Stress Analysis using Feature Extraction Approach Using EEG Signal
Keywords:
Signal processing, Brain-Computer Interface (BCI), Electroencephalography (EEG), Stress detectionAbstract
Individuals experience stress in every society. Work-related concerns, disappointments, poor working conditions, etc., are prevalent worldwide sources of stress. Stress can be useful in the short term. However, chronic stress has serious consequences for health, including an increased risk of cardiovascular problems like heart disease, hypertension, and stroke. Mood and personality disorders including depression and anxiety are also possible outcomes. Therefore, the ability to recognise stress is useful for managing the health problems stress might cause. Stress can be measured and evaluated dependant on perceptual, behaviour and physiological reactions. Using feature extraction and classification methods, a few scholars have developed alternative approaches. It is based on that some of these procedures are intricate in their applicability and they produce less precise findings in human stress analysis. Therefore, a trustworthy and exact system is required. The goal of this study is to use Electroencephalography (EEG) signals to identify stress in real time, with the ultimate goal of creating a more accurate and trustworthy system. Stress can be reliably measured in a noninvasive manner with the help of EEG signals. To improve the accuracy of classification in stress detection, in this study, have been employed for feature extraction to extract significant time-frequency features. Accurate classification relies heavily on the selection of the best suitable feature extraction method. Equipment for acquiring EEG signals is used to validate the designed system.
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Muhammad Adeel Asghar, Muhammad Jamil Khan, Muhammad Rizwan, Mohammad Shorfuzzaman, Raja Majid Mehmood, "AI inspired EEG based spatial feature selection method using multivariate empirical mode decomposition for emotion classification", Multimedia Systems, vol.28, pp.1275–1288, 2022.
Tanya Nijhawan, Girija Attigeri and T. Anantha krishna, "Stress detection using natural language processing and machine learning over social interactions," Journal of Big Data, vol.9, pp.33, 2022.
Subhajit Chatterjee and Yung-Cheol Byun, "EEG-Based Emotion Classification Using Stacking Ensemble Approach," Sensors, vol.22, pp.8550, 2022.
Jinxiao Dai, Xugang Xi, Ge Li and Ting Wang, "EEG-Based Emotion Classification Using Improved Cross-Connected Convolutional Neural Network," Brain Sciences, vol.12, pp.977, 2022.
Sergio Muñoz, and Carlos A. Iglesias, "A text classification approach to detect psychological stress combining a lexicon-based feature framework with distributional representations," Information Processing & Management, vol.59, Issue 5, pp.103011, September 2022.
Lakhan Dev Sharma, Vijay Kumar Bohat, Maria Habib, Ala’ M. Al-Zoubi, Hossam Faris, and Ibrahim Aljarah, "Evolutionary inspired approach for mental stress detection using EEG signal," Expert Systems with Applications, Vol. 197, pp.116634, 1 July 2022.
Prima Dewi Purnamasari, Anak Agung Putri Ratna and Benyamin Kusumoputro, "Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks," Algorithms, vol.10, Issue.2, pp.63, 2017.
Bishwajit Roy, Lokesh Malviya, Radhikesh Kumar, Sandip Mal, Amrendra Kumar, Tanmay Bhowmik and Jong Wan Hu, "Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals," Diagnostics, vol.13, Issue.11, pp.1936, 2023.
J. -L. Wu, Y. He, L. -C. Yu and K. R. Lai, "Identifying Emotion Labels From Psychiatric Social Texts Using a Bi-Directional LSTM-CNN Model," IEEE Access, vol. 8, pp. 66638-66646, 2020.
W. C. de Melo, E. Granger and M. B. López, "MDN: A Deep Maximization-Differentiation Network for Spatio-Temporal Depression Detection," IEEE Transactions on Affective Computing, vol. 14, no. 1, pp. 578-590, 1 Jan.-March 2023.
Moon, S. N. and Bawane, N. (2015), ‘Optimal feature selection by genetic algorithm for classification using neural network’, International Research Journal of Engineering and Technology (IRJET) 2, 582–586.
