AI Medical Compendium Topic

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Assessment of mental workload based on multi-physiological signals.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Mental workload is one of the contributing factors to human errors in road accidents or other potentially adverse incidents.

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...

Working memory load-dependent changes in cortical network connectivity estimated by machine learning.

NeuroImage
Working memory engages multiple distributed brain networks to support goal-directed behavior and higher order cognition. Dysfunction in working memory has been associated with cognitive impairment in neuropsychiatric disorders. It is important to cha...

Human Cognition in Interaction With Robots: Taking the Robot's Perspective Into Account.

Human factors
OBJECTIVE: The present study investigated whether and how different human-robot interactions in a physically shared workspace influenced human stimulus-response (SR) relationships.

Rapid Recalibration of Peri-Personal Space: Psychophysical, Electrophysiological, and Neural Network Modeling Evidence.

Cerebral cortex (New York, N.Y. : 1991)
Interactions between individuals and the environment occur within the peri-personal space (PPS). The encoding of this space plastically adapts to bodily constraints and stimuli features. However, these remapping effects have not been demonstrated on ...

Deep Neural Network for Visual Stimulus-Based Reaction Time Estimation Using the Periodogram of Single-Trial EEG.

Sensors (Basel, Switzerland)
Multiplexed deep neural networks (DNN) have engendered high-performance predictive models gaining popularity for decoding brain waves, extensively collected in the form of electroencephalogram (EEG) signals. In this paper, to the best of our knowledg...

Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision.

PLoS computational biology
Deep feedforward neural network models of vision dominate in both computational neuroscience and engineering. The primate visual system, by contrast, contains abundant recurrent connections. Recurrent signal flow enables recycling of limited computat...

Engaging proactive control: Influences of diverse language experiences using insights from machine learning.

Journal of experimental psychology. General
We used insights from machine learning to address an important but contentious question: Is bilingual language experience associated with executive control abilities? Specifically, we assess proactive executive control for over 400 young adult biling...

Reducing Response Time in Motor Imagery Using A Headband and Deep Learning.

Sensors (Basel, Switzerland)
Electroencephalography (EEG) signals to detect motor imagery have been used to help patients with low mobility. However, the regular brain computer interfaces (BCI) capturing the EEG signals usually require intrusive devices and cables linked to mach...

Combining convolutional neural networks and cognitive models to predict novel object recognition in humans.

Journal of experimental psychology. Learning, memory, and cognition
Object representations from convolutional neural network (CNN) models of computer vision (LeCun, Bengio, & Hinton, 2015) were used to drive a cognitive model of decision making, the linear ballistic accumulator (LBA) model (Brown & Heathcote, 2008), ...