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147
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NN
2000
Springer
159views Neural Networks» more  NN 2000»
15 years 3 months ago
Independent component analysis for noisy data -- MEG data analysis
ICA (independent component analysis) is a new, simple and powerful idea for analyzing multi-variant data. One of the successful applications is neurobiological data analysis such ...
Shiro Ikeda, Keisuke Toyama
ISBI
2009
IEEE
15 years 10 months ago
EEG Classification by ICA Source Selection of Laplacian-Filtered Data
We studied the performance of a double-spatial filtering method for classification of single-trial electroencephalography (EEG) data that couples the spherical surface Laplacian...
Claudio Carvalhaes, Marcos Perreau Guimaraes, Loga...
133
Voted
ICASSP
2010
IEEE
14 years 10 months ago
Temporally constrained SCA with applications to EEG data
In this paper we propose an iterative algorithm for solving the problem of extracting a sparse source signal when a reference signal for the desired source signal is available. In...
Nasser Mourad, James P. Reilly, Gary Hasey, Duncan...
119
Voted
NIPS
1997
15 years 4 months ago
Extended ICA Removes Artifacts from Electroencephalographic Recordings
Severe contamination of electroencephalographic (EEG) activity by eye movements, blinks, muscle, heart and line noise is a serious problem for EEG interpretation and analysis. Rej...
Tzyy-Ping Jung, Colin Humphries, Te-Won Lee, Scott...
203
Voted
HCI
2009
15 years 1 months ago
Mind-Mirror: EEG-Guided Image Evolution
Abstract. We propose a brain-computer interface (BCI) system for evolving images in realtime based on subject feedback derived from electroencephalography (EEG). The goal of this s...
Nima Bigdely Shamlo, Scott Makeig