Research Article
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Year 2022, , 148 - 168, 08.08.2022
https://doi.org/10.28978/nesciences.1159248

Abstract

References

  • Abhang, P. A., Gawali, B. W., & Mehrotra, S. C. (2016). Introduction to EEG- and Speech-Based Emotion Recognition. In: Introduction to EEG- and Speech-Based Emotion Recognition. https://doi.org/10.1016/C2015-0-01959-1
  • Aftanas, L. I., Varlamov, A. A., Pavlov, S. V., Makhnev, V. P., & Reva, N. V. (2002). Time-dependent cortical asymmetries induced by emotional arousal: EEG analysis of event-related synchronization and desynchronization in individually defined frequency bands. International Journal of Psychophysiology, 44(1). https://doi.org/10.1016/S0167-8760(01)00194-5.
  • Altan, G., & Inat, G. (2021). EEG based Spatial Attention Shifts Detection using Time-Frequency features on Empirical Wavelet Transform. Journal of Intelligent Systems with Applications. https://doi.org/10.54856/10.54856/jiswa.202112181.
  • Altan, G., & Kutlu, Y. (2018). Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis. Natural and Engineering Sciences, 3(3). https://doi.org/10.28978/nesciences.468978.
  • Altan, G., Kutlu, Y., & Allahverdi, N. (2016). Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke. International Journal of Applied Mathematics, Electronics and Computers, 205–205. https://doi.org/10.18100/ijamec.270307
  • Altan, G., Yayık, A., & Kutlu, Y. (2021). Deep Learning with ConvNet Predicts Imagery Tasks Through EEG. Neural Processing Letters, 53(4). https://doi.org/10.1007/s11063-021-10533-7. Badcock, N. A., Mousikou, P., Mahajan, Y., De Lissa, P., Thie, J., & McArthur, G. (2013). Validation of the Emotiv EPOC® EEG gaming systemfor measuring research quality auditory ERPs. PeerJ, 2013(1), 1–17. https://doi.org/10.7717/peerj.38.
  • Bălan, O., Moise, G., Moldoveanu, A., Leordeanu, M., & Moldoveanu, F. (2019). Fear level classification based on emotional dimensions and machine learning techniques. Sensors (Switzerland), 19(7), 1–18. https://doi.org/10.3390/s19071738.
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  • Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1). https://doi.org/10.1016/0005-7916(94)90063-9.
  • Chabin, T., Gabriel, D., Haffen, E., Moulin, T., & Pazart, L. (2020). Are the new mobile wireless EEG headsets reliable for the evaluation of musical pleasure? PLoS ONE, 15(12 December). https://doi.org/10.1371/journal.pone.0244820.
  • Ciuk, D., Troy, A. K., & Jones, M. C. (2015). Measuring Emotion: Self-Reports vs. Physiological Indicators. SSRN Electronic Journal, October. https://doi.org/10.2139/ssrn.2595359.
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  • Damasio, A. R. (1998). Emotion in the perspective of an integrated nervous system. Brain Research Reviews, 26(2–3), 83–86. https://doi.org/10.1016/S0165-0173(97)00064-7.
