دوره 7، شماره 1 - ( Winter 2016 -- 1394 )                   جلد 7 شماره 1 صفحات 13-20 | برگشت به فهرست نسخه ها


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Resalat S N, Saba V. A Study of Various Feature Extraction Methods on a Motor Imagery Based Brain Computer Interface System. BCN. 2016; 7 (1) :13-20
URL: http://bcn.iums.ac.ir/article-1-384-fa.html
A Study of Various Feature Extraction Methods on a Motor Imagery Based Brain Computer Interface System. مجله علوم اعصاب پایه و بالینی. 1394; 7 (1) :13-20

URL: http://bcn.iums.ac.ir/article-1-384-fa.html


چکیده:  

Introduction: Brain Computer Interface (BCI) systems based on Movement Imagination (MI) are widely used in recent decades. Separate feature extraction methods are employed in the MI data sets and classified in Virtual Reality (VR) environments for real-time applications.
Methods: This study applied wide variety of features on the recorded data using Linear Discriminant Analysis (LDA) classifier to select the best feature sets in the offline mode. The data set was recorded in 3-class tasks of the left hand, the right hand, and the foot motor imagery.
Results: The experimental results showed that Auto-Regressive (AR), Mean Absolute Value (MAV), and Band Power (BP) features have higher accuracy values,75% more than those for the other features.
Discussion: These features were selected for the designed real-time navigation. The corresponding results revealed the subject-specific nature of the MI-based BCI system however, the Power Spectral Density (PSD) based &alpha-BP feature had the highest averaged accuracy.

نوع مطالعه: Original | موضوع مقاله: Cognitive Neuroscience
دریافت: ۱۳۹۳/۱۲/۹ | پذیرش: ۱۳۹۴/۳/۳۱ | انتشار: ۱۳۹۴/۱۰/۱۱

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