Motor Görüntüleme Görevlerinde EEG Bant Dinamiklerinin Araştırılması: BCI Eğitimli Bir Bireyin Topografik Beyin Haritalama Çalışması

Öz

Elektroensefalogram (EEG) sinyalleri, maliyet etkinliği ve yüksek hareket kabiliyeti nedeniyle Beyin-Bilgisayar Arayüzü (BBA) uygulamaları için oldukça uygundur. EEG sinyalleri yüksek zamansal çözünürlüğe sahip olmasına rağmen, uzaysal çözünürlükleri düşüktür. Bu çalışmada, BBA eğitimi almış bir bireyin dört sınıflı Motor İmgeleme (MI) EEG sinyalleri, EEG'nin uzaysal dağılımını incelemek için topografik beyin haritalama yöntemi ile analiz edilmiştir. Analiz, MI görevinin her saniyesi için her bir EEG dalga bandının incelenmesiyle gerçekleştirilmiştir. Sonuç olarak, konsantrasyon ve öğrenme ile ilgili Delta ve Teta bantları ilk saniyelerde aktivite göstermiştir. Alfa, Beta ve Gamma aktiviteleri için aktif düşünce ile ilgili süreçler ilk saniyelerde, karar verme ile ilgili süreçler ise son saniyelerde gerçekleşmiştir. Her bir elektrot kanalı için EEG bantlarının aktiviteleri belirlendiğinde, BBA uygulamalarında başarı artırılabilir. Çalışmanın paradigma oluşturma ve iyileştirme konusunda yapılacak diğer çalışmalara referans olma potansiyeli bulunmaktadır.

Anahtar Kelimeler

• BBA
• EEG Dalga Bantları
• Motor Görüntüleme
• Topografik Beyin Haritalama

Investigating EEG Band Dynamics in Motor Imagery Tasks: A Topographic Brain Mapping Study of a BCI-Trained Individual

Öz

Electroencephalogram (EEG) signals are very suitable for Brain-Computer Interface (BCI) applications due to their cost-effectiveness and high mobility. Although EEG signals have a high temporal resolution, their spatial resolution is low. In this study, four-class Motor Imagery (MI) EEG signals of an individual who received 20-day BCI training were analyzed with the topographic brain mapping method to examine the spatial distribution of EEG. The analysis was performed by examining each EEG waveband for each second of the MI task. As a result, Delta and Theta bands related to concentration and learning showed activity in the first seconds. For Alpha, Beta, and Gamma activities, processes related to active thought occurred in the first seconds, and processes related to decision-making happened in the last seconds. When the activities of EEG bands for each electrode channel are determined, the success in BCI applications can be increased. The study has the potential to be a reference for other studies on paradigm creation and improvement.

