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- W2743949744 abstract "Grasping objects is one of the most important hand utilisation in everyday life. Due to neuromuscular ailments or injury, some people are unable to move their hands. Though myoelectrically controlled prostheses are widely available in the market, they require some muscle based control points which are hardly available for many. Motor Imagination (MI) controlled prostheses will surpass this shortcomings for them. However, restoring human grasping from MI is a challenging task. In this study, we have decoded two major types (Power and Precision) of grasping along with their three subtypes each from motor imagery. A comparative study of various time domain analysis combined with different classifiers is presented here to find an optimum choice of feature and corresponding classifier. It has been concluded that Hjorth parameters classified with kNN classifier have yielded the highest accuracy of 97.9% while separating different types of grasping from motor imagination." @default.
- W2743949744 created "2017-08-17" @default.
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- W2743949744 date "2017-05-01" @default.
- W2743949744 modified "2023-09-27" @default.
- W2743949744 title "EEG based motor imagery study of time domain features for classification of power and precision hand grasps" @default.
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- W2743949744 doi "https://doi.org/10.1109/ner.2017.8008384" @default.
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