Sinyal Segmentlerinden Elde Edilen Özniteliklerin Sınıflandırma Başarısına Etkisi Üzerine Bir Araştırma

Başarılı sınıflandırma, ayırt edici özniteliklerin ve sınıflandırmada kullanılan etkin kanal alt kümesinin seçimine bağlıdır. Bu çalışmada, EEG gibi çok kanallı sınıflandırma sistemlerinde ayırıcı özniteliklerin belirlenmesi ve etkin kanal alt kümelerinin saptanması için yeni ve pratik yöntemler önerilmiş ve iki farklı öznitelik çıkarma yöntemi karşılaştırılmıştır. Bunlardan ilki, klasik Dalgacık dönüşümüne ve ikincisi de sinyal segmentlerinin eğimini kullanan önerilen yaklaşımımızdır. Klasik Dalgacık dönüşümü tabanlı öznitelik çıkarma yöntemi için Dalgacık katsayılarının ortalama, standart sapma, sayısal integrali gibi bazı sinyal özelliklerinden öznitelik vektörleri üretilir. Önerilen, Sinyal Yolu Eğimi (SPS) yöntemi için ise öznitelik vektörleri sadece sinyal segmentlerinin eğimlerinden oluşmaktadır. Önerdiğimiz öznitelik çıkarma yönteminde, klasik Dalgacık tabanlı yöntemden farklı olarak, segmentasyondan önce sinyale zaman domeninde optimal çerçeve uzunluğuna sahip bir Savitzky Golay (SG) filtresi uygulanarak sinyal yolunun daha belirgin hale getirilmesi sağlanmıştır. Bu sayede SG filtresi kullanılarak ayırt edici sınıflandırma öznitelikleri çıkarılmaktadır. Kanal seçimi için, eğitim veri kümesi %90 ön eğitim ve %10 ön test verisi olarak iki gruba ayıran iteratif bir kanal seçim yöntemi önerilmiştir. Çalışmada BCI yarışması IV'te sunulan veri seti-3 kullanılmıştır. Önerilen yöntemler kullanılarak çıkarılan öznitelik vektörleri Destek Vektör makinesi sınıflandırıcısına tabi tutulmuştur. Sonuçlar karşılaştırmalı olarak verilmiş ve önerilen yöntemimizin Wavelet tabanlı klasik öznitelik çıkarma yöntemlerine göre daha az hesaplama karmaşıklığına ve daha başarılı sınıflandırma kabiliyetine sahip olduğu gözlemlenmiştir. Denek-1 ve denek-2 için sırasıyla % 67.74 ve % 49.27 olan en yüksek sınıflandırma doğruluğu, önerilen SPS öznitelik çıkarma yöntemi ile düşük boyutlu bir öznitelik vektörü ile elde edilmiştir. Çalışmada elde edilen sınıflandırma başarımı, yarışma elde edilen sonuçlarla karşılaştırıldığında, denek-1 için % 8.24 ve denek-2 için % 14.97 oranında sınıflandırma başarısı artışı gözlemlenmiştir. Her iki denek için de başarıdaki önemli artış, önerilen yöntemlerin tutarlılığını göstermektedir. Bu çalışma ile beyinde motor imgeleme görevleriyle ilgili deneğe özgü bir sinyal örüntüsü olduğu gözlemlenmiştir. Bu örüntünün ayırt edici özellikleri önerilen yöntemler kullanılarak başarılı bir şekilde tespit edilmiştir.

Anahtar Kelimeler:

Beyin Bilgisayar Arayüzü, Sınıflandırma, Öznitelik Vektörü, Kanal Seçimi, Savitzky Golay

A Study on the Effect of Features Obtained From Signal Segments on Classification Success

