شناسایی حرکات تصور شده برمبنای ویژگی‌های دینامیکی سیگنال EEG
الموضوعات :
1 - دانشکده مهندسی برق- واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
2 - مرکز تحقیقات پردازش دیجیتال و بینایی ماشین- واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران
الکلمات المفتاحية: SVM, طبقه بندی, تبدیل ویولت, تصور حرکت, ویژگی‌های غیرخطی,
ملخص المقالة :
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.
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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).
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[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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[25] G. Palshikar, "Simple algorithms for peak detection in time-series", In Proc. 1st Int. Conf. Advanced Data Analysis, Business Analytics and Intelligence, pp. 1-13, Jun. 2009.
