MOTOR İMGELEME EEG SİNYALLERİNDE SINIFLANDIRMA PERFORMANSINI ARTTIRMAYA YÖNELİK ADAPTİF SEGMENTASYON YAKLAŞIMI

Tuğçe Ballı

doi:10.17482/uumfd.1698204

Research Article

Adaptive Segmentation Approach for an Improved Classification of MI-EEG Signals

Year 2025, Volume: 30 Issue: 3, 711 - 730, 19.12.2025

Tuğçe Ballı

https://doi.org/10.17482/uumfd.1698204

Abstract

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) rely on effective design of signal processing pipelines to achieve high classification performance. The fundamental EEG signal processing steps include preprocessing, feature extraction, selection, and classification. In the feature extraction step, EEG signals are typically segmented using a fixed window length. This study proposes an adaptive segmentation scheme using change point detection (CPD) to achieve optimal segmentation for enhanced feature representation. Pruned exact linear time (PELT) algorithm was used to detect change points by minimizing a cost function with a sensitivity parameter for avoiding over segmentation. The proposed and fixed-point segmentation approaches were evaluated using BCI Competition IV dataset 2a, which contains EEG recordings from 9 subjects performing motor imagery tasks involving left-hand, righthand, foot and tongue movements. Results demonstrated that the CPD-based method improved classification performance on test data for both binary and four-class classification tasks. In binary classification, performance improvement ranged from 5.81% to 8.72%, depending on class pair. The highest classification performance was observed in left hand and tongue movements, with participantspecific improvements ranging from 4.16% to 12.73%. In four-class task, an average improvement of 7.5% was observed, with participant-specific improvements ranging from 3.93% to 11.11%.

Keywords

EEG , Motor Imagery , Change Point Detection , Fixed Point Segmentation , Classification

Supporting Institution

TÜBİTAK

Project Number

124E057

Thanks

This study was supported by the Scientific and Technological Research Council of Türkiye (TÜBİTAK) under Project No. 124E057.

References

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Ang, K. K., Chin, Z. Y., Wang, C., Guan, C., & Zhang, H. (2012). Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Frontiers in neuroscience, 6, 39. Doi: 10.3389/fnins.2012.00039
Azami, H., Anisheh, S. M., & Hassanpour, H. (2013, December). An Adaptive Automatic EEG Signal Segmentation Method Based on Generalized Likelihood Ratio. In International Symposium on Artificial Intelligence and Signal Processing (pp. 172-180). Cham: Springer International Publishing. Doi: 10.1007/978-3-319-10849-0_18
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MOTOR İMGELEME EEG SİNYALLERİNDE SINIFLANDIRMA PERFORMANSINI ARTTIRMAYA YÖNELİK ADAPTİF SEGMENTASYON YAKLAŞIMI

Year 2025, Volume: 30 Issue: 3, 711 - 730, 19.12.2025

Tuğçe Ballı

https://doi.org/10.17482/uumfd.1698204

Abstract

Elektroansefalogram (EEG) tabanlı beyin-bilgisayar arayüzlerinin (BBA) performansı, sistem tasarımında kullanılan sinyal işleme yöntemlerine doğrudan bağlıdır. EEG sinyal işleme süreci; önişleme, öznitelik çıkarma, seçme ve sınıflandırma adımlarını içerir. EEG verilerinden öznitelik çıkarımı genellikle sabit uzunlukta pencerelere ayrıldıktan sonra yapılmaktadır. Bu çalışmada özniteliklerin veri temsiliyetini artırmak için değişim noktası tespitine (change point detection, CPD) dayalı adaptif bir segmentasyon yaklaşımı önerilmiştir. Bu amaçla budanmış kesin doğrusal zaman (pruned exact linear time, PELT) algoritması kullanılmıştır. Bu yöntem, değişim noktalarını uygun bir maliyet fonksiyonu ile duyarlılık parametresinin belirlenmesi yoluyla tespit etmektedir. Önerilen yöntemin, sabit segmentasyona kıyasla etkinliği, BCI Competition IV 2a veri seti kullanılarak değerlendirilmiştir. Bu veri seti, 9 katılımcının sol el, sağ el, ayak ve dil imgeleme görevlerini gerçekleştirirken kaydedilmiş EEG verilerini içermektedir. Sonuçlar, CPD tabanlı yöntemin hem ikili hem de dört sınıflı sınıflandırmada test verisi üzerindeki sınıflandırma başarımını artırdığını göstermiştir. İkili sınıflandırma senaryosunda, önerilen yöntemin performans artışı %5,81 ile %8,72 arasında değişmiştir. En yüksek sınıflandırma performansı, sol el ve dil görevleri arasında gözlemlenmiş; katılımcı bazında performans artışları %4,16 ve %12,73 aralığında değişmiştir. Dört sınıflı sınıflandırma görevinde ise ortalama %7,5 oranında bir başarı artışı sağlanmış olup, katılımcı bazlı performans artışları %3,93 ile %11,11 aralığında değişmiştir.

