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Full-Text Articles in Other Psychiatry and Psychology

Deep-Learning-Based Multivariate Pattern Analysis (Dmvpa): A Tutorial And A Toolbox, Karl M. Kuntzelman, Jacob M. Williams, Phui Cheng Lim, Ashtok Samal, Prahalada K. Rao, Matthew R. Johnson Mar 2021

Deep-Learning-Based Multivariate Pattern Analysis (Dmvpa): A Tutorial And A Toolbox, Karl M. Kuntzelman, Jacob M. Williams, Phui Cheng Lim, Ashtok Samal, Prahalada K. Rao, Matthew R. Johnson

Center for Brain, Biology, and Behavior: Faculty and Staff Publications

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on …


The Psychology Of Neurofeedback: Clinical Intervention Even If Applied Placebo, Robert T. Thibault, Amir Raz Oct 2017

The Psychology Of Neurofeedback: Clinical Intervention Even If Applied Placebo, Robert T. Thibault, Amir Raz

Psychology Faculty Articles and Research

Advocates of neurofeedback make bold claims concerning brain regulation, treatment of disorders, and mental health. Decades of research and thousands of peer-reviewed publications support neurofeedback using electroencephalography (EEG-nf); yet, few experiments isolate the act of receiving feedback from a specific brain signal as a necessary precursor to obtain the purported benefits. Moreover, while psychosocial parameters including participant motivation and expectation, rather than neurobiological substrates, seem to fuel clinical improvement across a wide range of disorders, for-profit clinics continue to sprout across North America and Europe. Here, we highlight the tenuous evidence supporting EEG-nf and sketch out the weaknesses of this …


Oscillatory Neural Responses Evoked By Natural Vestibular Stimuli In Humans, Steven Gale, Mario Prsa, Aaron Schurger, Annietta Gay, Aurore Paillard, Bruno Herbelin, Jean-Philippe Guyot, Christophe Lopez, Olaf Blanke Mar 2016

Oscillatory Neural Responses Evoked By Natural Vestibular Stimuli In Humans, Steven Gale, Mario Prsa, Aaron Schurger, Annietta Gay, Aurore Paillard, Bruno Herbelin, Jean-Philippe Guyot, Christophe Lopez, Olaf Blanke

Psychology Faculty Articles and Research

While there have been numerous studies of the vestibular system in mammals, less is known about the brain mechanisms of vestibular processing in humans. In particular, of the studies that have been carried out in humans over the last 30 years, none has investigated how vestibular stimulation (VS) affects cortical oscillations. Here we recorded high-density electroencephalography (EEG) in healthy human subjects and a group of bilateral vestibular loss patients (BVPs) undergoing transient and constant-velocity passive whole body yaw rotations, focusing our analyses on the modulation of cortical oscillations in response to natural VS. The present approach overcame significant technical challenges …


Reducing Multi-Sensor Data To A Single Time Course That Reveals Experimental Effects, Aaron Schurger, Sebastien Marti, Stanislas Dehaene Oct 2013

Reducing Multi-Sensor Data To A Single Time Course That Reveals Experimental Effects, Aaron Schurger, Sebastien Marti, Stanislas Dehaene

Psychology Faculty Articles and Research

Background: Multi-sensor technologies such as EEG, MEG, and ECoG result in high-dimensional data sets. Given the high temporal resolution of such techniques, scientific questions very often focus on the time-course of an experimental effect. In many studies, researchers focus on a single sensor or the average over a subset of sensors covering a “region of interest” (ROI). However, single-sensor or ROI analyses ignore the fact that the spatial focus of activity is constantly changing, and fail to make full use of the information distributed over the sensor array.

Methods: We describe a technique that exploits the optimality and …