Method and Algorithm of Electroencephalographic Signals Processing in Computer Medical Diagnostic Systems for Human Psychoemotional Indicators Detection
DOI:
https://doi.org/10.20535/RADAP.2023.91.63-71Keywords:
EEG signal, psychoemotional load, detection, psychoemotional indicators, periodically correlated random process, synphase method, informative, computer medical diagnostic system, softwareAbstract
The method and algorithm for electroencephalographic signals processing during psychoemotional stress are developed to increase the informativeness of computer medical diagnostic systems in order to detect temporal transitions between various psychoemotional states in people. The method and algorithm for electroencephalographic signals processing is based on a mathematical model in the form of a periodically correlated random process and the synphase processing method without taking into account the relationship between correlation components as psycho-emotional indicators of a human. Such the model and method provide detection of the appearance of changes in the temporal structure of the electroencephalographic signal based on the data of changes in the periodic component in the form of averaged correlation components obtained within time-shift windows, which quantitatively reflect psycho-emotional changes in a humanin stressful situations.
It is found that during the period of background exposure, there is a gradual decrease in the power level of the averaged correlation components of the electroencephalographic signal, during the period of negative influence, there is an increase in power, and during the recovery period, there is a decrease in the power of the components in relation to the two previous impacts.
Software are developed based on the synphase method for electroencephalographic signals processing during psycho-emotional stress in the Matlab software environment.
References
References
Frantzidis C. A., Bratsas C., Papadelis C. L., Konstantinidis E., Pappas C. and Bamidis P. D. (2010). Toward Emotion Aware Computing: An Integrated Approach Using Multichannel Neurophysiological Recordings and Affective Visual Stimuli. IEEE Transactions on Information Technology in Biomedicine, Vol. 14, No. 3, pp. 589-597. doi: 10.1109/TITB.2010.2041553.
Melnikova T. S., Krasnov V. N., Lapin I. A., Andrushkyavichus S. I. (2009). Dnevnaya dinamika harakteristik EEG pri cirkulyarnyh depressivnyh rasstrojstvah [Daily dynamics of EEG characteristics in cyclic depressive disorders]. Psychic health, Vol. 12, pp. 43-47.
Lapshina T. N. (2004). EEG-indikaciya emocional'nyh sostoyanij cheloveka [EEG-indication of emotional states of a person]. Herald of Moscow State University. Psychology, Vol. 2, pp. 101–102.
Shpenkov O., Tukaev S., Zyma I. (2018). EEG gamma-band spectral power changes during listening to the rock-music with reduced low-frequency level. Visnyk Taras Shevchenko National University of Kyiv. Biology, 2018, Vol. 1(75), pp. 27-32.
Klibi S., Mestiri M. and Farah I. R. (2021). Emotional behavior analysis based on EEG signal processing using Machine Learning: A case study. 2021 International Congress of Advanced Technology and Engineering (ICOTEN), Taiz, Yemen, pp. 262-265. DOI:10.1109/ICOTEN52080.2021.9493537.
Qing C., Qiao R., Xiangmin X., Cheng Y. (2019). Interpretable Emotion Recognition Using EEG Signals. IEEE Access, Vol. 7, pp. 94160–94170. DOI:10.1109/ACCESS.2019.2928691.
Sharma R., Pachori R. B., Sircar P. (2020). Automated emotion recognition based on higher order statistics and deep learning algorithm. Biomedical Signal Processing and Control, Vol. 58, 101867. DOI:10.1016/j.bspc.2020.101867.
Topic A., Russo M. (2021). Emotion recognition based on EEG feature maps through deep learning network. Engineering Science and Technology, an International Journal, Vol. 24, Iss. 6, pp. 1442-1454. DOI:10.1016/j.jestch.2021.03.012.
Sakalle A., Tomar P., Bhardwaj H., Acharya D., Bhardwaj A. (2021). A LSTM based deep learning network for recognizing emotions using wireless brainwave driven system. Expert Systems with Applications, Vol. 173, 114516. DOI:10.1016/j.eswa.2020.114516.
Zheng W.-L., Zhu J.-Y., Lu B.-L. (2017). Identifying Stable Patterns over Time for Emotion Recognition from EEG. IEEE Transactions on Affective Computing, Vol. 10, Iss. 3, pp. 417–429. DOI:10.1109/TAFFC.2017.2712143.
Song T., Zheng W., Song P., Cui Z. (2018). EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks. IEEE Transactions on Affective Computing, Vol. 11, Iss. 3, pp. 532–541. DOI: 10.1109/TAFFC.2018.2817622.
Liu S., Wang X., Zhao L., Zhao J., Xin Q., Wang S. (2020). Subject-Independent Emotion Recognition of EEG Signals Based on Dynamic Empirical Convolutional Neural Network. IEEE/ACM Trans Comput Biol Bioinform, Vol. 18, Iss. 5, pp. 1710-1721. DOI: 10.1109/TCBB.2020.3018137.
Gupta R., Laghari K. R., Falk T. H. (2016). Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization. Neurocomputing, Vol. 174, Part B, pp. 875–884. DOI:10.1016/j.neucom.2015.09.085.
Yin Z., Liu L., Chen J., Zhao B., Wang Y. (2020). Locally robust EEG feature selection for individual-independent emotion recognition. Expert Systems with Applications, Vol. 162, 11376. DOI:10.1016/j.eswa.2020.113768.
