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Matematiksel ve İşitsel Görevlere Dayalı Bilişsel Yorgunluğun EEG Sinyallerinin Gama Bandından Tespiti

Year 2022, Issue: 41, 6 - 15, 30.11.2022
https://doi.org/10.31590/ejosat.1168173

Abstract

Bilişsel yorgunluk, var olan bilişsel performansı devam ettirebilmek için sürekli olmayan bir yetersizlik durumudur ve uzun süren aktiviteler veya stres altında uzun süre çalışma sonucu meydana gelen psiko-biyolojik bir durumdur. Bilişsel yorgunluk yapılan işte hataların artmasına, çeşitli güvenlik zafiyetlerinin ortaya çıkmasına ve performans azalmasına sebep olmaktadır. Bu nedenle bilişsel yorgunluğun tespiti bazı meslek grupları için oldukça önemlidir. Bu çalışmada, EEG sinyalleri kullanılarak bilişsel yorgunluk tespit edilmeye çalışılmıştır. Bilişsel yorgunluğun tespiti amacıyla oluşturulan paradigma kullanılarak toplam 8 katılımcıyla deneyler gerçekleştirilmiş ve EEG sinyalleri kaydedilmiştir. Kaydedilen EEG sinyalleri kullanılarak, beynin farklı bölgelerinin, farklı frekans bantlarının ve farklı EEG uzunluklarının bilişsel iş yükününün sınıflandırması üzerine etkisi araştırılmıştır. Yapay sinir ağı algoritmasıyla tüm elektrotların gama frekansına ait bant gücü ve 5 saniye uzunluğundaki EEG parçaları kullanılarak en yüksek %99.49 sınıflandırma doğruluğu ile bilişsel yorgunluk tespit edilmiştir.

References

  • ABUKHETTALA, K., & Oğuz, A. T. A. (2022). Analyzing of EEG Signals with Deep Learning and Discrete Wavelet Transform. Avrupa Bilim ve Teknoloji Dergisi, (35), 514-52
  • Bjørheim, F., Siriwardane, S. C., & Pavlou, D., 2022. A review of fatigue damage detection and measurement techniques. International Journal of Fatigue, 154, 106556.
  • Chaudhuri, A., ve Behan, P. O., 2004. Fatigue in neurological disorders. The lancet, 978-988. doi:10.1016/S0140-6736(04)15794-2
  • Chiossi, F., Welsch, R., Villa, S., Chuang, L., & Mayer, S., 2022. Virtual Reality Adaptation Using Electrodermal Activity to Support the User Experience. Big Data and Cognitive Computing, 6(2), 55. doi:10.3390/bdcc6020055
  • Dehais, F., Somon, B., Mullen, T., ve Callan, D. E., 2020. A neuroergonomics approach to measure pilot’s cognitive incapacitation in the real world with EEG. Applied Human Factors and Ergonomics, 111-117. doi:10.1007/978-3-030-51041-1_16
  • Karakaş, M. F., & Latifoğlu, F. (2022). Metaheuristic FIR Filter Design with Multi-Objective Atomic Orbital Search Algorithm. Avrupa Bilim ve Teknoloji Dergisi, (39), 13-16.
  • Marotta, L., Scheltinga, B. L., van Middelaar, R., Bramer, W. M., van Beijnum, B. J. F., Reenalda, J., & Buurke, J. H., 2022. Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review. Sensors, 22(8), 3008. doi:10.3390/s22083008
  • Papakostas, M., Rajavenkatanarayanan, A., ve Makedon, F., 2019. Cogbeacon: A multi-modal dataset and data-collection platform for modeling cognitive fatigue. Technologies, 7, 46. doi:10.3390/technologies7020046
  • Pires, F. O., Silva-Júnior, F. L., Brietzke, C., Franco-Alvarenga, P. E., Pinheiro, F. A., De Franca, N. M., ve Meireles Santos, T., 2018. Mental fatigue alters cortical activation and psychological responses, impairing performance in a distance-based cycling trial. Frontiers in physiology, 227. doi:10.3389/fphys.2018.00227
  • Salankar, N., Koundal, D., Chakraborty, C., & Garg, L., 2022. Automated attention deficit classification system from multimodal physiological signals. Multimedia Tools and Applications, 1-16. doi:10.1007/s11042-022-12170-1
  • Sun, Y., Lim, J., Meng, J., Kwok, K., Thakor, N., ve Bezerianos, A., 2014. Discriminative analysis of brain functional connectivity patterns for mental fatigue classification. Annals of biomedical engineering, 42, 2084-2094. doi:10.1007/s10439-014-1059-8
  • Trejo, L. J., Knuth, K., Prado, R., Rosipal, R., Kubitz, K., Kochavi, R., ve Zhang, Y., 2007. EEG-based estimation of mental fatigue: convergent evidence for a three-state model. Foundations of Augmented Cognition, 2000-2012. doi:10.1007/978-3-540-73216-7_23
  • Weinberg, R., ve Gould, D., 2003. Introduction to psychological skills training. Foundations of sport and exercise psychology, 327-352. doi:10.1080/1612197X.2003.9671724

