نوع مقاله: مقاله کامل پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی پزشکی، گروه مهندسی پزشکی، دانشکده‌ی مهندسی برق، دانشگاه علم و صنعت ایران، تهران، ایران

2 استاد، گروه مهندسی پزشکی، دانشکده‌ی مهندسی برق، دانشگاه علم و صنعت ایران، تهران، ایران

چکیده

کنترل پروتز عصبی به منظور بازیابی عمل‌کرد دست افراد مبتلا به فلج اندام­های فوقانی، یکی از کاربردهای مهم سیستم­های BCI می‌باشد. توانایی گرفتن اجسام، از ابتدایی­ترین نیازها برای انجام کارهای روزانه بوده و از این رو برای عمل‌کرد صحیح پروتز عصبی، لازم است تا کاربر بتواند مقدار نیروی لازم برای گرفتن اجسام را کنترل کند. به همین دلیل افزایش دقت رمزگشایی پیوسته‌ی نیرو موضوعی مهم برای عمل‌کرد صحیح این نوع سیستم­های BCI می­باشد. در اغلب پژوهش­های صورت گرفته در زمینه‌ی رمزگشایی نیرو از مدل­های خطی مانند فیلتر وینر، فیلتر کالمن و PLS استفاده شده و تا کنون تاثیر استفاده از مدل­های غیرخطی بر دقت رمزگشایی نیرو مورد بررسی قرار نگرفته است. هدف این پژوهش، بررسی تاثیر استفاده از مدل­های رگرسیون غیرخطی مبتنی بر توابع کرنل بر دقت رمزگشایی نیروی دست موش صحرایی با استفاده از سیگنال­های پتانسیل میدانی محلی می­باشد. بدین منظور روش­های رگرسیون ستیغی، PCR و PLS در نظر گرفته شده و با استفاده از تابع کرنل گوسی، از تعمیم­یافته‌ی غیرخطی آن‌ها برای تخمین پیوسته‌ی نیرو بهره گرفته شده است. ارزیابی و مقایسه‌ی روش­های رگرسیون ستیغی کرنلی، PCR کرنلی و PLS کرنلی نشان می­دهد که در نظر گرفتن ارتباطات غیرخطی بین ویژگی­های سیگنال مغزی، دقت رمزگشایی نیرو را نسبت به مدل­های خطی بهبود می‌بخشد. درصد بهبود میانگین ضریب R2 برابر با 7/12% برای روش رگرسیون ستیغی کرنلی نسبت به روش ستیغی، 5/25% برای روش PCR کرنلی نسبت به PCR و 1/19% برای روش PLS کرنلی نسبت به PLS به دست آمده است. بهترین دقت رمزگشایی نیرو نیز به ازای روش رگرسیون ستیغی کرنلی، با میانگین ضریب همبستگی 72% و مقدار R2 برابر با 62/0 به دست آمده است.

کلیدواژه‌ها

عنوان مقاله [English]

Evaluation of Nonlinear Models based on Kernel Functions for Force Decoding using Local Field Potential Signals

نویسندگان [English]

  • Maryam Fatemi 1
  • Mohammad Reza Daliri 2

1 Ph.D. Student, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

2 Professor, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

چکیده [English]

Controlling of neuroprostheses to restore grasping ability in patients with paralyzed or amputated upper limbs is one of the important applications of BCI systems. The ability to get objects is necessary for daily works so, for a reliable function of the neuroprostheses, it is necessary for the user to control the amount of force needed for grasping. For this reason, increasing the accuracy of continuous force decoding is an important issue for the convenient function of these BCI systems. In most studies in the field of force decoding, linear models such as wiener filter, Kalman filter, PLS, etc. are used to decode force. So far, the effect of using nonlinear models is not investigated on force decoding. The goal of this study is to investigate the effect of using nonlinear regression models based on kernel functions on the accuracy of force decoding in Vistar rats using local field potential signals. To do this, we choose ridge regression, PCR and PLS methods and use the Gaussian kernel function to construct a generalized nonlinear model for the force decoding. Evaluating kernel ridge, kernel PCR and kernel PLS methods shows that considering nonlinear relations between brain signal’s features improves decoding accuracy. The mean coefficient of determination (R2) improves 12.7% in kernel ridge toward ridge regression, 25.5% in kernel PCR toward PCR and 19.1% in kernel PLS toward PLS method. The best decoding accuracy has been achieved by the kernel ridge regression method and the mean correlation coefficient between the estimated and measured force is 0.72 and R2 is 0.62.

کلیدواژه‌ها [English]

  • Brain Computer Interface (BCI)
  • Local Field Potential
  • Continuous Force Decoding
  • nonlinear regression

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