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Enhancing the signal-to-noise ratio of ICA-based extracted ERPs
Steven Lemm
*
, Gabriel Curio
, Yevhen Hlushchuk
,
Klaus Robert Müller
*
Corresponding author for this work
Research output
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Contribution to journal
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Article
›
peer-review
75
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Keyphrases
Signal-to-noise Ratio
100%
Event-related Potentials
100%
Enhanced Separation
100%
Electroencephalography
50%
Artificial Data
50%
Background Activity
50%
Phase Locking
50%
Somatosensory Evoked Potentials
50%
Single-channel EEG
50%
Real-world Application
50%
EEG Recording
50%
Source Separation Methods
50%
Blind Source Separation Algorithm
50%
Regularization Framework
50%
Trial Phase
50%
Phase-locked Responses
50%
Computer Science
Noise-to-Signal Ratio
100%
Independent Component Analysis
100%
Enterprise Resource Planning
100%
Blind Signal Separation
50%
Regularization
50%
World Application
50%
Source Separation
50%
Weighted Average
50%
Artificial Data
50%
Engineering
Signal-to-Noise Ratio
100%
Independent Component Analysis
100%
Multichannel
50%
Regularization
50%
Blind Signal Separation
50%
Source Separation
50%
Real World Application
50%
Neuroscience
Event-Related Potential
100%
Signal-to-Noise Ratio
100%
Electroencephalography
50%
Electroencephalography
50%
Somatosensory Evoked Potential
50%
Physics
Independent Component Analysis
100%
Signal-to-Noise Ratio
100%
Electroencephalography
50%
Blind Signal Separation
50%
Psychology
Electroencephalography
100%
Event-Related Potential
100%
Evoked Potential
50%
Phase-Locking
50%