SUBTLE ELECTROMYOGRAPHIC PATTERN RECOGNITION FOR FINGER MOVEMENTS: A PILOT STUDY USING BSS TECHNIQUES
SUBTLE ELECTROMYOGRAPHIC PATTERN RECOGNITION FOR FINGER MOVEMENTS: A PILOT STUDY USING BSS TECHNIQUES
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DOI：
10.1142/s0219519412005009
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发表时间：
2012-09-01
影响因子：
0.8
通讯作者：
Kumar, Dinesh K.
中科院分区：
文献类型：
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作者：
Naik, Ganesh R.;Kumar, Dinesh K.
近年来，利用多元统计数据分析技术的盲源分离（BSS）算法已成功用于生物医学和统计信号处理领域的源识别和分离。最近开发了多种不同的 BSS 技术。由于 BSS 方法是生物信号源分离和分解的可行方法，因此比较不同的技术并确定最适合应用的方法非常重要。本文介绍了五种 BSS 算法（SOBI、TDSEP、FastICA、JADE 和 Infomax）的性能，用于分解 sEMG 以识别细微的手指运动。据观察，与基于高阶统计的算法（FastICA、JADE 和 infomax）相比，基于二阶统计的 BSS 算法（SOBI 和 TDSEP）具有更好的性能。
In the recent past, blind source separation (BSS) algorithms using multivariate statistical data analysis technique have been successfully used for source identification and separation in the field of biomedical and statistical signal processing. Recently numbers of different BSS techniques have been developed. With BSS methods being the feasible method for source separation and decomposition of biosignals, it is important to compare the different techniques and determine the most suitable method for the applications. This paper presents the performance of five BSS algorithms (SOBI, TDSEP, FastICA, JADE and Infomax) for decomposition of sEMG to identify subtle finger movements. It is observed that BSS algorithms based on second-order statistics (SOBI and TDSEP) gives better performance compared to algorithms based on higher-order statistics (FastICA, JADE and infomax).