Araştırma Makalesi
BibTex RIS Kaynak Göster

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

Yıl 2025, Cilt: 30 Sayı: 3, 711 - 730, 19.12.2025

Tuğçe Ballı

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

Öz

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%.

Anahtar Kelimeler

EEG Motor Imagery Change Point Detection Fixed Point Segmentation Classification

Destekleyen Kurum

TÜBİTAK

Proje Numarası

124E057

Teşekkür

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

Kaynakça

  • Abbas, W., & Khan, N. A. (2018, July). FBCSP-based multi-class motor imagery classification using BP and TDP features. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 215-218). IEEE. Doi: 10.1109/EMBC.2018.8512238
  • 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
  • D'Croz-Baron, D., Ramirez, J. M., Baker, M., Alarcon-Aquino, V., & Carrera, O. (2012, February). A BCI motor imagery experiment based on parametric feature extraction and fisher criterion. In CONIELECOMP 2012, 22nd International Conference on Electrical Communications and Computers (pp. 257-261). IEEE. Doi: 10.1109/CONIELECOMP.2012.6189920
  • Dagdevir, E., & Tokmakci, M. (2023). Determination of effective signal processing stages for brain computer interface on BCI competition IV data set 2b: a review study. IETE Journal of Research, 69(6), 3144-3155. Doi: 10.1080/03772063.2021.1914204
  • Dreyer, P., Roc, A., Pillette, L., Rimbert, S., & Lotte, F. (2023). A large EEG database with users’ profile information for motor imagery brain-computer interface research. Scientific Data, 10(1), 580. Doi: 10.1038/s41597-023-02445-z
  • Esri. (t.y.). How change point detection works. ArcGIS Pro documentation. https://pro.arcgis.com/en/pro-app/latest/tool-reference/space-time-pattern-mining/how-change-point-detection-works.htm
  • Fang, H., Jin, J., Daly, I., & Wang, X. (2022). Feature extraction method based on filter banks and Riemannian tangent space in motor-imagery BCI. IEEE journal of biomedical and health informatics, 26(6), 2504-2514. Doi: 10.1109/JBHI.2022.3146274
  • Ferdi, A. Y., & Ghazli, A. (2025). Mind-Controlled Web Browser Navigation Based on Brain Computer Interfaces. Algerian Journal of Signals and Systems, 10(1), 24-28. Doi: 10.51485/ajss.v10i1.260
  • Firoz, K. F., Seong, Y., & Yi, S. (2024). A preliminary study of neural signals of motor imagery task of arm movements through electroencephalography data classification with machine learning. In Proceedings of the 2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS) (pp. 1–6). IEEE. Doi: 10.1109/ICHMS59971.2024.10555801
  • Gao Z, Lu G, Yan P, Lyu C, Li X, Shang W, Xie Z, Zhang W. (2018). Automatic change detection for real-time monitoring of EEG signals. Frontiers in physiology, 9, 325. Doi: 10.3389/fphys.2018.00325
  • Góngora, L., Paglialonga, A., Mastropietro, A., Rizzo, G., & Barbieri, R. (2022). A novel approach for segment-length selection based on stationarity to perform effective connectivity analysis applied to resting-state eeg signals. Sensors, 22(13), 4747. Doi: 10.3390/s22134747
  • González-Cely, A. X., Soekadar, S. R., Delisle-Rodriguez, D., & Bastos-Filho, T. (2025, May). Lower-Limb Motor Imagery-Based Brain-Computer Interface to Control Treadmill Velocities. In 2025 International Conference On Rehabilitation Robotics (ICORR) (pp. 76-81). IEEE. Doi: 10.1109/ICORR66766.2025.11063181
  • Hamavar, R., & Asl, B. M. (2025). Feature selection based on game theory optimization to achieve desired performance metrics in seizure onset detection. Biomedical Signal Processing and Control, 100, 107008. Doi: 10.1016/j.bspc.2024.107008
  • Hamner, B., Leeb, R., Tavella, M., & Millán, J. D. R. (2011, August). Phase-based features for motor imagery brain-computer interfaces. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 2578-2581). IEEE. Doi: 10.1109/IEMBS.2011.6090712
  • Hou, Y., Chen, T., Lun, X., & Wang, F. (2022). A novel method for classification of multi-class motor imagery tasks based on feature fusion. Neuroscience Research, 176, 40-48. Doi: 10.1016/j.neures.2021.09.002
  • Isa, N. M., Amir, A., Ilyas, M. Z., & Razalli, M. S. (2019). Motor imagery classification in Brain computer interface (BCI) based on EEG signal by using machine learning technique. Bulletin of Electrical Engineering and Informatics, 8(1), 269-275. Doi: 10.11591/eei.v8i1.1402
  • Jin, L., Song, Y., Zhao, H., Cao, J., Cheung, V. C., & Liao, W. H. (2025). Frequency-Aware Spatial-Temporal Attention Explainable Network for EEG Decoding. IEEE Journal of Biomedical and Health Informatics. Doi: 10.1109/JBHI.2025.3576088
  • Jo, S., Jung, J. H., Yang, M. J., Lee, Y., Jang, S. J., Feng, J., Heo, S., Kim, J., Shin, J., Jeon, J. & Park, H. S. (2022, July). EEG-EMG hybrid real-time classification of hand grasp and release movements intention in chronic stroke patients. In 2022 International Conference on Rehabilitation Robotics (ICORR) (pp. 1-6). IEEE. Doi: 10.1109/ICORR55369.2022.9896592
  • Joy, M. M. H., Hasan, M., Miah, A. S. M., Ahmed, A., Tohfa, S. A., Bhuaiyan, M. F. I., Zannat, A. & Rashid, M. M. (2020, March). Multiclass mi-task classification using logistic regression and filter bank common spatial patterns. In International Conference on Computing Science, Communication and Security (pp. 160-170). Singapore: Springer Singapore. Doi: 10.1007/978-981-15-6648-6_13
