Paper Info
Title
Mining Eeg Scalp Maps Of Independent Components Related To Hct Tasks
Abstract
This work presents an unsupervised mining strategy, applied to an independent component analysis (ICA) of segments of data collected while participants are answering to the items of the Halstead Category Test (HCT). This new methodology was developed to achieve signal components at trial level and therefore to study signal dynamics which are not available within participants' ensemble average signals. The study will be focused on the signal component that can be elicited by the binary visual feedback which is part of the HCT protocol. The experimental study is conducted using a cohort of 58 participants.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8857600
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Computer vision,Ensemble average,Task analysis,Pattern recognition,Visualization,Computer science,Independent component analysis,Artificial intelligence,Halstead Category Test,Scalp,Electroencephalography,Binary number
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Ana R. Teixeira100.34
Isabel M. Santos200.34
Elmar Wolfgang Lang326036.10
Ana Maria Tomé416330.42