Published On: 2024-04-25

Published In: Volume 12, Issue 2, March-April 2024

Cite This: Stress Detection with Machine Learning and Deep Learning Using Multi-Model Physiological Data - Sandip Sahane, Vishal Nilwarn, Harshad Gholap, Amol Tile, Prof. Mr.M.S.Khan - IJIRMPS Volume 12, Issue 2, March-April 2024.

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