AMICA : An Adaptive Mixture of Independent Component Analyzers with Shared Components

By Jason A. Palmer, Kenneth Kreutz-Delgado, Scott Makeig. 2011

327 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5774e96ad450c228400dc311f16caf1f20967c10

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Begonya García-Zapirain Soto
2. Jose Antonio Urigüen
3. Xiao Jiang

Citations in the corpus

• EEG artifact removal—state-of-the-art and guidelines (2015, background): “To mention just a few cases: Jung et al deal with ocular and muscular artifacts in [43] via extended InfoMax; Iriarte et al evaluate joint approximate diagonalization of eigenmatrices (JADE) to eliminate electrocardiogram, eye movement, 50 Hz interference, muscle, or electrode artifacts from interic”
• Get ready! High urgency reduces beta band cortico-muscular coherence during motor preparation (2024, unknown): “To correct for blinks, horizontal saccades and other artifacts, Independent Component Analysis was conducted using runamica15() from the AMICA plugin on 100 Hz down-sampled data and the result was applied to the original and independent components (ICs) containing artifacts were automatically identi”