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(A) (Left) Smoothed probability of three Adaptive Mixture ICA Models found by <t>AMICA</t> decomposition of concatenated data during the walk and rest conditions for one subject. Models 1 and 3 are active respectively during the walk and rest conditions. (B) (Right) Model Activity Rate (MAR) of the three models during resting and during walking. (C) (Bottom) Three representative components only present in Model 1 (walking), and a component only in Model 3 (resting). This representation demonstrates stationarity of resting and walking phases and the necessity of two separate models to explain the data.
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(A) (Left) Smoothed probability of three Adaptive Mixture ICA Models found by <t>AMICA</t> decomposition of concatenated data during the walk and rest conditions for one subject. Models 1 and 3 are active respectively during the walk and rest conditions. (B) (Right) Model Activity Rate (MAR) of the three models during resting and during walking. (C) (Bottom) Three representative components only present in Model 1 (walking), and a component only in Model 3 (resting). This representation demonstrates stationarity of resting and walking phases and the necessity of two separate models to explain the data.
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(A) (Left) Smoothed probability of three Adaptive Mixture ICA Models found by <t>AMICA</t> decomposition of concatenated data during the walk and rest conditions for one subject. Models 1 and 3 are active respectively during the walk and rest conditions. (B) (Right) Model Activity Rate (MAR) of the three models during resting and during walking. (C) (Bottom) Three representative components only present in Model 1 (walking), and a component only in Model 3 (resting). This representation demonstrates stationarity of resting and walking phases and the necessity of two separate models to explain the data.
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(A) (Left) Smoothed probability of three Adaptive Mixture ICA Models found by <t>AMICA</t> decomposition of concatenated data during the walk and rest conditions for one subject. Models 1 and 3 are active respectively during the walk and rest conditions. (B) (Right) Model Activity Rate (MAR) of the three models during resting and during walking. (C) (Bottom) Three representative components only present in Model 1 (walking), and a component only in Model 3 (resting). This representation demonstrates stationarity of resting and walking phases and the necessity of two separate models to explain the data.
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(A) (Left) Smoothed probability of three Adaptive Mixture ICA Models found by <t>AMICA</t> decomposition of concatenated data during the walk and rest conditions for one subject. Models 1 and 3 are active respectively during the walk and rest conditions. (B) (Right) Model Activity Rate (MAR) of the three models during resting and during walking. (C) (Bottom) Three representative components only present in Model 1 (walking), and a component only in Model 3 (resting). This representation demonstrates stationarity of resting and walking phases and the necessity of two separate models to explain the data.
Independent Component Analysis Algorithm, supplied by InfoMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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(A) (Left) Smoothed probability of three Adaptive Mixture ICA Models found by <t>AMICA</t> decomposition of concatenated data during the walk and rest conditions for one subject. Models 1 and 3 are active respectively during the walk and rest conditions. (B) (Right) Model Activity Rate (MAR) of the three models during resting and during walking. (C) (Bottom) Three representative components only present in Model 1 (walking), and a component only in Model 3 (resting). This representation demonstrates stationarity of resting and walking phases and the necessity of two separate models to explain the data.
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(A) (Left) Smoothed probability of three Adaptive Mixture ICA Models found by <t>AMICA</t> decomposition of concatenated data during the walk and rest conditions for one subject. Models 1 and 3 are active respectively during the walk and rest conditions. (B) (Right) Model Activity Rate (MAR) of the three models during resting and during walking. (C) (Bottom) Three representative components only present in Model 1 (walking), and a component only in Model 3 (resting). This representation demonstrates stationarity of resting and walking phases and the necessity of two separate models to explain the data.
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Image Search Results


(A) (Left) Smoothed probability of three Adaptive Mixture ICA Models found by AMICA decomposition of concatenated data during the walk and rest conditions for one subject. Models 1 and 3 are active respectively during the walk and rest conditions. (B) (Right) Model Activity Rate (MAR) of the three models during resting and during walking. (C) (Bottom) Three representative components only present in Model 1 (walking), and a component only in Model 3 (resting). This representation demonstrates stationarity of resting and walking phases and the necessity of two separate models to explain the data.

Journal: NeuroImage

Article Title: Unidirectional brain to muscle connectivity reveals motor cortex control of leg muscles during stereotyped walking

doi: 10.1016/j.neuroimage.2017.07.013

Figure Lengend Snippet: (A) (Left) Smoothed probability of three Adaptive Mixture ICA Models found by AMICA decomposition of concatenated data during the walk and rest conditions for one subject. Models 1 and 3 are active respectively during the walk and rest conditions. (B) (Right) Model Activity Rate (MAR) of the three models during resting and during walking. (C) (Bottom) Three representative components only present in Model 1 (walking), and a component only in Model 3 (resting). This representation demonstrates stationarity of resting and walking phases and the necessity of two separate models to explain the data.

Article Snippet: Finally the hypothesis of data stationarity underlining the whole analysis hitherto described was demonstrated by means of multiple-model Adaptive Mixture Independent Component Analysis (AMICA) , which can be viewed as a generalization of the Infomax algorithm ( ) supporting a multiple mixture approach ( ).

Techniques: Activity Assay