شناسایی حرکات تصور شده برمبنای ویژگی‌های دینامیکی سیگنال EEG
محورهای موضوعی : انرژی های تجدیدپذیر
نگار دشتی
1
(دانشکده مهندسی برق- واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران)
مهدی خضری
2
(مرکز تحقیقات پردازش دیجیتال و بینایی ماشین- واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران)
کلید واژه: SVM, طبقه بندی, تبدیل ویولت, تصور حرکت, ویژگی‌های غیرخطی,
چکیده مقاله :
کنترل اندام های مصنوعی می تواند از طریق تفکیک الگوهای تصورحرکت با استفاده ازسیگنال های الکتروانسفالوگرافی (EEG) انجام شود. هدف از انجام این مطالعه تشخیص تصور حرکات دست و پا برمبنای سیگنال EEG است. مجموعه آزمون های IVA از داده های BCI Competition IIIکه شامل سیگنال های EEG ثبت شده از 5 فرد سالم و در سه کانال C3، C4 و CZ است، برای طراحی سیستم تشخیص حرکات تصور شده به کار رفت. در ابتدا، با استفاده از روش تحلیل مولفه ی اصلی چند مقیاسی (MSPCA) اجزای اساسی نویز سیگنال EEG حذف شدند. در مرحله ی بعد، سیگنال های EEG با دو روش مختلف شامل فیلترینگ فرکانسی با استفاده از فیلتر باترورث و روش تبدیل بسته ویولت (WPT) به بخش هایی تجزیه شدند. در این مطالعه، تجزیه و تحلیل نوسانات تفکیک‌شده، بعد فرکتال، بعد همبستگی، پیچیدگی لیمپل-زیو و آنتروپی به عنوان ویژگی های دینامیکی برای سیگنال ها محاسبه شدند. ویژگی های مورد نظر در هر دو روش تجزیه، برای نسخه زمانی زیرباندهای تعیین شده محاسبه شدند. به منظور تعیین بهترین عملکرد سیستم، ترکیب های متفاوتی از کانال ها و ویژگی ها مورد ارزیابی قرار گرفتند. روش تجزیه بر مبنای تبدیل ویولت، درحالت استفاده از هر سه کانال و پنج ویژگی، بالاترین دقت تشخیص را ارایه کرد؛ به گونه ای که با استفاده از روش طبقه بندی ماشین بردار پشتیبان (SVM)، دقت 93 درصد در شناسایی حرکات مورد نظر به دست آمد.
The control of artificial limbs can be done by distinguishing the patterns of imagined movement using the EEG signals. The aim of this study was to identify hand and foot imagery movements based on EEG signals. The Iva dataset of BCI Competition III, which includes EEG signals from 5 healthy individuals in C3, C4 and CZ channels, was used to design the imagery movements detection system. Initially, the basic components of EEG signal noise were removed using the MSPCA method. In the next step, the EEG signals were decomposed in two different ways including frequency filtering using the Butterworth filter and the wavelet packet transform (WPT). In this study, the detrended Fluctuation Analysis, Fractal dimension, Correlation dimension, Lempel-ziv complexity and Entropy as nonlinear dynamics features, were calculated for the signals. In both decomposition methods, the desired features were calculated for the temporal version of the specified subbands. In order to determine the best performance of the system, different combinations of the channels and the features were evaluated. The wavelet-based decomposition method, in the case of using all three channels and five features, provided the highest recognition accuracy; So that using support vector machine (SVM) classification method, the accuracy of 93% was obtained in identifying the desired movements.
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_||_[1] B. Blankertz, G. Dornhege, M. Krauledat, K. Müller, G. Curio, "The non-invasive Berlin Brain-computer interface: fast acquisition of effective performance in untrained subjects", NeuroImage, vol. 37, pp. 539-550, Aug. 2007 (doi: 10.1016/j.neuroimage.2007.01.051).
[2] E.W. Sellers, E. Donchin, "A P300-based brain-computer interface: initial tests by ALS patients", Clin Neurophysiol, vol. 117, pp. 538-548, Mar. 2006 (doi: 10.1016/j.clinph.2005.06.027).
[3] X. Gao, D. Xu, M. Cheng, S. Gao, "A BCI-based environmental controller for the Motion disabled", IEEE Trans on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 137–140, Jun. 2003 (doi: 10.1109/TNSRE.2003.814449).