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  • Di Flumeri, G., Aricò, P., Borghini, G., Sciaraffa, N., Di Florio, A., & Babiloni, F. (2019). The dry revolution: Evaluation of three different eeg dry electrode types in terms of signal spectral features, mental states classification and usability. Sensors (Switzerland), 19(6). https://doi.org/10.3390/s19061365.
  • Duvinage, M., Castermans, T., Petieau, M., Hoellinger, T., Cheron, G., & Dutoit, T. (2013). Performance of the Emotiv Epoc headset for P300-based applications. BioMedical Engineering Online, 12(1), 1–15. https://doi.org/10.1186/1475-925X-12-56.
  • EEGLAB download page. (n.d.). https://sccn.ucsd.edu/eeglab/download.php.
  • EEGLAB Plotting Channel Spectra Tutorial. (n.d.). https://eeglab.org/tutorials/08_Plot_data/Plotting_Channel_Spectra_and_Maps.html.
  • Eijlers, E., Smidts, A., & Boksem, M. A. S. (2019). Implicit measurement of emotional experience and its dynamics. PLoS ONE, 14(2), 1–15. https://doi.org/10.1371/journal.pone.0211496.
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  • Emotiv home page. (n.d.). https://www.emotiv.com/.
  • EPOC user manual. (n.d.). https://emotiv.gitbook.io/epoc-user-manual/.
  • Fakhruzzaman, M. N., Riksakomara, E., & Suryotrisongko, H. (2015). EEG Wave Identification in Human Brain with Emotiv EPOC for Motor Imagery. Procedia Computer Science, 72. https://doi.org/10.1016/j.procs.2015.12.140.
  • Frantzidis, C. A., Bratsas, C., Papadelis, C. L., Konstantinidis, E., Pappas, C., & 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, 14(3). https://doi.org/10.1109/TITB.2010.2041553.
  • Harmon-Jones, E., Gable, P. A., & Peterson, C. K. (2010). The role of asymmetric frontal cortical activity in emotion-related phenomena: A review and update. Biological Psychology, 84 (3). https://doi.org/10.1016/j.biopsycho.2009.08.010.
  • Hassouneh, A., Mutawa, A. M., & Murugappan, M. (2020). Development of a Real-Time Emotion Recognition System Using Facial Expressions and EEG based on machine learning and deep neural network methods. Informatics in Medicine Unlocked, 20, 100372. https://doi.org/10.1016/j.imu.2020.100372.
  • Iacoviello, D., Petracca, A., Spezialetti, M., & Placidi, G. (2015). A classification algorithm for electroencephalography signals by self-induced emotional stimuli. IEEE Transactions on Cybernetics, 46(10). https://doi.org/10.1109/TCYB.2015.2498974.
  • 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, xxxx. https://doi.org/10.1016/j.jksuci.2020.10.007.
  • Katsigiannis, S., & Ramzan, N. (2018). DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices. IEEE Journal of Biomedical and Health Informatics, 22(1), 98–107. https://doi.org/10.1109/JBHI.2017.2688239.
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Detection of EEG Patterns for Induced Fear Emotion State via EMOTIV EEG Testbench

