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Other Neuroscience and Neurobiology Commons™
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Articles 1 - 6 of 6
Full-Text Articles in Other Neuroscience and Neurobiology
Self-Initiated Action Rescues Visual Targets From Backwards Masking, Amber R. Hopkins, Tian Lan, Aaron Schurger
Self-Initiated Action Rescues Visual Targets From Backwards Masking, Amber R. Hopkins, Tian Lan, Aaron Schurger
Psychology Faculty Articles and Research
When a visual target is followed by a visual mask stimulus, the target sometimes goes unseen. This is visual masking. In an experiment treating a masked visual stimulus as the sensory outcome of self-initiated action, we demonstrate that self-initiated action can rescue visual targets from backwards masks. Specifically, participants were correct about a visual target more often when the target-mask sequence appeared after their self-initiated action than when its onset was triggered externally. While self-initiated action sometimes induces sensory attenuation, whereby the perception of sensory events that follow actions is dampened, we observed that self-initiated action promotes the perception of …
Slow Ramping Emerges From Spontaneous Fluctuations In Spiking Neural Networks, Jake Gavenas, Ueli Rutishauer, Aaron Schurger, Uri Maoz
Slow Ramping Emerges From Spontaneous Fluctuations In Spiking Neural Networks, Jake Gavenas, Ueli Rutishauer, Aaron Schurger, Uri Maoz
Psychology Faculty Articles and Research
The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural-network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ~2 s before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns …
Dimensionality Reduction For Classification Of Object Weight From Electromyography, Elnaz Lashgari, Uri Maoz
Dimensionality Reduction For Classification Of Object Weight From Electromyography, Elnaz Lashgari, Uri Maoz
Psychology Faculty Articles and Research
Electromyography (EMG) is a simple, non-invasive, and cost-effective technology for measuring muscle activity. However, multi-muscle EMG is also a noisy, complex, and high-dimensional signal. It has nevertheless been widely used in a host of human-machine-interface applications (electrical wheelchairs, virtual computer mice, prosthesis, robotic fingers, etc.) and, in particular, to measure the reach-and-grasp motions of the human hand. Here, we developed an automated pipeline to predict object weight in a reach-grasp-lift task from an open dataset, relying only on EMG data. In doing so, we shifted the focus from manual feature-engineering to automated feature-extraction by using pre-processed EMG signals and thus …
An End-To-End Cnn With Attentional Mechanism Applied To Raw Eeg In A Bci Classification Task, Elnaz Lashgari, Jordan Ott, Akima Connelly, Pierre Baldi, Uri Maoz
An End-To-End Cnn With Attentional Mechanism Applied To Raw Eeg In A Bci Classification Task, Elnaz Lashgari, Jordan Ott, Akima Connelly, Pierre Baldi, Uri Maoz
Psychology Faculty Articles and Research
Objective. Motor-imagery (MI) classification base on electroencephalography (EEG) has been long studied in neuroscience and more recently widely used in healthcare applications such as mobile assistive robots and neurorehabilitation. In particular, EEG-based motor-imagery classification methods that rely on convolutional neural networks (CNNs) have achieved relatively high classification accuracy. However, naively training CNNs to classify raw EEG data from all channels, especially for high-density EEG, is computationally demanding and requires huge training sets. It often also introduces many irrelevant input features, making it difficult for the CNN to extract the informative ones. This problem is compounded by a dearth of training …
Neurofeedback With Fmri: A Critical Systematic Review, Robert T. Thibault, Amanda Macpherson, Michael Lifshitz, Raquel R. Roth, Amir Raz
Neurofeedback With Fmri: A Critical Systematic Review, Robert T. Thibault, Amanda Macpherson, Michael Lifshitz, Raquel R. Roth, Amir Raz
Psychology Faculty Articles and Research
Neurofeedback relying on functional magnetic resonance imaging (fMRI-nf) heralds new prospects for self-regulating brain and behavior. Here we provide the first comprehensive review of the fMRI-nf literature and the first systematic database of fMRI-nf findings. We synthesize information from 99 fMRI-nf experiments—the bulk of currently available data. The vast majority of fMRI-nf findings suggest that self-regulation of specific brain signatures seems viable; however, replication of concomitant behavioral outcomes remains sparse. To disentangle placebo influences and establish the specific effects of neurofeedback, we highlight the need for double-blind placebo-controlled studies alongside rigorous and standardized statistical analyses. Before fMRI-nf can join the …
Virtual Reality As A Training Tool To Treat Physical Inactivity In Children, Adam W. Kiefer, David Pincus, Michael J. Richardson, Gregory D. Myer
Virtual Reality As A Training Tool To Treat Physical Inactivity In Children, Adam W. Kiefer, David Pincus, Michael J. Richardson, Gregory D. Myer
Psychology Faculty Articles and Research
Lack of adequate physical activity in children is an epidemic that can result in obesity and other poor health outcomes across the lifespan. Physical activity interventions focused on motor skill competence continue to be developed, but some interventions, such as neuromuscular training (NMT), may be limited in how early they can be implemented due to dependence on the child’s level of cognitive and perceptual-motor development. Early implementation of motor-rich activities that support motor skill development in children is critical for the development of healthy levels of physical activity that carry through into adulthood. Virtual reality (VR) training may be beneficial …