Source separation using single channel ICA

By M. Davies, Christopher J. James. 2007

327 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrochemistry, Speech & Audio, Signal Processing

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Citations in the corpus

GAN Inversion: A Survey (2021, methodology): “For example, we can formulate GAN inversion as decomposing signals into components (matrix factorization problems) and use non-linear Factor Analysis (FA) [208], Independent Component Analysis (ICA) [209], Latent Dirichlet Allocation (LDA) [210], [211] to decompose the network weight and to find int”
Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics (2021, background): “The single-channel source separation problem (Davies & James, 2007) asks us to recover unobserved sources x 1 , x 2 ∈ X n = R 2 × n given an observed mixture y = g ( x ) = x 1 + x 2 .”
TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise (2014, methodology): “There are many signal decomposition approaches, such as singular spectrum analysis (SSA) [23], [24], single-channel independent component analysis [25], [26], and [27].”
Removal of Artifacts from EEG Signals: A Review (2019, methodology): “Considering that the trend of healthcare systems are developed toward few channels, Davies and James explored a single channel ICA method to decompose single channel signal into independent sources [113].”
Default-mode brain dysfunction in mental disorders: a systematic review. (2009, background): “Single channel ICA (SCICA) is a special form of ICA that isolates underlying components using only temporal information inherent in single channel recordings (Davies and James, 2007).”