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 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: Common Spatial Patterns, Functional Connectivity, and Event-Related De/Synchronization. In terms of interpretability, we evaluate the effectiveness in gathering MI data from collective populations by introducing two assumptions: (i) Non-linear assessment of the similarity between multi-subject data originating the subject-level dynamics; (ii) Assessment of time-varying brain network responses according to the ranking of individual accuracy performed in distinguishing distinct motor imagery tasks (left-hand vs. 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.
Highlights
Motor imagery (MI) is a dynamic mental state in which an individual performs a mental rehearsal of motor action without any overt output
We develop a dynamic model for estimating the common neural activity across subjects to provide new insights into the evolution of collective mental imagery processes
The t-f EEG signal set is fed into a feature extraction algorithm to improve the efficiency of triggering activity representation
Summary
Motor imagery (MI) is a dynamic mental state in which an individual performs a mental rehearsal of motor action without any overt output. There is sufficient experimental evidence that MI contributes to substantial improvements in motor learning and performance (Aymeric and Ursula, 2019), games and entertainment, sports training, therapy to induce recovery and neuroplasticity in neurophysical regulation and rehabilitation, and activation of brain neural networks as the basis of motor learning (Machado et al, 2019), and education scenarios (Boe and Kraeutner, 2018; MacIntyre et al, 2018; Suica et al, 2018), where the Media and Information Literacy methodology proposed by UNESCO includes many competencies that are vital for people to be effectively engaged in human development (Frau-Meigs, 2007) These applications reinforce the importance of studying the evolving brain organization to model plastic changes accurately, putting strength on dynamic modeling of temporal, spectral, and spatial features extracted from single channels due to most MI systems rely on them to distinguish distinctive neural activation patterns (Hamedi et al, 2016; Allen et al, 2018). The performance of MI systems varies considerably across and within-subjects, severely degrading their reliability
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