Lakshmi, M. R., Prasad, T. and Prakash, D. V. C. (2014), ‘Survey on eeg signal processing methods’, International Journal of Advanced Research in Computer Science and Software Engineering 4(1), 84–91.
Guo, Lei, Youxi Wu, Lei Zhao, Ting Cao, Weili Yan, and Xueqin Shen. 2011. “Classification of Mental Task From EEG Signals Using Immune Feature Weighted Support Vector Machines” 47 (5): 866–69.
Khalid, S., Khalil, T. and Nasreen, S. (2014), A survey of feature selection and feature extraction techniques in machine learning, in ‘2014 Science and Information Conference’,IEEE, pp. 372–378.
Murthy, G. and Khan, Z. A. (2014), ‘Cognitive attention behaviour detection systems using electroencephalograph (eeg) signals’, Research Journal of Pharmacy and Technology 7(2), 238–247.
A. R. Subhani, w. Mumtaz, m. Naufal, b. I. N. Mohamed, n. Kamel, and a. S. Malik, “machine learning framework for the detection of mental stress at multiple levels,” ieee access, vol. 5, pp. 13545–13556, 2017.
Selma , “THE BRAIN-COMPUTER INTERFACE”, International Conference on Technics, Technologies and Education ICTTE 2019 October 16-18, 2019, Yambol, Bulgaria.
C. Lin, j. King, j. Fan, a. Appaji, and m. Prasad, “the influence of acute stress on brain dynamics during task switching activities,” pp. 3249–3255, 2018.
S. Koldijk and m. A. Neerincx, “detecting work stress in offices by combining unobtrusive sensors,” vol. 3045, no. C, 2016.
N. Sulaiman, s. Armiza, m. Aris, n. Hayatee, and u. T. Mara, “eeg-based stress features using spectral centroids technique and k-nearest neighbor classifier,” 2011.
Viegas, carla, and roy maxion. 2018. “towards independent stress detection: a dependent model using facial action units.” 2018 international conference on content-based multimedia indexing (cbmi), 1–6.
Woo, seong-woo. 2018. “classification of stress and non-stress condition using functional near-infrared spectroscopy.” 2018 18th international conference on control, automation and systems (iccas), no. Iccas: 1147–51.
Sulaiman, norizam, siti armiza, mohd aris, noor hayatee, and universiti teknologi mara. 2011. “eeg-based stress features using spectral centroids technique and k-nearest neighbor classifier.” Https://doi.org/10.1109/uksim.2011.23.
Dilbag Singh, “Human Emotion Recognition System”, I.J. Image, Graphics and Signal Processing, 2012, 8, 50-56.
Systems, c. (2018). Eeg-based stress detection system using human emotions, 10, 2360–2370.
Khorshidtalab, a. 2011. “eeg signal classification for real-time brain-computer interface applications : a review,” no. May: 17–19.
Nawasalkar, ram k. 2015. “eeg based stress recognition system based on indian classical music.”
Zheng Rahnuma, kazi shahzabeen, abdul wahab, norhaslinda kamaruddin, and hariyati majid. 2011. “eeg analysis for understanding stress based on affective model basis function,” 592–97.
Seyyed abed hosseini, mohammad ali khalilzadeh, and mohammad bagher naghibi-sistani. 2010. “emotional stress states,” 60–63. Https://doi.org/10.1109/itcs.2010.21.
Sulaiman, norizam, mohd nasir taib, sahrim lias, and zunairah hj murat. N.d. “novel methods for stress features identification using eeg signals,” 27–33, 2011. Https://doi.org/10.5013/ijssst.a.12.01.04.
Chaudhury, S., Dhabliya, D., Madan, S., Chakrabarti, S. Blockchain technology: A global provider of digital technology and services (2023) Building Secure Business Models Through Blockchain Technology: Tactics, Methods, Limitations, and Performance, pp. 168-193.
Soundararajan, R., Stanislaus, P.M., Ramasamy, S.G., Dhabliya, D., Deshpande, V., Sehar, S., Bavirisetti, D.P. Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks (2023) Energies, 16 (5), art. no. 2285,
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