Emotional stress recognition system using EEG and psychophysiological signals

• Published in: 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) pp. 1 - 6
• Main Authors: Vyshali Rao, K P, Ashwini, H K, Akshatha, S
• Format: Conference Proceeding
• Language: English
• Published: IEEE 08-10-2021
• Subjects: Automation; Biological system modeling; Brain modeling; EEG Signals; Electroencephalography; Emotion recognition; Emotional Stress; Genetic Algorithm; Machine learning; psychophysiological signals; Support Vector Machine; Support vector machines; wavelet features
• DOI: 10.1109/ICAECA52838.2021.9675782

There are situations where Stress can affect a person on a larger scale. Early detection and analysis of such factors that lead to stress can be very helpful in ensuring good health of a person. Hence, we propose to use EEG Signals such that given a set of brain signal Dataset (DS), each of which is associated with electrical activity. E.g., thinking., the Emotional Stress Recognition using EEG and psychophysiological signals finds the emotion that is running inside a person from the given set of brain signals (e.g., a happy or sad), and covers a user-specified set of categories (e.g., positive or negative). An approach for the general case is to obtain the best signal from the group of signals and classify them as the emotion. We also propose to implement a feature that helps you find out if the person wants to communicate but is not able to express it verbally. Motivated by this, we propose a system which can convert the signals into understandable form. This can further help in knowing what the person desires to express in form of thoughts. This can confirm that the proposed solution is efficient than the existing model.