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- W3100728131 abstract "Evaluation of brain dynamics elicited by motor imagery (MI) tasks can contribute to clinical and learning applications. The multi-subject analysis is to make inferences on the group/population level about the properties of MI brain activity. However, intrinsic neurophysiological variability of neural dynamics poses a challenge for devising efficient MI systems. Here, we develop a textit{time-frequency} model for estimating the spatial relevance of common neural activity across subjects employing an introduced statistical thresholding rule. In deriving multi-subject spatial maps, we present a comparative analysis of three feature extraction methods: textit{Common Spatial Patterns}, textit{Functional Connectivity}, and textit{Event-Related De/Synchronization}. In terms of interpretability, we evaluate the effectiveness in gathering MI data from collective populations by introducing two assumptions: textit{i}) Non-linear assessment of the similarity between multi-subject data originating the subject-level dynamics; textit{ii}) Assessment of time-varying brain network responses according to the ranking of individual accuracy performed in distinguishing distinct motor imagery tasks (left-hand versus right-hand). The obtained validation results indicate that the estimated collective dynamics differently reflect the flow of sensorimotor cortex activation, providing new insights into the evolution of MI responses." @default.
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- W3100728131 date "2020-11-19" @default.
- W3100728131 modified "2023-09-25" @default.
- W3100728131 title "Dynamic Modeling of Common Brain Neural Activity in Motor Imagery Tasks" @default.
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- W3100728131 doi "https://doi.org/10.3389/fnins.2020.00714" @default.
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