Keywords

EEG , Motor İmgeleme , Değişim Noktası Tespiti , Sabit Süreli Segmentasyon , Sınıflandırma

Supporting Institution

TÜBİTAK

Project Number

124E057

Thanks

Bu çalışma, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından 124E057 numaralı proje kapsamında desteklenmiştir.

References

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Al-Saegh, A., Dawwd, S. A., & Abdul-Jabbar, J. M. (2021). Deep learning for motor imagery EEG-based classification: A review. Biomedical Signal Processing and Control, 63, 102172. Doi: 10.1016/j.bspc.2020.102172
Allison, B. Z., & Neuper, C. (2010). Could anyone use a BCI?. In Brain-computer interfaces: Applying our minds to human-computer interaction (pp. 35-54). London: Springer London. Doi: 10.1007/978-1-84996-272-8_3
Altaheri, H., Muhammad, G., Alsulaiman, M., Amin, S. U., Altuwaijri, G. A., Abdul, W., Bencherif, M.A. & Faisal, M. (2023). Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review. Neural Computing and Applications, 35(20), 14681-14722. Doi: 10.1007/s00521-021-06352-5
Amer, N. S., & Belhaouari, S. B. (2023). Eeg signal processing for medical diagnosis, healthcare, and monitoring: A comprehensive review. IEEE Access, 11, 143116-143142. Doi: 10.1109/ACCESS.2023.3341419
Ang, K. K., Chin, Z. Y., Wang, C., Guan, C., & Zhang, H. (2012). Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Frontiers in neuroscience, 6, 39. Doi: 10.3389/fnins.2012.00039
Azami, H., Anisheh, S. M., & Hassanpour, H. (2013, December). An Adaptive Automatic EEG Signal Segmentation Method Based on Generalized Likelihood Ratio. In International Symposium on Artificial Intelligence and Signal Processing (pp. 172-180). Cham: Springer International Publishing. Doi: 10.1007/978-3-319-10849-0_18
Bentlemsan, M., Zemouri, E. T., Bouchaffra, D., Yahya-Zoubir, B., & Ferroudji, K. (2014, January). Random forest and filter bank common spatial patterns for EEG-based motor imagery classification. In 2014 5th International conference on intelligent systems, modelling and simulation (pp. 235-238). IEEE. Doi: 10.1109/ISMS.2014.46
Blanco-Diaz, C. F., da Silva Serafini, E. R., Bastos-Filho, T., de Azevedo Dantas, A. F. O., do Espirito Santo, C. C., & Delisle-Rodriguez, D. (2024). A gait imagery-based brain-computer interface with visual feedback for spinal cord injury rehabilitation on lokomat. IEEE Transactions on Biomedical Engineering. Doi: 10.1109/TBME.2024.3440036
Brunner, C., Leeb, R., Müller-Putz, G., Schlögl, A., & Pfurtscheller, G. (2008). BCI Competition 2008–Graz data set A. Institute for knowledge discovery (laboratory of brain-computer interfaces), Graz University of Technology, 16(1-6), 1. Doi: 10.21227/katb-zv89
Candra, H., Yuwono, M., Chai, R., Handojoseno, A., Elamvazuthi, I., Nguyen, H. T., & Su, S. (2015, August). Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine. In 2015 37th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 7250-7253). IEEE. Doi: 10.1109/EMBC.2015.7320065
Cantillo-Negrete, J., Carino-Escobar, R. I., Carrillo-Mora, P., Elias-Vinas, D., & Gutierrez-Martinez, J. (2018). Motor Imagery‐Based Brain‐Computer Interface Coupled to a Robotic Hand Orthosis Aimed for Neurorehabilitation of Stroke Patients. Journal of healthcare engineering, 2018(1), 1624637. Doi: 10.1155/2018/1624637
Chaer, M. S. I., Nugroho, A. P., Putra, G. M. D., Ngadisih, N., Sutiarso, L., & Okayasu, T. (2022, March). Early warning system using change point analysis to detect microclimate anomalies. In 2nd International Conference on Smart and Innovative Agriculture (ICoSIA 2021) (pp. 144-149). Atlantis Press. Doi: 10.2991/absr.k.220305.021
Chin, Z. Y., Ang, K. K., Guan, C., Wang, C., & Zhang, H. (2011, July). Filter Bank Feature Combination (FBFC) approach for brain-computer interface. In The 2011 International Joint Conference on Neural Networks (pp. 1352-1357). IEEE. 15. Chou, T. P., Wang, W. R., & Chang, T. S. (2015, July). Low complexity real time BCI for stroke rehabilitation. In 2015 IEEE International Conference on Digital Signal Processing (DSP) (pp. 809-812). IEEE. Doi: 10.1109/IJCNN.2011.6033381
Chou, T. P., Wang, W. R., & Chang, T. S. (2015, July). Low complexity real time BCI for stroke rehabilitation. In 2015 IEEE International Conference on Digital Signal Processing (DSP) (pp. 809-812). IEEE. Doi: 10.1109/ICDSP.2015.7251988
Chu, Y., Zhao, X., Zou, Y., Xu, W., Song, G., Han, J., & Zhao, Y. (2020). Decoding multiclass motor imagery EEG from the same upper limb by combining Riemannian geometry features and partial least squares regression. Journal of neural engineering, 17(4), 046029. Doi: 10.1088/1741-2552/aba7cd
Craik, A., He, Y., & Contreras-Vidal, J. L. (2019). Deep learning for electroencephalogram (EEG) classification tasks: a review. Journal of neural engineering, 16(3), 031001. Doi: 10.1088/1741-2552/ab0ab5
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Details

