Cultural diversity and global media studies

Frau‐Meigs, Divina

doi:10.1177/17427665070030030104

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…Currently, MI has been postulated as a reliable tool to promote motor learning in all of its phases in activities, like the development of professional motor skills learning [ 2 ], improvement of sports gestures [ 3 ], and enhancement of skilled performance in the patient rehabilitation [ 4 , 5 ], among others. In these applications, the Media and Information Literacy methodology proposed by UNESCO includes many competencies that are vital for people to be effectively engaged in human development [ 6 ]. Electroencephalography (EEG) signals are broadly accepted to implement MI systems due to their noninvasive nature, portability, and cost-efficiency.…”

Section: Introductionmentioning

confidence: 99%

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

et al. 2020

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Motor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we develop a data-driven estimator, termed Deep Regression Network (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain–Computer Interface inefficiency of subjects.

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Section: Introductionmentioning

confidence: 99%

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

et al. 2020

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“…Understanding of MI fundamentals gives insights into the underpinning brain dynamic organization since a mental representation of specific movements involves cooperating (sub-)cortical networks in the brain. Thus, evaluation and interpretation of brain dynamics in the sensorimotor area may contribute to the assessment of pathological conditions, the rehabilitation of motor functions [ 1 , 2 ], motor learning and performance [ 3 ], evaluation of brain activity functioning in children with developmental coordination disorders [ 4 ], improving balance and mobility outcomes in older adults [ 5 ], and more recently in education scenarios, allows analyzing learner’s mental situation under frameworks as the Media and Information Literacy methodology [ 6 ] and John Sweller’s Cognitive LoadTheory [ 7 ], among other applications.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns

Velásquez-Martínez

Caicedo-Acosta

Castellanos-Domínguez

2020

Entropy

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Assessment of brain dynamics elicited by motor imagery (MI) tasks contributes to clinical and learning applications. In this regard, Event-Related Desynchronization/Synchronization (ERD/S) is computed from Electroencephalographic signals, which show considerable variations in complexity. We present an Entropy-based method, termed VQEnt, for estimation of ERD/S using quantized stochastic patterns as a symbolic space, aiming to improve their discriminability and physiological interpretability. The proposed method builds the probabilistic priors by assessing the Gaussian similarity between the input measured data and their reduced vector-quantized representation. The validating results of a bi-class imagine task database (left and right hand) prove that VQEnt holds symbols that encode several neighboring samples, providing similar or even better accuracy than the other baseline sample-based algorithms of Entropy estimation. Besides, the performed ERD/S time-series are close enough to the trajectories extracted by the variational percentage of EEG signal power and fulfill the physiological MI paradigm. In BCI literate individuals, the VQEnt estimator presents the most accurate outcomes at a lower amount of electrodes placed in the sensorimotor cortex so that reduced channel set directly involved with the MI paradigm is enough to discriminate between tasks, providing an accuracy similar to the performed by the whole electrode set.

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Circumnavigating de-Westernisation: Theoretical reflexivities in researching political communication in Africa

Ngomba¹

2012

Communicatio

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Cultural diversity and global media studies

Cited by 5 publications

References 2 publications

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns

Circumnavigating de-Westernisation: Theoretical reflexivities in researching political communication in Africa

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