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NIPS
2008
13 years 9 months ago
Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing
EEG connectivity measures could provide a new type of feature space for inferring a subject's intention in Brain-Computer Interfaces (BCIs). However, very little is known on ...
Moritz Grosse-Wentrup
ICASSP
2011
IEEE
12 years 11 months ago
Classification by weighting for spatio-frequency components of EEG signal during motor imagery
We propose a novel method for the classification of EEG signals during motor-imagery. For motor-imagery based brain computer interface (MI-BCI), a method called common spatial pa...
Hiroshi Higashi, Toshihisa Tanaka
ICPR
2008
IEEE
14 years 9 months ago
Pattern rejection strategies for the design of self-paced EEG-based Brain-Computer Interfaces
This paper deals with pattern rejection strategies for self-paced Brain-Computer Interfaces (BCI). First, it introduces two pattern rejection strategies not used yet for self-pace...
Fabien Lotte, Harold Mouchère, Anatole L&ea...
JUCS
2006
185views more  JUCS 2006»
13 years 7 months ago
The Berlin Brain-Computer Interface: Machine Learning Based Detection of User Specific Brain States
We outline the Berlin Brain-Computer Interface (BBCI), a system which enables us to translate brain signals from movements or movement intentions into control commands. The main co...
Benjamin Blankertz, Guido Dornhege, Steven Lemm, M...
WSCG
2004
188views more  WSCG 2004»
13 years 9 months ago
Recognition of Motor Imagery Electroencephalography Using Independent Component Analysis and Machine Classifiers
Motor imagery electroencephalography (EEG), which embodies cortical potentials during mental simulation of left or right finger lifting tasks, can be used as neural input signals ...
Chih-I. Hung, Po-Lei Lee, Yu-Te Wu, Hui-Yun Chen, ...