KACZMAR SPATIO Temporal Nelder Mead Multilayer Perceptrons for Stress Detection Using EEG Signals

KACZMAR SPATIO Temporal Nelder Mead Multilayer Perceptrons for Stress Detection Using EEG Signals

  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-5
Year of Publication : 2024
Author : Muhammadu Sathik Raja.M.S, Arun Raaza, Meena, Farida Virani
DOI : 10.14445/22315381/IJETT-V72I5P101

How to Cite?

Muhammadu Sathik Raja.M.S, Arun Raaza, Meena, Farida Virani, "KACZMAR SPATIO Temporal Nelder Mead Multilayer Perceptrons for Stress Detection Using EEG Signals," International Journal of Engineering Trends and Technology, vol. 72, no. 5, pp. 1-15, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I5P101

Abstract
Stress is an emotion that people encounter when they are extremely loaded and encounter trials and tribulations while carrying out day-to-day chores. Stress influences individual health seriously, like soaring blood pressure, heart disease, cardiovascular disease, and even lead to stroke. As a result, early stress detection becomes helpful to keep an eye on health-related issues caused by stress. Electro Encephalography (EEG) signal based system assists in identifying the different disorders and disabilities. Hence, there is a requirement for early stress detection using EEG signals that are accurate, precise, and reliable. This is resolved in the proposed method by introducing Kaczmar Spatio Temporal Nelder Mead Multilayer Perceptrons (KST-NMMP) that can accurately classify and detect the stress level. In this KST-NMMP method, deep learning using multilayer perceptrons is employed for early stress detection. It is split into four layers, i.e., one input layer, two hidden layers, and one output layer. The input EEG signals obtained from the subjects are provided in the input layer. Next, in the first hidden layer, the artifacts present in the raw EEG signals are filtered out; thus, the stress detection time can be reduced. After noise reduction, the spatial and temporal domain features are extracted from EEG signals; thus, stress detection overhead can be reduced significantly. Finally, stress level classification and detection at an early stage are performed in the second hidden layer employing spatial and temporal features using the Nelder Mead activation function. This proposed KST-NMMP method ensures accurate classification outcome which leads to improvement both in terms of precision and recall significantly. The overall implementation is performed in the Matlab programming language. Finally, the performance is evaluated and compared with the conventional method in terms of precision, recall, stress detection time, and stress detection overhead.

Keywords
Stress Detection, Electro Encephalo Graphy, Finite Impulse, Kernel Smoother, Kaczmarz Spatio Temporal, Nelder Mead, Deep Neural Activation.

References
[1] Ruiqi Fu et al., “Symmetric Convolutional and Adversarial Neural Network Enables Improved Mental Stress Classification from EEG,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 1384-1400, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Fabrizio Albertetti, Alena Simalastar, and Aicha Rizzotti-Kaddouri, “Stress Detection with Deep Learning Approaches Using Physiological Signals,” IoT Technologies for HealthCare Conference paper, vol. 360, pp. 95-111, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Ashlesha Akella et al., “Classifying Multi-Level Stress Responses from Brain Cortical EEG in Nurses and Non-Health Professionals using Machine Learning Auto Encoder,” IEEE Journal of Transactional Engineering in Health and Medicine, vol. 9, pp. 1-9, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Avik Sarkar, Ankita Singh, and Rakhi Chakraborty, “A Deep Learning-Based Comparative Study to Track Mental Depression from EEG Data,” Neuroscience Informatics, vol. 2, no. 4, pp. 1-6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Atefeh Safayari, and Hamidreza Bolhasani, “Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review,” Medicine in Novel Technology and Devices, vol. 12, pp. 1-16, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Pallavi Pandey, and K.R. Seeja, “Subject Independent Emotion Recognition from EEG Using VMD and Deep Learning,” Journal of King Saud University –Computer and Information Sciences, vol. 34, no. 5, pp. 1730-1738, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] S.K.B. Sangeetha et al., “A Deep Learning Approach to Detect Microsleep Using Various Forms of EEG Signal,” Mathematical Problems in Engineering, vol. 2023, pp. 1-8, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Nishtha Phutela et al., “Stress Classification Using Brain Signals Based on LSTM Network,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1-13, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Md. Rabiul Islam et al., “Emotion Recognition from EEG Signal Focusing on Deep Learning and Shallow Learning Techniques,” IEEE Access, vol. 9, pp. 94601-94624, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Avirath Sundaresan et al., “Evaluating Deep Learning EEG‑Based Mentalstress Classification in Adolescents with Autism for Breathing Entrainment BCI,” Brain Informatics, vol. 8, pp. 1-12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Ubaid M. Al-Saggaf et al., “Performance Evaluation of EEG Based Mental Stress Assessment Approaches for Wearable Devices,” Frontiers in Neurorobotics, vol. 15, pp. 1-8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Shruti Gedam, and Sanchita Paul, “A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques,” IEEE Access, vol. 9, pp. 84045-84066, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Ramesh Babu Vallabhaneni et al., “Deep Learning Algorithms in EEG Signal Decoding Application: A Review,” IEEE Access, vol. 9, pp. 125778-125786, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Essam H. Houssein, Asmaa Hammad, and Abdelmgeid A. Ali, “Human Emotion Recognition from EEG-Based Brain–Computer Interface using Machine Learning: A Comprehensive Review,” Neural Computing and Applications, vol. 34, pp. 12527-12557, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] G.S. Shashi Kumar, Niranjana Sampathila, and Tanishq Tanmay, “Wavelet Based Machine Learning Models for Classification of Human Emotions Using EEG Signal,” Measurement: Sensors, vol. 24, pp. 1-8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Beatriz García-Martínez et al., “Assessment of Dispersion Patterns for Negative Stress Detection from Electroencephalographic Signals,” Pattern Recognition, vol. 119, pp. 1-9, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] K. Saranya, and S. Jayanthy, “An Efficient AP-ANN-Based Multimethod Fusion Model to Detect Stress through EEG Signal Analysis,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1-18, 2022.
[CrossRef] [Google Scholar] [Publisher Link]