Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces

被引：206

作者：

Vidaurre, Carmen ^{[1
,2
]}

Kraemer, Nicole ^{[1
]}

Blankertz, Benjamin ^{[1
,2
]}

Schloegl, Alois ^{[3
]}

机构：

[1] Berlin Inst Technol, Machine Learning Grp, D-10587 Berlin, Germany

[2] IDA Fraunhofer FIRST, D-12489 Berlin, Germany

[3] Graz Univ Technol, Inst Human Comp Interfaces, A-8010 Graz, Austria

来源：

NEURAL NETWORKS | 2009年 / 22卷 / 09期

关键词：

Brain-Computer Interface; Hjorth parameters; Band power estimates; Time Domain Parameters; Derivative operator; ONLINE CLASSIFICATION; COMMUNICATION; PERFORMANCE;

D O I：

10.1016/j.neunet.2009.07.020

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Several feature types have been used with EEG-based Brain-Computer Interfaces Among the most popular are logarithmic band power estimates with more or less subject-specific optimization of file frequency bands. In this paper we introduce a feature called Time Domain Parameter that is defined by the generalization of the Hjorth parameters. Time Domain Parameters are Studied Under two different conditions. The first setting is defined when no data from a Subject is available. In this condition our results show that Time Domain Parameters outperform all band power features tested with all spatial filters applied. The second setting is the transition from calibration (no feedback) to feedback, in which the frequency content of the signals can change for some subjects We compare Time Domain Parameters with logarithmic band power in subject-specific bands and show that these features are advantageous in this situation as well. (C) 2009 Elsevier Ltd All rights reserved.

引用

页码：1313 / 1319

页数：7

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