Anahtar Kelimeler

• BCI
• EEG Wave Bands
• Motor Imagery
• Topographic Brain Mapping

Kaynakça

1. [1] Li, R., Yang, D., Fang, F., Hong, K.-S., Reiss, A.L., and Zhang, Y.: ‘Concurrent fNIRS and EEG for brain function investigation: a systematic, methodology-focused review’, Sensors, 2022, 22, (15), pp. 5865
2. [2] Zhang, Y., Yu, Y., Li, H., Wu, A., Zeng, L.-L., and Hu, D.: ‘MASER: Enhancing EEG Spatial Resolution with State Space Modeling’, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024
3. [3] Formaggio, E., Storti, S.F., Cerini, R., Fiaschi, A., and Manganotti, P.: ‘Brain oscillatory activity during motor imagery in EEG-fMRI coregistration’, Magnetic resonance imaging, 2010, 28, (10), pp. 1403-1412
4. [4] Shin, Y.K., Lee, D.R., Hwang, H.J., You, S.H., and Im, C.H.: ‘A novel EEG-based brain mapping to determine cortical activation patterns in normal children and children with cerebral palsy during motor imagery tasks’, NeuroRehabilitation, 2012, 31, (4), pp. 349-355
5. [5] Edelman, B., Baxter, B., and He, B.: ‘Decoding and mapping of right hand motor imagery tasks using EEG source imaging’, in Editor (Ed.)^(Eds.): ‘Book Decoding and mapping of right hand motor imagery tasks using EEG source imaging’ (IEEE, 2015, edn.), pp. 194-197
6. [6] Edelman, B.J., Baxter, B., and He, B.: ‘EEG source imaging enhances the decoding of complex right-hand motor imagery tasks’, IEEE Transactions on Biomedical Engineering, 2015, 63, (1), pp. 4-14
7. [7] Wilson, V., Dikman, Z., Bird, E., Williams, J., Harmison, R., Shaw-Thornton, L., and Schwartz, G.: ‘EEG topographic mapping of visual and kinesthetic imagery in swimmers’, Applied Psychophysiology and Biofeedback, 2016, 41, pp. 121-127
8. [8] Catrambone, V., Greco, A., Averta, G., Bianchi, M., Bicchi, A., Scilingo, E.P., and Valenza, G.: ‘EEG complexity maps to characterise brain dynamics during upper limb motor imagery’, in Editor (Ed.)^(Eds.): ‘Book EEG complexity maps to characterise brain dynamics during upper limb motor imagery’ (IEEE, 2018, edn.), pp. 3060-3063

9. [9] Taylor, P.N., Papasavvas, C.A., Owen, T.W., Schroeder, G.M., Hutchings, F.E., Chowdhury, F.A., Diehl, B., Duncan, J.S., McEvoy, A.W., and Miserocchi, A.: ‘Normative brain mapping of interictal intracranial EEG to localize epileptogenic tissue’, Brain, 2022, 145, (3), pp. 939-949
10. [10] Kaya, E., and Saritas, I.: ‘Identifying optimal channels and features for multi-participant motor imagery experiments across a participant’s multi-day multi-class EEG data’, Cognitive Neurodynamics, 2024, 18, (3), pp. 987-1003
11. [11] Oostenveld, R., Fries, P., Maris, E., and Schoffelen, J.-M.: ‘FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data’, Computational intelligence and neuroscience, 2011, 2011, (1), pp. 156869
12. [12] Fernandez Rojas, R., Debie, E., Fidock, J., Barlow, M., Kasmarik, K., Anavatti, S., Garratt, M., and Abbass, H.: ‘Electroencephalographic workload indicators during teleoperation of an unmanned aerial vehicle shepherding a swarm of unmanned ground vehicles in contested environments’, Frontiers in neuroscience, 2020, 14, pp. 40
13. [13] Niedermeyer, E.: ‘Niedermeyer's electroencephalography: basic principles, clinical applications, and related fields’ (Lippincott Williams & Wilkins, 2011. 2011)
14. [14] Kirmizi-Alsan, E., Bayraktaroglu, Z., Gurvit, H., Keskin, Y.H., Emre, M., and Demiralp, T.: ‘Comparative analysis of event-related potentials during Go/NoGo and CPT: decomposition of electrophysiological markers of response inhibition and sustained attention’, Brain research, 2006, 1104, (1), pp. 114-128
15. [15] Edla, D.R., Ansari, M.F., Chaudhary, N., and Dodia, S.: ‘Classification of facial expressions from eeg signals using wavelet packet transform and svm for wheelchair control operations’, Procedia computer science, 2018, 132, pp. 1467-1476
16. [16] Ha, K.-W., and Jeong, J.-W.: ‘Motor imagery EEG classification using capsule networks’, Sensors, 2019, 19, (13), pp. 2854
17. [17] Tiwari, S., Goel, S., and Bhardwaj, A.: ‘MIDNN-a classification approach for the EEG based motor imagery tasks using deep neural network’, Applied Intelligence, 2022, pp. 1-20
18. [18] Kaya, E., and Sarıtas, I.: ‘Feature Analysis for Motor Imagery EEG Signals with Different Classification Schemes’, Sakarya University Journal of Science, 2023, 27, (2), pp. 259-270Substance Abuse and Mental Health Services Administration, Office of Applied Studies, August, 2013, DOI:10.3886/ICPSR30122.v2