AIMC Topic: Motor Activity

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Object parsing in the left lateral occipitotemporal cortex: Whole shape, part shape, and graspability.

Neuropsychologia
Small and manipulable objects (tools) preferentially evoke a network of brain regions relative to other objects, including the lateral occipitotemporal cortex (LOTC), which is assumed to process tool shape information. Given the correlation between v...

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 ...

The Interaction Between Feedback Type and Learning in Routine Grasping With Myoelectric Prostheses.

IEEE transactions on haptics
While prosthetic fitting after upper-limb loss allows for restoration of motor functions, it deprives the amputee of tactile sensations that are essential for grasp control in able-bodied subjects. Therefore, it is commonly assumed that restoring the...

Using a Variable-Friction Robot Hand to Determine Proprioceptive Features for Object Classification During Within-Hand-Manipulation.

IEEE transactions on haptics
Interactions with an object during within-hand manipulation (WIHM) constitutes an assortment of gripping, sliding, and pivoting actions. In addition to manipulation benefits, the re-orientation and motion of the objects within-the-hand also provides ...

Hierarchical motor control in mammals and machines.

Nature communications
Advances in artificial intelligence are stimulating interest in neuroscience. However, most attention is given to discrete tasks with simple action spaces, such as board games and classic video games. Less discussed in neuroscience are parallel advan...

Evaluating a Semiautonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision.

Computational intelligence and neuroscience
In this paper, we evaluate a semiautonomous brain-computer interface (BCI) for manipulation tasks. In such a system, the user controls a robotic arm through motor imagery commands. In traditional process-control BCI systems, the user has to provide t...

Improved Transductive Support Vector Machine for a Small Labelled Set in Motor Imagery-Based Brain-Computer Interface.

Computational intelligence and neuroscience
Long and tedious calibration time hinders the development of motor imagery- (MI-) based brain-computer interface (BCI). To tackle this problem, we use a limited labelled set and a relatively large unlabelled set from the same subject for training bas...

Data-driven analyses of motor impairments in animal models of neurological disorders.

PLoS biology
Behavior provides important insights into neuronal processes. For example, analysis of reaching movements can give a reliable indication of the degree of impairment in neurological disorders such as stroke, Parkinson disease, or Huntington disease. T...

Profiling movement behaviours in pre-school children: A self-organised map approach.

Journal of sports sciences
Application of machine learning techniques has the potential to yield unseen insights into movement and permits visualisation of complex behaviours and tangible profiles. The aim of this study was to identify profiles of relative motor competence (MC...

Attribution of intentional agency towards robots reduces one's own sense of agency.

Cognition
In the presence of others, sense of agency (SoA), i.e. the perceived relationship between our own actions and external events, is reduced. The present study aimed at investigating whether the phenomenon of reduced SoA is observed in human-robot inter...