Mining EEG scalp maps of independent components related to HCT tasks

被引：0
|
作者：
Teixeira, A. R. [1 ]
Santos, I. M. [3 ]
Lang, E. W. [4 ]
Tome, A. M. [2 ]
机构：
[1] ESEC Inst Politecn Coimbra, P-3030329 Coimbra, Portugal
[2] IEETA Univ Aveiro, DETI, P-3810193 Aveiro, Portugal
[3] Univ Aveiro, CINTESIS, Dept Educ & Psicol, P-3810193 Aveiro, Portugal
[4] Univ Regensburg, Inst Biophys, CIML Grp, D-93040 Regensburg, Germany
来源：
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2019年
关键词：
ELECTROENCEPHALOGRAM;
D O I：
10.1109/embc.2019.8857600
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
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.
引用
收藏
页码：3888 / 3891
页数：4
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