This work presents an innovative application of the well-known concept of cortico-muscular coherence for the classification of various motor tasks, i.e., grasps of different kinds of objects. Our approach can classify objects with different weights (motor-related features) and different surface frictions (haptics-related features) with high accuracy (over 0.8). The outcomes presented here provide information about the synchronization existing between the brain and the muscles during specific activities; thus, this may represent a new effective way to perform activity recognition.
Classification of grasping tasks based on EEG-EMG coherence / Cisotto, Giulia; Guglielmi, Anna V.; Badia, Leonardo; Zanella, Andrea. - (2018), pp. 1-6. ( 20th IEEE International Conference on e-Health Networking, Applications and Services (HEALTHCOM) - SEP 17-20, 2018 Ostrava, CZECH REPUBLIC 2018) [10.1109/HealthCom.2018.8531140].
Classification of grasping tasks based on EEG-EMG coherence
Cisotto, Giulia;
2018-01-01
Abstract
This work presents an innovative application of the well-known concept of cortico-muscular coherence for the classification of various motor tasks, i.e., grasps of different kinds of objects. Our approach can classify objects with different weights (motor-related features) and different surface frictions (haptics-related features) with high accuracy (over 0.8). The outcomes presented here provide information about the synchronization existing between the brain and the muscles during specific activities; thus, this may represent a new effective way to perform activity recognition.Pubblicazioni consigliate
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