Successful classification depends on the selection of the distinctive features and the effective channel subset used in the classification. In this study, novel and practical methods are proposed for determining the distinctive features and detecting effective channel subsets in the multi channel classification systems such as EEG. Two different feature extraction methods are compared in the study. The first one is based on classical Wavelet transform and the second is our proposed approach which used the slope of signal segments. Feature vectors are generated from some signal properties such as the mean, standard deviation, numerical integral of the Wavelet coefficients for classical Wavelet transform based feature extraction method. For our proposed method, only the slopes of signal segments are used for the feature vectors. In the proposed Signal Path Slope (SPS) feature extraction method, differently from the classical Wavelet based method, a Savitzky Golay (S-G) filter with an optimal frame length is applied to the signal before segmentation to make the path of the signal more prominent in time domain. In this way, the distinctive classification features are extracted by using S-G filter. For channel selection, an iterative channel selection method based on the classification results which divide the dataset labelled dataset into two groups as % 90 pre-training and %10 pre-test data is proposed. The dataset provided as dataset-3 in BCI competition IV is used in this study. The feature vectors extracted by using the proposed methods are classified for each method with the Support Vector Machine classifier. The results are given comparatively and it is observed that our proposed method has less computational complexity and more successful classification than Wavelet based classical feature extraction methods. The highest classification accuracies of % 67.74 and % 49.27 for subject-1 and subject-2 respectively are obtained with a low dimensional feature vector by proposed SPS feature extraction method. The classification accuracies achieved in the study are increased by % 8.24 for subject-1 and % 14.97 for subject-2 when compared average of the competition results. The significant increase in the success for both subjects shows the consistency of the proposed methods. By this study, it is observed that there is a subject-specific signal pattern related to motor imagery tasks in the brain. This pattern distinctive features is successfully determined by using the proposed methods.

Keywords:

Brain Computer Interface, Classification, Feature Vector, Channel Selection, Savitzky Golay,