[4] B. Graimann, B. Allison, G. Pfurtscheller, "Brain-computer interfaces: A gentle introduction", In: Graimann B., Pfurtscheller G., Allison B. (eds) Brain-Computer Interfaces. The Frontiers Collection. Springer, Berlin, Heidelberg, pp. 1-27, 2010 (doi: 10.1007/978-3-642-02091-9_1).
[5] J. Meng, G. Liu, G. Huang, X. Zhu, "Automated selecting subset of channels based on CSP in motor imagery brain-computer interface system", IEEE International Conference on Robotics and Biomimetics (ROBIO), Guilin, China, pp. 2290-2294, Dec. 2009 (doi: 10.1109/ROBIO.2009.5420462).
[6] M. Li, C. Lu, "The recognition of EEG with CSSD and SVM", Proceedings of the IEEE/WCICA, Beijing, China, pp. 4741-4746, Nov. 2012 (doi: 10.1109/WCICA.2012.6359377).
[7] N. Robinson, A.P. Vinod, K.K. Ang, K.P. Tee, C.T. Guan, "EEG-based classification of fast and slow hand movements using wavelet-CSP algorithm", IEEE Trans on Biomedical Engineering, vol. 60, no. 8, pp. 2123-2132, Aug. 2013 (doi: 10.1109/TBME.2013.2248153).
[8] Y. Siuly, P. Li, P. Wen, "Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain computer interface", Comput Methods Programs Biomed, vol. 113 no.13, pp. 767-780, Jan. 2014 (doi: 10.1016/j.cmpb.2013.12.020).
[9] M. Severens, M. Perusquia-Hernandez, B. Nienhuis, J. Farquhar and J. Duysens, "Using Actual and Imagined Walking Related Desynchronization Features in a BCI", IEEE Trans Neural Syst Rehabil Eng, vol. 23, no. 5, pp. 877-886, Sept. 2015 (doi: 10.1109/TNSRE.2014.2371391).
[10] M. Ma, L. Guo, K. Su and D. Liang, "Classification of motor imagery EEG signals based on wavelet transform and sample entropy", 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, pp. 905-910, Oct. 2017 (doi: 10.1109/IAEAC.2017.8054145).
[11] S. Enshaeifar, C. C. Took, C. Park and D. P. Mandic, "Quaternion Common Spatial Patterns", IEEE Trans Neural Syst Rehabil Eng, vol. 25, no. 8, pp. 1278-1286, Aug. 2017 (doi: 10.1109/TNSRE.2016.2625039).
[12] X. Ma, S. Qiu, W. Wei, S. Wang and H. He, "Deep Channel-Correlation Network for Motor Imagery Decoding from the Same Limb", IEEE Trans. Neural Syst Rehabil Eng, vol. 28, no. 1, pp. 297-306, Jan. 2020 (doi: 10.1109/TNSRE.2019.2953121).
[13] B. Blankertz, K.R. Müller, D.J. Krusienski, G. Schalk, J.R. Wolpaw, A. Schlögl, G. Pfurtscheller, R.M. Jdel, M. Schröder, N. Birbaumer, "The BCI competition III: Validating alternative approaches to actual BCI problems", IEEE Tans Neural Syst Rehabil Eng, vol. 14, no. 2, pp. 153–159, Jun. 2006 (doi: 10.1109/TNSRE.2006.875642).
[14] J.Kevric, and Subasi, "Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system". Biomed Signal Process Control, vol. 31, pp.398-406, Jan. 2017 (doi: 10.1016/j.bspc.2016.09.007).
[15] J.A. Wilson, G. Schalk, L.M. Walton, J.C. Williams, "Using an EEG-Based brain-computer interface for virtual cursor movement with BCI2000", J. Vis Exp. vol.29, Jul. 2009 (doi: 10.3791/1319).
[16] B. Hosseinifard, M.H. Moradi and R. Rostami, "Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal", Comput methods programs biomed, vol.109, no.3, pp.339-345, Mar. 2013 (doi: 10.1016/j.cmpb.2012.10.008).
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[21] M. Akay, "Nonlinear Biomedical Signal Processing: Dynamic Analysis and Modeling", Wiley-IEEE Press, New York, Sep. 2000.
[22] X. S. Zhang, R. J. Roy, and E. W. Jensen, "EEG complexity as a measure of depth of anesthesia for patients", IEEE Trans Biomed Eng, vol. 48, no. 12, pp. 1424–1433, Dec. 2001 (doi: 10.1109/10.966601).
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