Joshi V. M., Ghongade R. B. (2020). IDEA: Intellect database for emotion analysis using EEG signal. Journal of King Saud University - Computer and Information Sciences, Vol. 34, Iss. 7, pp. 4433-4447. DOI:10.1016/j.jksuci.2020.10.007.
Chakladar D. D., Chakraborty S. (2018). EEG based emotion classification using «Correlation Based Subset Selection». Biologically Inspired Cognitive Architectures, Vol. 24, pp. 98–106. DOI:10.1016/J.BICA.2018.04.012.
Kumar N., Khaund K., Hazarika S. M. (2016). Bispectral Analysis of EEG for Emotion Recognition. Procedia Computer Science, Vol. 84, pp.31–35. DOI:10.1016/j.procs.2016.04.062.
Arnau-González P., Arevalillo-Herráez M., Ramzan N. (2017). Fusing highly dimensional energy and connectivity features to identify affective states from EEG signals. Neurocomputing, Vol. 244, pp. 81–89. DOI:10.1016/j.neucom.2017.03.027.
Murugappan M., Rizon M., Nagarajan R., Yaacob S., Hazry D. & Zunaidi I. (2008). Time-Frequency Analysis of EEG Signals for Human Emotion Detection. 4th Kuala Lumpur International Conference on Biomedical Engineering 2008, pp.262-265. DOI:10.1007/978-3-540-69139-6_68.
Iacoviello D., Petracca A., Spezialetti M., Placidi G. (2015). A real-time classification algorithm for EEG-based BCI driven by self-induced emotions. Comput Methods Programs Biomed, Vol. 122, Iss. 3, pp. 293–303. DOI:10.1016/j.cmpb.2015.08.011.
Özerdem M. S., Polat H. (2017). Emotion recognition based on EEG features in movie clips with channel selection. Brain informatics, Vol. 4(4), pp. 241–252. DOI:10.1007/s40708-017-0069-3.
Alakus T. B., Gonen M., Turkoglu I. (2020). Database for an emotion recognition system based on EEG signals and various computer games -- GAMEEMO. Biomedical Signal Processing and Control, Vol. 60, 101951. DOI:10.1016/j.bspc.2020.101951.
Li M., Hongpei X., Liu X., Shengfu L. (2018). Emotion recognition from multichannel EEG signals using K-nearest neighbor classification. Technol Health Care, Vol. 26(S1), pp. 509–519. DOI: 10.3233/THC-174836.
Garg A., Kapoor A., Bedi A. K., Sunkaria R. K. (2019). Merged LSTM Model for emotion classification using EEG signals. 2019 International conference on Data Science and Engineering (ICDSE), pp 139–143. DOI: 10.1109/ICDSE47409.2019.8971484.
Atkinson J., Campos D. (2016). Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Expert Systems with Applications, Vol. 47, pp. 35–41. DOI: 10.1016/j.eswa.2015.10.049.
Huang C. (2021). Recognition of psychological emotion by EEG features. Network Modeling Analysis in Health Informatics and Bioinformatics, Vol. 10(1), pp. 1–11. doi: 10.1007/s13721-020-00283-2.
Subasi A., Tuncer T., Dogan S., Tanko D., Sakoglu U. (2021). EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier. Biomedical Signal Processing and Control, Vol. 68, 102648. DOI:10.1016/j.bspc.2021.102648.
Pane E. S., Wibawa A. D., Purnomo M. H. (2019). Improving the accuracy of EEG emotion recognition by combining valence lateralization and ensemble learning with tuning parameters. Cognitive Processing, Vol. 20(4), pp. 405–417. DOI: 10.1007/s10339-019-00924-z.
Wei C., Chen L., Song Z., Lou X., Li D. (2020). EEG-based emotion recognition using simple recurrent units network and ensemble learning. Biomedical Signal Processing and Control, Vol. 58, 101756. doi:10.1016/j.bspc.2019.101756.
Mert A., Akan A. (2018). Emotion recognition based on time-frequency distribution of EEG signals using multivariate synchrosqueezing transform. Digital Signal Processing, Vol. 81, pp. 106–115. DOI:10.1016/j.dsp.2018.07.003.
Pandey P., Seeja K. R. (2019). Subject independent emotion recognition from EEG using VMD and deep learning. Journal of King Saud University - Computer and Information Sciences, Vol. 34, Iss. 5, pp. 1730-1738. DOI:10.1016/j.jksuci.2019.11.003.
Dragan Ya. P. (1997). Enerhetychna teoriia liniinykh modelei stokhastychnykh syhnaliv [Energy theory of linear models of stochastic signals]. Lviv: Center for Strategic Studies of Eco-BioTechnical Systems [Tsentr stratehichnykh doslidzhen eko-biotekhnichnykh system], 333 p.
Hvostivska L., Yavorskyy B. (2015). The pulse signal mathematical model for the man vessels state diagnostics systems informativity increasing. Transactions of Kremenchuk Mykhailo Ostrohradskyi National University, Vol. 6(95), pp. 29-34.
Lang P. J., Bradley M. M., and Cuthbert B. N. (2005). International Affective Picture System (IAPS): Affective Ratings of Pictures and Instruction Manual, NIMH center for the study of emotion & attention, Univ. Florida, Gainesvill.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Микола Хвостівський, Ірина Паньків, Ольга Фуч, Лілія Хвостівська, Роман Бойко, Василь Дунець
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