  • Kaplan, A., Röschke, J., Darkhovsky, B., & Fell, J. (2001). Macrostructural EEG characterization based on nonparametric change point segmentation: application to sleep analysis. Journal of neuroscience methods, 106(1), 81-90. Doi: 10.1016/S0165-0270(01)00331-4
  • Keković, G., & Sekulić, S. (2019). Detection of change points in time series with moving average filters and wavelet transform: Application to EEG signals. Neurophysiology, 51, 2-8. Doi: 10.1007/s11062-019-09783-y
  • Killick, R., Fearnhead, P., & Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590-1598. Doi: 10.1080/01621459.2012.737745
  • Kim, K., Park, J. H., Lee, M., & Song, J. W. (2022). Unsupervised change point detection and trend prediction for financial time-series using a new cusum-based approach. IEEE Access, 10, 34690-34705. Doi: 10.1109/ACCESS.2022.3162399
  • Kirch, C., Muhsal, B., & Ombao, H. (2015). Detection of changes in multivariate time series with application to EEG data. Journal of the American Statistical Association, 110(511), 1197-1216. Doi: 10.1080/01621459.2014.957545
  • Koles, Z. J., Lazar, M. S., & Zhou, S. Z. (1990). Spatial patterns underlying population differences in the background EEG. Brain topography, 2(4), 275-284. Doi: 10.1007/BF01129656
  • Maarouf, K., Alzahab, N. A., & Saad, G. (2024, October). The Effect of EEG Segment's Length on Mental Workload Detection. In 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (pp. 271-276). IEEE. Doi: 10.1109/MetroXRAINE62247.2024.10795942
  • Malladi, R., Kalamangalam, G. P., & Aazhang, B. (2013, November). Online Bayesian change point detection algorithms for segmentation of epileptic activity. In 2013 Asilomar conference on signals, systems and computers (pp. 1833-1837). IEEE. Doi: 10.1109/ACSSC.2013.6810619
  • Manca, G., & Fay, A. (2022, March). Off-line detection of abrupt and transitional change points in industrial process signals. In 2022 4th International Conference on Applied Automation and Industrial Diagnostics (ICAAID) (Vol. 1, pp. 1-6). IEEE. Doi: 10.1109/ICAAID51067.2022.9799507
  • Rosiani, U. D., Saputra, P. Y., & Hendrawan, M. A. (2021, October). The Impact of Segment Length on EEG Based Biometric System. In 2021 IEEE 7th Information Technology International Seminar (ITIS) (pp. 1-5). IEEE. Doi: 10.1109/ITIS53497.2021.9791686
  • Sarma, P., & Barma, S. (2021). Emotion recognition by distinguishing appropriate EEG segments based on random matrix theory. Biomedical Signal Processing and Control, 70, 102991. Doi: 10.1016/j.bspc.2021.102991
  • Schröder, A. L., & Ombao, H. (2019). FreSpeD: Frequency-specific change-point detection in epileptic seizure multi-channel EEG data. Journal of the American Statistical Association, 114(525), 115-128. Doi: 10.1080/01621459.2018.1476238
  • Shahlaei, F., Bagh, N., Shaligram, A. D., Reddy, M. R., & Zambare, M. S. (2018, June). Classification of motor imagery tasks using inter trial variance in the brain computer interface. In 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (pp. 1-6). IEEE. Doi: 10.1109/MeMeA.2018.8438648
  • Steyrl, D., Scherer, R., Faller, J., & Müller-Putz, G. R. (2016). Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier. Biomedical Engineering/Biomedizinische Technik, 61(1),77-86. Doi: 10.1515/bmt-2014-0117
  • Subasi, A., & Mian Qaisar, S. (2021). The Ensemble Machine Learning‐Based Classification of Motor Imagery Tasks in Brain‐Computer Interface. Journal of Healthcare Engineering, 2021(1), 1970769. Doi: 10.1155/2021/1970769
  • Truong, C., Oudre, L., & Vayatis, N. (2020). Selective review of offline change point detection methods. Signal Processing, 167, 107299. Doi: 10.1016/j.sigpro.2019.107299
  • Vidaurre, C., Sannelli, C., Müller, K. R., & Blankertz, B. (2010, June). Machine-learning based co-adaptive calibration: a perspective to fight BCI illiteracy. In International Conference on Hybrid Artificial Intelligence Systems (pp. 413-420). Berlin, Heidelberg: Springer Berlin Heidelberg. Doi: 10.1007/978-3-642-13769-3_50
  • Vuckovic, A., Wallace, L., & Allan, D. B. (2015). Hybrid brain-computer interface and functional electrical stimulation for sensorimotor training in participants with tetraplegia: a proof-of-concept study. Journal of neurologic physical therapy, 39(1), 3-14. Doi: 10.1097/NPT.0000000000000063
  • Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., & Zhu, J. (2019, September). Explainable AI: A brief survey on history, research areas, approaches and challenges. In CCF international conference on natural language processing and Chinese computing (pp. 563-574). Cham: Springer International Publishing. Doi: 10.1007/978-3-030-32236-6_51
  • Yang, J., Ma, Z., & Shen, T. (2021). Multi-time and multi-band CSP motor imagery EEG feature classification algorithm. Applied Sciences, 11(21), 10294. Doi: 10.3390/app112110294
  • Zhang, C., & Eskandarian, A. (2020, July). A computationally efficient multiclass time-frequency common spatial pattern analysis on EEG motor imagery. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 514-518). IEEE. Doi: 10.1109/EMBC44109.2020.9176705
  • Zhang, R., Xiao, X., Liu, Z., Jiang, W., Li, J., Cao, Y., Ren, J., Jiang, D. & Cui, L. (2018). A new motor imagery EEG classification method FB-TRCSP+ RF based on CSP and random forest. IEEE Access, 6, 44944-44950. Doi: 10.1109/ACCESS.2018.2860633

