AIMC Topic: Sensorimotor Cortex
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Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback.
Sensors (Basel, Switzerland)
Mar 14, 2020
Optimizing neurofeedback (NF) and brain-computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user's control ability f...
Endocannabinoid degradation inhibitors ameliorate neuronal and synaptic alterations following traumatic brain injury.
Journal of neurophysiology
Jan 8, 2020
Our previous work showed that lateral fluid percussion injury to the sensorimotor cortex (SMC) of anesthetized rats increased neuronal synaptic hyperexcitability in layer 5 (L5) neurons in ex vivo brain slices 10 days postinjury. Furthermore, endocan...
Enzyme Inhibitors
Carbamates
Amidohydrolases
Succinimides
Benzamides
Sensorimotor Cortex
Pyramidal Cells
Rats
Endocannabinoids
Electrophysiological Phenomena
Brain Injuries, Traumatic
Benzodioxoles
Machine Learning
Excitatory Postsynaptic Potentials
Piperazines
Monoacylglycerol Lipases
Animals
Piperidines
Male
Rats, Wistar
A versatile robotic platform for the design of natural, three-dimensional reaching and grasping tasks in monkeys.
Journal of neural engineering
Dec 5, 2019
OBJECTIVE: Translational studies on motor control and neurological disorders require detailed monitoring of sensorimotor components of natural limb movements in relevant animal models. However, available experimental tools do not provide a sufficient...
Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex.
PLoS computational biology
Sep 16, 2019
A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear ...
A simulation-based approach to improve decoded neurofeedback performance.
NeuroImage
Apr 4, 2019
The neural correlates of specific brain functions such as visual orientation tuning and individual finger movements can be revealed using multivoxel pattern analysis (MVPA) of fMRI data. Neurofeedback based on these distributed patterns of brain acti...
A Single Session of Robot-Controlled Proprioceptive Training Modulates Functional Connectivity of Sensory Motor Networks and Improves Reaching Accuracy in Chronic Stroke.
Neurorehabilitation and neural repair
Dec 29, 2018
BACKGROUND: Passive robot-generated arm movements in conjunction with proprioceptive decision making and feedback modulate functional connectivity (FC) in sensory motor networks and improve sensorimotor adaptation in normal individuals. This proof-of...
MRI-compatible pneumatic stimulator for sensorimotor mapping.
Journal of neuroscience methods
Dec 19, 2018
BACKGROUND: Two major concerns with respect to task-based motor functional magnetic resonance imaging (fMRI) are inadequate participants' performance as well as intra- and inter-subject variability in execution of the motor action.
Representational fluidity in embodied (artificial) cognition.
Bio Systems
Aug 7, 2018
Theories of embodied cognition agree that the body plays some role in human cognition, but disagree on the precise nature of this role. While it is (together with the environment) fundamentally engrained in the so-called 4E (or multi-E) cognition sta...
Encoding of Articulatory Kinematic Trajectories in Human Speech Sensorimotor Cortex.
Neuron
May 17, 2018
When speaking, we dynamically coordinate movements of our jaw, tongue, lips, and larynx. To investigate the neural mechanisms underlying articulation, we used direct cortical recordings from human sensorimotor cortex while participants spoke natural ...
Decoding of finger trajectory from ECoG using deep learning.
Journal of neural engineering
Nov 28, 2017
OBJECTIVE: Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained...
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