Year 2022, , 148 - 168, 08.08.2022
https://doi.org/10.28978/nesciences.1159248

Abstract

In this study, International Affective Picture System (IAPS) were used to evoke fear and neutral stimuli using EMOTIV EPOC EEG recognition system (n=15). During the experiments, EEG data were recorded using the Test bench program. To synchronize the EEG records, IAPS pictures were reflected on the screen. A Python script was written in the Open Sesame program to provide a synchronized data flow in the Input/Output channels of the installed virtual serial port. The Event-Related Oscillations (ERO) responses and Event-Related Potentials (ERPs) were calculated. Statistically significant differences (p<0.05) were observed among the mean amplitude differences in the P7, O1, F3, AF3, P8 channels at 200-400 milliseconds in the ERP analysis, and also significant (p<0.05) differences were found in alpha(∝) and beta(β) brainwaves compared to neutral stimuli, in the Fast Fourier Transform (FFT) analysis. After these evaluations, different time-spectral signal activity patterns occurred in the right frontal lobe (F4) at the (∝) band, and in the left parietal lobe at the (β) band, respectively.

References

  • Abhang, P. A., Gawali, B. W., & Mehrotra, S. C. (2016). Introduction to EEG- and Speech-Based Emotion Recognition. In: Introduction to EEG- and Speech-Based Emotion Recognition. https://doi.org/10.1016/C2015-0-01959-1
  • Aftanas, L. I., Varlamov, A. A., Pavlov, S. V., Makhnev, V. P., & Reva, N. V. (2002). Time-dependent cortical asymmetries induced by emotional arousal: EEG analysis of event-related synchronization and desynchronization in individually defined frequency bands. International Journal of Psychophysiology, 44(1). https://doi.org/10.1016/S0167-8760(01)00194-5.
  • Altan, G., & Inat, G. (2021). EEG based Spatial Attention Shifts Detection using Time-Frequency features on Empirical Wavelet Transform. Journal of Intelligent Systems with Applications. https://doi.org/10.54856/10.54856/jiswa.202112181.
  • Altan, G., & Kutlu, Y. (2018). Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis. Natural and Engineering Sciences, 3(3). https://doi.org/10.28978/nesciences.468978.
  • Altan, G., Kutlu, Y., & Allahverdi, N. (2016). Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke. International Journal of Applied Mathematics, Electronics and Computers, 205–205. https://doi.org/10.18100/ijamec.270307
  • Altan, G., Yayık, A., & Kutlu, Y. (2021). Deep Learning with ConvNet Predicts Imagery Tasks Through EEG. Neural Processing Letters, 53(4). https://doi.org/10.1007/s11063-021-10533-7. Badcock, N. A., Mousikou, P., Mahajan, Y., De Lissa, P., Thie, J., & McArthur, G. (2013). Validation of the Emotiv EPOC® EEG gaming systemfor measuring research quality auditory ERPs. PeerJ, 2013(1), 1–17. https://doi.org/10.7717/peerj.38.
  • Bălan, O., Moise, G., Moldoveanu, A., Leordeanu, M., & Moldoveanu, F. (2019). Fear level classification based on emotional dimensions and machine learning techniques. Sensors (Switzerland), 19(7), 1–18. https://doi.org/10.3390/s19071738.
  • Barrett, L. F., & Wager, T. D. (2006). The structure of emotion evidence from neuroimaging studies. Current Directions in Psychological Science, 15(2). https://doi.org/10.1111/j.0963-7214.2006.00411.x. Basar, M. D., Duru, A. D., & Akan, A. (2020). Emotional state detection based on common spatial patterns of EEG. Signal, Image and Video Processing, 14(3). https://doi.org/10.1007/s11760-019-01580-8.
  • Bazgir, O., Mohammadi, Z., & Habibi, S. A. H. (2018). Emotion Recognition with Machine Learning Using EEG Signals. 2018 25th Iranian Conference on Biomedical Engineering and 2018 3rd International Iranian Conference on Biomedical Engineering, ICBME 2018. https://doi.org/10.1109/ICBME.2018.8703559.
  • Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1). https://doi.org/10.1016/0005-7916(94)90063-9.
  • Chabin, T., Gabriel, D., Haffen, E., Moulin, T., & Pazart, L. (2020). Are the new mobile wireless EEG headsets reliable for the evaluation of musical pleasure? PLoS ONE, 15(12 December). https://doi.org/10.1371/journal.pone.0244820.
  • Ciuk, D., Troy, A. K., & Jones, M. C. (2015). Measuring Emotion: Self-Reports vs. Physiological Indicators. SSRN Electronic Journal, October. https://doi.org/10.2139/ssrn.2595359.