MOTOR İMGELEME EEG SİNYALLERİNDE SINIFLANDIRMA PERFORMANSINI ARTTIRMAYA YÖNELİK ADAPTİF SEGMENTASYON YAKLAŞIMI

Yıl 2025, Cilt: 30 Sayı: 3, 711 - 730, 19.12.2025

Tuğçe Ballı

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

Öz

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.

Anahtar Kelimeler

EEG Motor İmgeleme Değişim Noktası Tespiti Sabit Süreli Segmentasyon Sınıflandırma

Destekleyen Kurum

TÜBİTAK

Proje Numarası

124E057

Teşekkür

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

Kaynakça

  • Abbas, W., & Khan, N. A. (2018, July). FBCSP-based multi-class motor imagery classification using BP and TDP features. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 215-218). IEEE. Doi: 10.1109/EMBC.2018.8512238
  • 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
  • D'Croz-Baron, D., Ramirez, J. M., Baker, M., Alarcon-Aquino, V., & Carrera, O. (2012, February). A BCI motor imagery experiment based on parametric feature extraction and fisher criterion. In CONIELECOMP 2012, 22nd International Conference on Electrical Communications and Computers (pp. 257-261). IEEE. Doi: 10.1109/CONIELECOMP.2012.6189920
  • Dagdevir, E., & Tokmakci, M. (2023). Determination of effective signal processing stages for brain computer interface on BCI competition IV data set 2b: a review study. IETE Journal of Research, 69(6), 3144-3155. Doi: 10.1080/03772063.2021.1914204
  • Dreyer, P., Roc, A., Pillette, L., Rimbert, S., & Lotte, F. (2023). A large EEG database with users’ profile information for motor imagery brain-computer interface research. Scientific Data, 10(1), 580. Doi: 10.1038/s41597-023-02445-z
  • Esri. (t.y.). How change point detection works. ArcGIS Pro documentation. https://pro.arcgis.com/en/pro-app/latest/tool-reference/space-time-pattern-mining/how-change-point-detection-works.htm
  • Fang, H., Jin, J., Daly, I., & Wang, X. (2022). Feature extraction method based on filter banks and Riemannian tangent space in motor-imagery BCI. IEEE journal of biomedical and health informatics, 26(6), 2504-2514. Doi: 10.1109/JBHI.2022.3146274
  • Ferdi, A. Y., & Ghazli, A. (2025). Mind-Controlled Web Browser Navigation Based on Brain Computer Interfaces. Algerian Journal of Signals and Systems, 10(1), 24-28. Doi: 10.51485/ajss.v10i1.260
  • Firoz, K. F., Seong, Y., & Yi, S. (2024). A preliminary study of neural signals of motor imagery task of arm movements through electroencephalography data classification with machine learning. In Proceedings of the 2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS) (pp. 1–6). IEEE. Doi: 10.1109/ICHMS59971.2024.10555801
  • Gao Z, Lu G, Yan P, Lyu C, Li X, Shang W, Xie Z, Zhang W. (2018). Automatic change detection for real-time monitoring of EEG signals. Frontiers in physiology, 9, 325. Doi: 10.3389/fphys.2018.00325
  • Góngora, L., Paglialonga, A., Mastropietro, A., Rizzo, G., & Barbieri, R. (2022). A novel approach for segment-length selection based on stationarity to perform effective connectivity analysis applied to resting-state eeg signals. Sensors, 22(13), 4747. Doi: 10.3390/s22134747
  • González-Cely, A. X., Soekadar, S. R., Delisle-Rodriguez, D., & Bastos-Filho, T. (2025, May). Lower-Limb Motor Imagery-Based Brain-Computer Interface to Control Treadmill Velocities. In 2025 International Conference On Rehabilitation Robotics (ICORR) (pp. 76-81). IEEE. Doi: 10.1109/ICORR66766.2025.11063181
  • Hamavar, R., & Asl, B. M. (2025). Feature selection based on game theory optimization to achieve desired performance metrics in seizure onset detection. Biomedical Signal Processing and Control, 100, 107008. Doi: 10.1016/j.bspc.2024.107008