PDF

___

G. Pfurtscheller, C. Neuper, N. Birbaumer, “Human brain-computer interface (BCI),” FIn:Riehle A, Vaadia E, editors. A distributed system for distributed functions, Motor Cortex in Voluntary Movements,pp. 367–401, 2005.
G. S. Sagee, S. Hema, “EEG feature extraction and classification in multiclass multiuser motor imagery brain computer interface using Bayesian Network and ANN,” Intelligent Computing, Instrumentation and Control Technologies (ICICICT) International Conference on. IEEE, 2017.
H. K. Lee, Y.S. Choi, “A convolution neural networks scheme for classification of motor imagery EEG based on wavelet time-frequecy image,” Intelligent Computing, Information Networking (ICOIN) International Conference on. IEEE, 2018.
N. Lu , T. Li, X. Ren, H. Miao, “A deep learning scheme for motor imagery classification based on restricted Boltzmann machines,” IEEE Trans Neural Systems Rehabil Eng, vol. 25, pp. 566–76, 2017.
A. S. Al-Fahoum, A. A. Al-Fraihat, “Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains,” ISRN neuroscience, January 2014.
C.Y. Chen, C. W. Wu, C. T. Lin, S. A. Chen, “A novel classification method for motor imagery based on brain-computer interface,” Neural Networks (IJCNN), July 2014.
P. Gaur, R. B. Pachori, H. Wang, G. Prasad, “Empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface,” Neural Networks (IJCNN), July 2015.
J. S. Kirar, R. K. Agrawal, “Relevant feature selection from a combination of spectraltemporal and spatial features for classification of motor imagery EEG,”J. Med Syst, pp.47–78, 2018.
T. Chivalai, “Increase performance of four-class classification for motorimagery based brain-computer interface,” Computer, information and telecommunication systems (CITS), July 2014.
T. Alotaiby, F. El-Samie, S. Alshebeili, I. Ahmad, “A review of channel selectionalgorithms for EEG signal processing,” EURASIP J. Adv. Signal Process, vol. 1, pp. 66–86, 2015.
E. Erkan, I. Kurnaz, “A study on the effect of psychophysiological signal features on classification methods,” Measurements in Biology and Medicine, vol. 101, pp. 45–52, January 2017.
H. Choubey, A. Pandey, “A combination of statistical parameters for the detection of epilepsy and EEG classification using ANN and KNN classifier,” SIViP, vol. 15, pp. 475–483, 2021.
R. Aler, I. M. Galvan, J. M. Valls, “Transition detection for brain computer interface classification,” International joint conference on biomedical engineering systems and technologies, Berlin, January 2009.
R. Aler, I. M. Galvan, J. M. Valls, “Evolving spatial and frequency selection filters for brain-computer interfaces,” Evolutionary Computation (CEC), July 2010.
C. Schuldt, I. Laptev, B. Caputo, “Recognizing Human Actions: A Local ¨ SVM Approach,” In Proc. CVPR, vol. 3, pp. 32–36, March 2004.
A. Anuragi, D. S. Sisodia, “Empirical wavelet transform based automated alcoholism detecting using EEG signal features,” Biomedical Signal Processing and Control, vol. 57, pp. 1746–36, 2020.
S.Z. Zahid, M. Aqil, M. Tufail, M.S. Nazir, “Online Classification of Multiple Motor Imagery Tasks Using Filter Bank Based Maximum-aPosteriori Common Spatial Pattern Filters,”IRBM, pp.141–150, 2020.
P. Gaur, H. Gupta, A. Chowdhury, K. McCreadie, R. B. Pachori, H. Wang, “A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI,”IEEE Transactions on Instrumentation and Measurement, vol. 70, pp.1–9, 2021.
M. Hamedi, S. H. Salleh, A. M. Noor, I. Mohammad-Rezazadeh, “Neural network-based three-class motor imagery classification using time-domain features for BCI applications,” Region 10 Symposium, pp.14–16, April 2014.
S. K. Agarwal, S. Shah, R. Kumar, “Classification of mental tasks from EEG data using backtracking search optimization based neural classifier,” Neurocomputing, vol. 166, pp. 397–403, 2015.
J. Zhang, C. Yan, X. Gong, “Deep convolutional neural network for decoding motor imagery based brain computer interface, Signal Processing,” Communications and Computing (ICSPCC), October 2017.
S. Sakhavi, C. Guan, S. Yan, “Parallel convolutional-linear neural network for motor imagery classification, Signal Processing Conference (EUSIPCO), 2015.
W. Ko, J. Yoon, E. Kang, E. Jun, J.S. Choi, H.I. Suk, “Deep recurrent spatio-temporal neural network for motor imagery based BCI,” Braincomputer interface (BCI), January 2018.
S. Sakhavi, C. Guan, S. Yan, “Learning temporal information for brain-computer interface using convolutional neural networks,” . IEEE Transactions on Neural Networks and Learning Systems, pp. 5619–5629, March 2018.
A. Savitzky, M. J. E. Golay, “Smoothing and Differentiation of Data by Simplified Least Squares Procedures,” Anal Chem., vol. 36, pp. 1627– 1639, 1964.
S. Julius, C. C. Wright, “The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requirements,” Physical Therapy, vol. 85, pp. 257–268, March 2005.
S. H. Sardouie, M. B. Shamsollahi, ”Selection of efficient features for discrimination of hand movements from MEG using a BCIcompetition IV dataset,” Frontiers in Neuroscience, vol. 6(42), July 2012.
M. Tangermann, K. R. Muller,A. Aertsen, N. Birbaumer, C. B, C. ¨ Brunner, R. Leeb, C. Mehring,1 K. J. Miller, G. R. Muller-Putz, G. ¨ Nolte, G. Pfurtscheller, H. Preissl, G. Schalk, A. Schlogl, C. Vidaurre, ¨ S. Waldert, B. Blankertz, “Review of the BCI competition IV,” Frontiers in Neuroscience, vol. 6(55), January 2012.
A. N. Belkacem, H. Hirose, N. Yoshimura, D. Shin, Y. Koike, “Classification of Four EyeDirections from EEG Signals for Eye-MovementBased Communication Systems,” Journal of Medical and Biological Engineering, vol. 34(6), pp. 581–508, October 2013.
H. Li, R. Lan, N. Peng, J. Sun, Y. Zhu, “High resolution melting curve analysis with MATLAB-based program,” Measurement, vol. 90, pp. 178–186, Agust 2016.
S. Hargittai, “Savitzky-Golay least-squares polynomial filters in ECG signal processing,” Computers in Cardiology, September 2005.
S. Agarwa, A. Rani, V. Singh, A. .P.Mittal, “EEG signal enhancement using cascaded S-Golay filter,” Biomedical Signal Processing and Control, vol. 36, pp. 194–204, July 2017.
V. Gandhi, G. Prasad, D. Coyle, L. Behera, T. M. McGinnity, “Quantum Neural Network-Based EEG Filtering for a Brain–Computer Interface,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25(2), pp. 278–288, Agust 2013.
B. Kaur, D. Singh, P. P. Roy, “A Novel framework of EEG-based user identification by analyzing music-listening behavior,” Multimedia Tools and Applications, vol. 76, pp. 25581–25602, December 2017.
M. Alam, S. Basak, Md. I. Islam, “Fingerprint Detection Applying Discrete Wavelet Transform on ROI,” International Journal of Scientific and Engineering Research, vol. 3(6), pp. 1360–1364, June 2012.
M. Balasubramanian, S. Palanivel, V.Ramalingam, “Real time face and mouth recognition using radial basis function neural networks,” Expert Systems with Applications, vol. 36(3), pp. 6879–6888, April 2009.
S. Madabusi, V. Srinivas, S. Bhaskaran, M. Balasubramanian, “Online and off-line signature verification using relative slope algorithm,” Measurement Systems for Homeland Security, Contraband Detection and Personal Safety, March 2005.
L. Wolf, A. Shashua, “Kernel principal angles for classification machines with applications to image sequence interpretation,” In Proc. CVPR, vol. 1, pp. 635–640, March 2003.