Multi-class grasping classifiers using EEG data and a common spatial pattern filter. (3rd May 2017)

• Record Type: Journal Article
• Authors: Uchiyama, Emiko; Takano, Wataru; Nakamura, Yoshihiko
• Is Part Of: Advanced robotics. Volume 31:Number 9(2017)
• Page Start: 468
• Page End: 481
• Publication Date: 2017-05-03
• Subjects: EEG -- brain computer interface -- CSP filter -- Cutkosky's grasp taxonomy -- kNN method; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2017.1279569
• Languages: English
• ISSNs: 0169-1864

Abstract

Graphical Abstract: Abstract: This paper describes the classification of various human actions from brain activity. In particular, we focus on grasping movements and estimate grasping patterns from electroencephalogram (EEG) data. EEG data is converted to grasping features by using a common spatial pattern filter (CSP filter), and the features are subsequently classified into grasping categories by using the k -nearest neighbor method. We tested the pipeline of feature extraction and classification on the EEG dataset. The EEG data were acquired while participants grasped an object according to the Cutkosky's grasping taxonomy, in which grasping movements are categorized into nine power-type grasping patterns and seven precision-type grasping patterns. The best classification rate for 9-class power-type grasping patterns was 48% and for 7-class precision-type grasping patterns was 40%.