  • Hamner, B., Leeb, R., Tavella, M., & Millán, J. D. R. (2011, August). Phase-based features for motor imagery brain-computer interfaces. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 2578-2581). IEEE. Doi: 10.1109/IEMBS.2011.6090712
  • Hou, Y., Chen, T., Lun, X., & Wang, F. (2022). A novel method for classification of multi-class motor imagery tasks based on feature fusion. Neuroscience Research, 176, 40-48. Doi: 10.1016/j.neures.2021.09.002
  • Isa, N. M., Amir, A., Ilyas, M. Z., & Razalli, M. S. (2019). Motor imagery classification in Brain computer interface (BCI) based on EEG signal by using machine learning technique. Bulletin of Electrical Engineering and Informatics, 8(1), 269-275. Doi: 10.11591/eei.v8i1.1402
  • Jin, L., Song, Y., Zhao, H., Cao, J., Cheung, V. C., & Liao, W. H. (2025). Frequency-Aware Spatial-Temporal Attention Explainable Network for EEG Decoding. IEEE Journal of Biomedical and Health Informatics. Doi: 10.1109/JBHI.2025.3576088
  • Jo, S., Jung, J. H., Yang, M. J., Lee, Y., Jang, S. J., Feng, J., Heo, S., Kim, J., Shin, J., Jeon, J. & Park, H. S. (2022, July). EEG-EMG hybrid real-time classification of hand grasp and release movements intention in chronic stroke patients. In 2022 International Conference on Rehabilitation Robotics (ICORR) (pp. 1-6). IEEE. Doi: 10.1109/ICORR55369.2022.9896592
  • Joy, M. M. H., Hasan, M., Miah, A. S. M., Ahmed, A., Tohfa, S. A., Bhuaiyan, M. F. I., Zannat, A. & Rashid, M. M. (2020, March). Multiclass mi-task classification using logistic regression and filter bank common spatial patterns. In International Conference on Computing Science, Communication and Security (pp. 160-170). Singapore: Springer Singapore. Doi: 10.1007/978-981-15-6648-6_13
  • Kaplan, A., Röschke, J., Darkhovsky, B., & Fell, J. (2001). Macrostructural EEG characterization based on nonparametric change point segmentation: application to sleep analysis. Journal of neuroscience methods, 106(1), 81-90. Doi: 10.1016/S0165-0270(01)00331-4
  • Keković, G., & Sekulić, S. (2019). Detection of change points in time series with moving average filters and wavelet transform: Application to EEG signals. Neurophysiology, 51, 2-8. Doi: 10.1007/s11062-019-09783-y
  • Killick, R., Fearnhead, P., & Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590-1598. Doi: 10.1080/01621459.2012.737745
  • Kim, K., Park, J. H., Lee, M., & Song, J. W. (2022). Unsupervised change point detection and trend prediction for financial time-series using a new cusum-based approach. IEEE Access, 10, 34690-34705. Doi: 10.1109/ACCESS.2022.3162399
  • Kirch, C., Muhsal, B., & Ombao, H. (2015). Detection of changes in multivariate time series with application to EEG data. Journal of the American Statistical Association, 110(511), 1197-1216. Doi: 10.1080/01621459.2014.957545
  • Koles, Z. J., Lazar, M. S., & Zhou, S. Z. (1990). Spatial patterns underlying population differences in the background EEG. Brain topography, 2(4), 275-284. Doi: 10.1007/BF01129656
  • Maarouf, K., Alzahab, N. A., & Saad, G. (2024, October). The Effect of EEG Segment's Length on Mental Workload Detection. In 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (pp. 271-276). IEEE. Doi: 10.1109/MetroXRAINE62247.2024.10795942
  • Malladi, R., Kalamangalam, G. P., & Aazhang, B. (2013, November). Online Bayesian change point detection algorithms for segmentation of epileptic activity. In 2013 Asilomar conference on signals, systems and computers (pp. 1833-1837). IEEE. Doi: 10.1109/ACSSC.2013.6810619
