Paper Info
Title
Classification of finger pairs from one hand based on spectral features in human EEG.
Abstract
Individual finger movements are well-articulated movements of fine body parts, the successful decoding of which can provide extra degrees of freedom to drive brain computer interface (BCI) applications. Past studies present some unique features revealed from spectral principal component analysis (PCA) on electrophysiological data recorded in both the surface of the brain (electrocorticography, ECoG) and the scalp (electroencephalography, EEG). These features contain discriminable information about fine individual finger movements from one hand. However, the efficacy of these spectral features has not been well investigated under the application of various classifiers. In the present study, we set out to investigate the topic using noninvasive human EEG. Several classifiers were chosen to explore their capability in capturing the spectral PC features to decode individual finger movements pairwisely from one hand using noninvasive EEG, aiming to investigate the efficacy of these spectral features in a decoding task.
Year
DOI
Venue
2014
10.1109/EMBC.2014.6943827
EMBC
Keywords
DocType
Volume
biomechanics,bioelectric potentials,classifier application,electroencephalography,brain-computer interfaces,medical signal processing,human eeg spectral features,well-articulated finger movements,finger movement pairwise decoding,spectral analysis,spectral feature efficacy,pca,one hand finger pair classification,scalp electrophysiological data recording,feature extraction,spectral pc feature capture,brain surface electrophysiological data recording,signal classification,electrocorticography,individual finger movement decoding,spectral principal component analysis,brain computer interface,principal component analysis,noninvasive human eeg,decoding,ecog,bci application
Conference
2014
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
2
2
Name
Order
Citations
PageRank
Ran Xiao172.09
Lei Ding214226.77