Primary Language	Turkish
Subjects	Computer Software, Software Engineering (Other)
Journal Section	Research Article
Authors	Tuğçe Ballı 0000-0002-6509-3725
Project Number	124E057
Submission Date	May 13, 2025
Acceptance Date	September 24, 2025
Early Pub Date	December 11, 2025
Publication Date	December 19, 2025
Published in Issue	Year 2025 Volume: 30 Issue: 3

Cite

APA	Ballı, T. (2025). MOTOR İMGELEME EEG SİNYALLERİNDE SINIFLANDIRMA PERFORMANSINI ARTTIRMAYA YÖNELİK ADAPTİF SEGMENTASYON YAKLAŞIMI. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 30(3), 711-730. https://doi.org/10.17482/uumfd.1698204
AMA	Ballı T. MOTOR İMGELEME EEG SİNYALLERİNDE SINIFLANDIRMA PERFORMANSINI ARTTIRMAYA YÖNELİK ADAPTİF SEGMENTASYON YAKLAŞIMI. UUJFE. December 2025;30(3):711-730. doi:10.17482/uumfd.1698204
Chicago	Ballı, Tuğçe. “MOTOR İMGELEME EEG SİNYALLERİNDE SINIFLANDIRMA PERFORMANSINI ARTTIRMAYA YÖNELİK ADAPTİF SEGMENTASYON YAKLAŞIMI”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 30, no. 3 (December 2025): 711-30. https://doi.org/10.17482/uumfd.1698204.
EndNote	Ballı T (December 1, 2025) MOTOR İMGELEME EEG SİNYALLERİNDE SINIFLANDIRMA PERFORMANSINI ARTTIRMAYA YÖNELİK ADAPTİF SEGMENTASYON YAKLAŞIMI. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 30 3 711–730.
IEEE	T. Ballı, “MOTOR İMGELEME EEG SİNYALLERİNDE SINIFLANDIRMA PERFORMANSINI ARTTIRMAYA YÖNELİK ADAPTİF SEGMENTASYON YAKLAŞIMI”, UUJFE, vol. 30, no. 3, pp. 711–730, 2025, doi: 10.17482/uumfd.1698204.
ISNAD	Ballı, Tuğçe. “MOTOR İMGELEME EEG SİNYALLERİNDE SINIFLANDIRMA PERFORMANSINI ARTTIRMAYA YÖNELİK ADAPTİF SEGMENTASYON YAKLAŞIMI”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 30/3 (December2025), 711-730. https://doi.org/10.17482/uumfd.1698204.
JAMA	Ballı T. MOTOR İMGELEME EEG SİNYALLERİNDE SINIFLANDIRMA PERFORMANSINI ARTTIRMAYA YÖNELİK ADAPTİF SEGMENTASYON YAKLAŞIMI. UUJFE. 2025;30:711–730.
MLA	Ballı, Tuğçe. “MOTOR İMGELEME EEG SİNYALLERİNDE SINIFLANDIRMA PERFORMANSINI ARTTIRMAYA YÖNELİK ADAPTİF SEGMENTASYON YAKLAŞIMI”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol. 30, no. 3, 2025, pp. 711-30, doi:10.17482/uumfd.1698204.
Vancouver	Ballı T. MOTOR İMGELEME EEG SİNYALLERİNDE SINIFLANDIRMA PERFORMANSINI ARTTIRMAYA YÖNELİK ADAPTİF SEGMENTASYON YAKLAŞIMI. UUJFE. 2025;30(3):711-30.

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