  • Manca, G., & Fay, A. (2022, March). Off-line detection of abrupt and transitional change points in industrial process signals. In 2022 4th International Conference on Applied Automation and Industrial Diagnostics (ICAAID) (Vol. 1, pp. 1-6). IEEE. Doi: 10.1109/ICAAID51067.2022.9799507
  • Rosiani, U. D., Saputra, P. Y., & Hendrawan, M. A. (2021, October). The Impact of Segment Length on EEG Based Biometric System. In 2021 IEEE 7th Information Technology International Seminar (ITIS) (pp. 1-5). IEEE. Doi: 10.1109/ITIS53497.2021.9791686
  • Sarma, P., & Barma, S. (2021). Emotion recognition by distinguishing appropriate EEG segments based on random matrix theory. Biomedical Signal Processing and Control, 70, 102991. Doi: 10.1016/j.bspc.2021.102991
  • Schröder, A. L., & Ombao, H. (2019). FreSpeD: Frequency-specific change-point detection in epileptic seizure multi-channel EEG data. Journal of the American Statistical Association, 114(525), 115-128. Doi: 10.1080/01621459.2018.1476238
  • Shahlaei, F., Bagh, N., Shaligram, A. D., Reddy, M. R., & Zambare, M. S. (2018, June). Classification of motor imagery tasks using inter trial variance in the brain computer interface. In 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (pp. 1-6). IEEE. Doi: 10.1109/MeMeA.2018.8438648
  • Steyrl, D., Scherer, R., Faller, J., & Müller-Putz, G. R. (2016). Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier. Biomedical Engineering/Biomedizinische Technik, 61(1),77-86. Doi: 10.1515/bmt-2014-0117
  • Subasi, A., & Mian Qaisar, S. (2021). The Ensemble Machine Learning‐Based Classification of Motor Imagery Tasks in Brain‐Computer Interface. Journal of Healthcare Engineering, 2021(1), 1970769. Doi: 10.1155/2021/1970769
  • Truong, C., Oudre, L., & Vayatis, N. (2020). Selective review of offline change point detection methods. Signal Processing, 167, 107299. Doi: 10.1016/j.sigpro.2019.107299
  • Vidaurre, C., Sannelli, C., Müller, K. R., & Blankertz, B. (2010, June). Machine-learning based co-adaptive calibration: a perspective to fight BCI illiteracy. In International Conference on Hybrid Artificial Intelligence Systems (pp. 413-420). Berlin, Heidelberg: Springer Berlin Heidelberg. Doi: 10.1007/978-3-642-13769-3_50
  • Vuckovic, A., Wallace, L., & Allan, D. B. (2015). Hybrid brain-computer interface and functional electrical stimulation for sensorimotor training in participants with tetraplegia: a proof-of-concept study. Journal of neurologic physical therapy, 39(1), 3-14. Doi: 10.1097/NPT.0000000000000063
  • Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., & Zhu, J. (2019, September). Explainable AI: A brief survey on history, research areas, approaches and challenges. In CCF international conference on natural language processing and Chinese computing (pp. 563-574). Cham: Springer International Publishing. Doi: 10.1007/978-3-030-32236-6_51
  • Yang, J., Ma, Z., & Shen, T. (2021). Multi-time and multi-band CSP motor imagery EEG feature classification algorithm. Applied Sciences, 11(21), 10294. Doi: 10.3390/app112110294
  • Zhang, C., & Eskandarian, A. (2020, July). A computationally efficient multiclass time-frequency common spatial pattern analysis on EEG motor imagery. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 514-518). IEEE. Doi: 10.1109/EMBC44109.2020.9176705
  • Zhang, R., Xiao, X., Liu, Z., Jiang, W., Li, J., Cao, Y., Ren, J., Jiang, D. & Cui, L. (2018). A new motor imagery EEG classification method FB-TRCSP+ RF based on CSP and random forest. IEEE Access, 6, 44944-44950. Doi: 10.1109/ACCESS.2018.2860633
Toplam 56 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı, Yazılım Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Tuğçe Ballı 0000-0002-6509-3725