  • Cruz-Garza, J. G., Brantley, J. A., Nakagome, S., Kontson, K., Megjhani, M., Robleto, D., & Contreras-Vidal, J. L. (2017). Deployment of mobile EEG technology in an art museum setting: Evaluation of signal quality and usability. Frontiers in Human Neuroscience, 11. https://doi.org/10.3389/fnhum.2017.00527.
  • Damasio, A. R. (1998). Emotion in the perspective of an integrated nervous system. Brain Research Reviews, 26(2–3), 83–86. https://doi.org/10.1016/S0165-0173(97)00064-7.
  • de Cesarei, A., & Codispoti, M. (2011). Affective modulation of the LPP and α-ERD during picture viewing. Psychophysiology, 48(10). https://doi.org/10.1111/j.1469-8986.2011.01204.x.
  • Di Flumeri, G., Aricò, P., Borghini, G., Sciaraffa, N., Di Florio, A., & Babiloni, F. (2019). The dry revolution: Evaluation of three different eeg dry electrode types in terms of signal spectral features, mental states classification and usability. Sensors (Switzerland), 19(6). https://doi.org/10.3390/s19061365.
  • Duvinage, M., Castermans, T., Petieau, M., Hoellinger, T., Cheron, G., & Dutoit, T. (2013). Performance of the Emotiv Epoc headset for P300-based applications. BioMedical Engineering Online, 12(1), 1–15. https://doi.org/10.1186/1475-925X-12-56.
  • EEGLAB download page. (n.d.). https://sccn.ucsd.edu/eeglab/download.php.
  • EEGLAB Plotting Channel Spectra Tutorial. (n.d.). https://eeglab.org/tutorials/08_Plot_data/Plotting_Channel_Spectra_and_Maps.html.
  • Eijlers, E., Smidts, A., & Boksem, M. A. S. (2019). Implicit measurement of emotional experience and its dynamics. PLoS ONE, 14(2), 1–15. https://doi.org/10.1371/journal.pone.0211496.
  • Ekman, P., & Friesen, W. V. (1971). Constants across cultures in the face and emotion. Journal of Personality and Social Psychology, 17(2). https://doi.org/10.1037/h0030377.
  • Emotiv home page. (n.d.). https://www.emotiv.com/.
  • EPOC user manual. (n.d.). https://emotiv.gitbook.io/epoc-user-manual/.
  • Fakhruzzaman, M. N., Riksakomara, E., & Suryotrisongko, H. (2015). EEG Wave Identification in Human Brain with Emotiv EPOC for Motor Imagery. Procedia Computer Science, 72. https://doi.org/10.1016/j.procs.2015.12.140.
  • Frantzidis, C. A., Bratsas, C., Papadelis, C. L., Konstantinidis, E., Pappas, C., & 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, 14(3). https://doi.org/10.1109/TITB.2010.2041553.
  • Harmon-Jones, E., Gable, P. A., & Peterson, C. K. (2010). The role of asymmetric frontal cortical activity in emotion-related phenomena: A review and update. Biological Psychology, 84 (3). https://doi.org/10.1016/j.biopsycho.2009.08.010.
  • Hassouneh, A., Mutawa, A. M., & Murugappan, M. (2020). Development of a Real-Time Emotion Recognition System Using Facial Expressions and EEG based on machine learning and deep neural network methods. Informatics in Medicine Unlocked, 20, 100372. https://doi.org/10.1016/j.imu.2020.100372.
  • Iacoviello, D., Petracca, A., Spezialetti, M., & Placidi, G. (2015). A classification algorithm for electroencephalography signals by self-induced emotional stimuli. IEEE Transactions on Cybernetics, 46(10). https://doi.org/10.1109/TCYB.2015.2498974.
  • 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, xxxx. https://doi.org/10.1016/j.jksuci.2020.10.007.
  • Katsigiannis, S., & Ramzan, N. (2018). DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices. IEEE Journal of Biomedical and Health Informatics, 22(1), 98–107. https://doi.org/10.1109/JBHI.2017.2688239.
  • Klimesch, W. (2012). Alpha-band oscillations, attention, and controlled access to stored information. Trends in Cognitive Sciences, 16(12). https://doi.org/10.1016/j.tics.2012.10.007
  • Klug, M., & Gramann, K. (2020). Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments. European Journal of Neuroscience, May, 1–15. https://doi.org/10.1111/ejn.14992.
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There are 64 citations in total.

Details

Primary Language English
Subjects Clinical Sciences
Journal Section Articles
Authors

Ahmet Ergun Gümüş 0000-0002-2044-5504

Çağlar Uyulan This is me 0000-0002-6423-6720

Zozan Guleken 0000-0002-4136-4447

Publication Date August 8, 2022
Submission Date January 4, 2022
Published in Issue Year 2022

Cite

APA Gümüş, A. E., Uyulan, Ç., & Guleken, Z. (2022). Detection of EEG Patterns for Induced Fear Emotion State via EMOTIV EEG Testbench. Natural and Engineering Sciences, 7(2), 148-168. https://doi.org/10.28978/nesciences.1159248

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