Paper Info
Title
Decoding of Upper Limb Movement Using EEG and Sparse Coding
Abstract
In this paper, a system is proposed for decoding upper limb movement from EEG. The signal processing procedure consists of a learning phase and test phase. In the learning phase, a Kinect sensor is utilized to measure the true values for a hand's movement. Sparse coding is applied to calculate the weight of a linear decoding model. Because sparse coding can be used to derive the sparse weight, most of the elements are zero, with the remaining elements being non-zero. Thus, it is an effective method for reducing the calculation costs. Sparse coding was combined with noise reduction of the EEG signals to achieve good estimation for upper limb movements in the experimental results.
Year
DOI
Venue
2015
10.1007/978-3-319-21380-4_124
Communications in Computer and Information Science
Keywords
Field
DocType
Electroencephalogram,Sparse coding,Decoding,Rehabilitation
Noise reduction,Signal processing,Upper limb,Pattern recognition,Effective method,Neural coding,Computer science,Human–computer interaction,Artificial intelligence,Decoding methods,Electroencephalography
Conference
Volume
ISSN
Citations 
528
1865-0929
0
PageRank 
References 
Authors
0.34
2
1
Name
Order
Citations
PageRank
Masashi Yamashita100.34