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Motor Activity

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A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification.

Computational intelligence and neuroscience
Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their ...

Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off.

PloS one
Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the...

A systematic evaluation of the evidence for perceptual control theory in tracking studies.

Neuroscience and biobehavioral reviews
Perceptual control theory (PCT) proposes that perceptual inputs are controlled to intentional 'reference' states by hierarchical negative feedback control, evidence for which comes from manual tracking experiments in humans. We reviewed these experim...

GEARing smart environments for pediatric motor rehabilitation.

Journal of neuroengineering and rehabilitation
BACKGROUND: There is a lack of early (infant) mobility rehabilitation approaches that incorporate natural and complex environments and have the potential to concurrently advance motor, cognitive, and social development. The Grounded Early Adaptive Re...

Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control.

Communications biology
Efficient action control is indispensable for goal-directed behaviour. Different theories have stressed the importance of either attention or response selection sub-processes for action control. Yet, it is unclear to what extent these processes can b...

A pilot study on the design and validation of a hybrid exoskeleton robotic device for hand rehabilitation.

Journal of hand therapy : official journal of the American Society of Hand Therapists
STUDY DESIGN: An iterative design process was used to obtain design parameters that satisfy both kinematic and dynamic requirements for the hand exoskeleton. This design was validated through experimental studies.

Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces.

Journal of integrative neuroscience
One of the challenges in brain-computer interface systems is obtaining motor imagery recognition from brain activities. Brain-signal decoding robustness and system performance improvement during the motor imagery process are two of the essential issu...

Behavioral Activity Recognition Based on Gaze Ethograms.

International journal of neural systems
Noninvasive behavior observation techniques allow more natural human behavior assessment experiments with higher ecological validity. We propose the use of gaze ethograms in the context of user interaction with a computer display to characterize the ...

Screening of Parkinsonian subtle fine-motor impairment from touchscreen typing via deep learning.

Scientific reports
Fine-motor impairment (FMI) is progressively expressed in early Parkinson's Disease (PD) patients and is now known to be evident in the immediate prodromal stage of the condition. The clinical techniques for detecting FMI may not be robust enough and...

Statistical measures of motor, sensory and cognitive performance across repeated robot-based testing.

Journal of neuroengineering and rehabilitation
BACKGROUND: Traditional clinical assessments are used extensively in neurology; however, they can be coarse, which can also make them insensitive to change. Kinarm is a robotic assessment system that has been used for precise assessment of individual...