Detection of Cognitive Fatigue Based on Mathematical and Auditory Tasks using Gamma Band of EEG Signals

Year 2022, Issue: 41, 6 - 15, 30.11.2022
https://doi.org/10.31590/ejosat.1168173

Abstract

Cognitive fatigue is a discontinuous inability to maintain the existing cognitive performance and is a psycho-biological condition that occurs due to prolonged activities or working under stress. Cognitive fatigue causes an increase in errors, the emergence of various security vulnerabilities, and a decrease in performance. In this study, cognitive fatigue was tried to be determined by using EEG signals, which provide advantages in terms of use-transportation. Experiments were carried out with a total of 8 participants using the paradigm created for the detection of cognitive fatigue and EEG signals were recorded. Using the recorded EEG signals, the effects of different brain regions, different frequency bands, and different EEG lengths on the classification of cognitive workload were investigated. In addition, band power of EEG signals in situations with resting and cognitive workload were compared graphically. With the artificial neural network algorithm, the highest 99.49% classification accuracy was obtained by using the band power of the gamma frequency of all electrodes and the 5-second-long EEG segments.

References

  • ABUKHETTALA, K., & Oğuz, A. T. A. (2022). Analyzing of EEG Signals with Deep Learning and Discrete Wavelet Transform. Avrupa Bilim ve Teknoloji Dergisi, (35), 514-52
  • Bjørheim, F., Siriwardane, S. C., & Pavlou, D., 2022. A review of fatigue damage detection and measurement techniques. International Journal of Fatigue, 154, 106556.
  • Chaudhuri, A., ve Behan, P. O., 2004. Fatigue in neurological disorders. The lancet, 978-988. doi:10.1016/S0140-6736(04)15794-2
  • Chiossi, F., Welsch, R., Villa, S., Chuang, L., & Mayer, S., 2022. Virtual Reality Adaptation Using Electrodermal Activity to Support the User Experience. Big Data and Cognitive Computing, 6(2), 55. doi:10.3390/bdcc6020055
  • Dehais, F., Somon, B., Mullen, T., ve Callan, D. E., 2020. A neuroergonomics approach to measure pilot’s cognitive incapacitation in the real world with EEG. Applied Human Factors and Ergonomics, 111-117. doi:10.1007/978-3-030-51041-1_16
  • Karakaş, M. F., & Latifoğlu, F. (2022). Metaheuristic FIR Filter Design with Multi-Objective Atomic Orbital Search Algorithm. Avrupa Bilim ve Teknoloji Dergisi, (39), 13-16.
  • Marotta, L., Scheltinga, B. L., van Middelaar, R., Bramer, W. M., van Beijnum, B. J. F., Reenalda, J., & Buurke, J. H., 2022. Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review. Sensors, 22(8), 3008. doi:10.3390/s22083008
  • Papakostas, M., Rajavenkatanarayanan, A., ve Makedon, F., 2019. Cogbeacon: A multi-modal dataset and data-collection platform for modeling cognitive fatigue. Technologies, 7, 46. doi:10.3390/technologies7020046
  • Pires, F. O., Silva-Júnior, F. L., Brietzke, C., Franco-Alvarenga, P. E., Pinheiro, F. A., De Franca, N. M., ve Meireles Santos, T., 2018. Mental fatigue alters cortical activation and psychological responses, impairing performance in a distance-based cycling trial. Frontiers in physiology, 227. doi:10.3389/fphys.2018.00227
  • Salankar, N., Koundal, D., Chakraborty, C., & Garg, L., 2022. Automated attention deficit classification system from multimodal physiological signals. Multimedia Tools and Applications, 1-16. doi:10.1007/s11042-022-12170-1
  • Sun, Y., Lim, J., Meng, J., Kwok, K., Thakor, N., ve Bezerianos, A., 2014. Discriminative analysis of brain functional connectivity patterns for mental fatigue classification. Annals of biomedical engineering, 42, 2084-2094. doi:10.1007/s10439-014-1059-8
  • Trejo, L. J., Knuth, K., Prado, R., Rosipal, R., Kubitz, K., Kochavi, R., ve Zhang, Y., 2007. EEG-based estimation of mental fatigue: convergent evidence for a three-state model. Foundations of Augmented Cognition, 2000-2012. doi:10.1007/978-3-540-73216-7_23
  • Weinberg, R., ve Gould, D., 2003. Introduction to psychological skills training. Foundations of sport and exercise psychology, 327-352. doi:10.1080/1612197X.2003.9671724
There are 13 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Sevde Gül Korkmaz 0000-0001-6043-1353

Onur Erdem Korkmaz 0000-0001-6336-6147

Önder Aydemir 0000-0002-1177-8518

Early Pub Date October 2, 2022
Publication Date November 30, 2022
Published in Issue Year 2022 Issue: 41

Cite

APA Korkmaz, S. G., Korkmaz, O. E., & Aydemir, Ö. (2022). Matematiksel ve İşitsel Görevlere Dayalı Bilişsel Yorgunluğun EEG Sinyallerinin Gama Bandından Tespiti. Avrupa Bilim Ve Teknoloji Dergisi(41), 6-15. https://doi.org/10.31590/ejosat.1168173