کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10435045 | 910806 | 2015 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
NIRS-based classification of clench force and speed motor imagery with the use of empirical mode decomposition for BCI
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی پزشکی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Near-infrared spectroscopy (NIRS) is a non-invasive optical technique used for brain-computer interface (BCI). This study aims to investigate the brain hemodynamic responses of clench force and speed motor imagery and extract task-relevant features to obtain better classification performance. Given the non-stationary characteristics of real hemodynamic measurements, empirical mode decomposition (EMD) was applied to reduce the physiological noise overwhelmed in the task-relevant NIRS signals. Compared with continuous wavelet decomposition, EMD does not require a pre-determined basis function. EMD decomposes the original signals into a set of intrinsic mode functions (IMFs). In this study, joint mutual information was applied to select the optimal features, and support vector machine was used as a classifier. Offline and pseudo-online analyses showed that the most feasible classification accuracy can be obtained using IMFs as input features. Accordingly, an alternative feature is provided to develop the NIRS-BCI system.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Medical Engineering & Physics - Volume 37, Issue 3, March 2015, Pages 280-286
Journal: Medical Engineering & Physics - Volume 37, Issue 3, March 2015, Pages 280-286
نویسندگان
Xuxian Yin, Baolei Xu, Changhao Jiang, Yunfa Fu, Zhidong Wang, Hongyi Li, Gang Shi,