AIMC Topic: Motor Activity

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Time-frequency modulation of ERD and EEG coherence in robot-assisted hand performance.

Brain topography
A better understanding of cortical modifications related to movement preparation and execution after robot-assisted training could aid in refining rehabilitation therapy protocols for stroke patients. Electroencephalography (EEG) modifications of cor...

Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people.

IEEE journal of biomedical and health informatics
Activity recognition of an elderly person can be used to provide information and intelligent services to health care professionals, carers, elderly people, and their families so that the elderly people can remain at homes independently. This study in...

Cerebellar circuit computations for predictive motor control.

Nature reviews. Neuroscience
The rise of the deep neural network as the workhorse of artificial intelligence has brought increased attention to how network architectures serve specialized functions. The cerebellum, with its largely shallow, feedforward architecture, provides a c...

A subject transfer neural network fuses Generator and Euclidean alignment for EEG-based motor imagery classification.

Journal of neuroscience methods
BACKGROUND: Brain-computer interface (BCI) facilitates the connection between human brain and computer, enabling individuals to control external devices indirectly through cognitive processes. Although it has great development prospects, the signific...

EEG-Based Feature Classification Combining 3D-Convolutional Neural Networks with Generative Adversarial Networks for Motor Imagery.

Journal of integrative neuroscience
BACKGROUND: The adoption of convolutional neural networks (CNNs) for decoding electroencephalogram (EEG)-based motor imagery (MI) in brain-computer interfaces has significantly increased recently. The effective extraction of motor imagery features is...

Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning.

Human brain mapping
Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion magnetic resonance imaging tractography parcellation methods have been successful in defining major cerebellar fibre tracts,...

Classification of Motor Imagery Tasks Derived from Unilateral Upper Limb based on a Weight-optimized Learning Model.

Journal of integrative neuroscience
BACKGROUND: The accuracy of decoding fine motor imagery (MI) tasks remains relatively low due to the dense distribution of active areas in the cerebral cortex.

Exploring differences for motor imagery using Teager energy operator-based EEG microstate analyses.

Journal of integrative neuroscience
In this paper, the differences between two motor imagery tasks are captured through microstate parameters (occurrence, duration and coverage, and mean spatial correlation (Mspatcorr)) derived from a novel method based on electroencephalogram microsta...

Concerns for management of STEMI patients in the COVID-19 era: a paradox phenomenon.

Journal of thrombosis and thrombolysis
The pandemic of coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern. During this time, the management of people with acute coronary syndromes (ACS) and COVID-19 has become a global issue, especially since...

A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives.

Neuron
Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem. Recent advances in deep learning have tremendously advanced our ability to predict posture directly from videos, which has qu...