Proje Numarası 124E057
Gönderilme Tarihi 13 Mayıs 2025
Kabul Tarihi 24 Eylül 2025
Erken Görünüm Tarihi 11 Aralık 2025
Yayımlanma Tarihi 19 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 30 Sayı: 3

Kaynak Göster

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. Aralık 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, sy. 3 (Aralık 2025): 711-30. https://doi.org/10.17482/uumfd.1698204.
EndNote Ballı T (01 Aralık 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, c. 30, sy. 3, ss. 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 (Aralık2025), 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, c. 30, sy. 3, 2025, ss. 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.

DUYURU:

30.03.2021- Nisan 2021 (26/1) sayımızdan itibaren TR-Dizin yeni kuralları gereği, dergimizde basılacak makalelerde, ilk gönderim aşamasında Telif Hakkı Formu yanısıra, Çıkar Çatışması Bildirim Formu ve Yazar Katkısı Bildirim Formu da tüm yazarlarca imzalanarak gönderilmelidir. Yayınlanacak makalelerde de makale metni içinde "Çıkar Çatışması" ve "Yazar Katkısı" bölümleri yer alacaktır. İlk gönderim aşamasında doldurulması gereken yeni formlara "Yazım Kuralları" ve "Makale Gönderim Süreci" sayfalarımızdan ulaşılabilir. (Değerlendirme süreci bu tarihten önce tamamlanıp basımı bekleyen makalelerin yanısıra değerlendirme süreci devam eden makaleler için, yazarlar tarafından ilgili formlar doldurularak sisteme yüklenmelidir).  Makale şablonları da, bu değişiklik doğrultusunda güncellenmiştir. Tüm yazarlarımıza önemle duyurulur.

Bursa Uludağ Üniversitesi, Mühendislik Fakültesi Dekanlığı, Görükle Kampüsü, Nilüfer, 16059 Bursa. Tel: (224) 294 1907, Faks: (224) 294 1903, e-posta: mmfd@uludag.edu.tr