Matthew J. Boring, PhD.

Matthew J. Boring, PhD.

I am a cognitive vision scientist using biosensing and psychophysics to improve virtual and augmented reality.

Posts by Collection

portfolio

publications

Very high density EEG elucidates spatiotemporal aspects of early visual processing

Published in Nature Scientific Reports, 2017

In this paper we investigated whether electroencephalography systems [EEG] with higher density sensor arrays allowed us to non-invasively record more information from the brain. We found higher density systems captured more information than current state-of-the-art systems.

Recommended citation: Robinson AK, Venkatesh P, Boring MJ, Tarr MJ, Grover P, Behrmann M. Very high density EEG elucidates spatiotemporal aspects of early visual processing. Sci Rep. 2017;7: 1–11. https://www.nature.com/articles/s41598-017-16377-3

Quantitatively validating the efficacy of artifact suppression techniques to study the cortical consequences of deep brain stimulation with magnetoencephalography

Published in Neuroimage, 2019

In this work we sought to determine if magnetoencephalography [MEG] data preprocessing techniques were effective at cleansing magnetic artifacts induced by deep brain stimulating [DBS] electrodes. By comparing information contained in recordings collected from DBS patients and healthy controls we concluded that they were effective. This quantitatively demonstrated the efficacy of these techniques and opens the door for future studies investigating the effects of DBS using MEG.

Recommended citation: Boring MJ, Jessen ZF, Wozny TA, Ward MJ, Whiteman AC, Richardson RM, et al. Quantitatively validating the efficacy of artifact suppression techniques to study the cortical consequences of deep brain stimulation with magnetoencephalography. Neuroimage. 2019;199: 366–374. https://www.sciencedirect.com/science/article/abs/pii/S1053811919304768

Continuous decoding of cognitive load from electroencephalography reveals task-general and task-specific correlates

Published in Journal of Neural Engineering, 2020

Many studies have investigated how the brain reacts to changes in mental effort. However, most of those studies have restricted their investigations to very limited experimental contexts. In this study we use machine learning to predict how much mental effort a person is under given EEG activity across a variety of tasks. This represents a step forward for brain-computer interfaces and human-computer interaction systems that adapt computer behavior based on non-invasively recorded neural activity.

Recommended citation: Boring MJ, Ridgeway K, Shvartsman M, Jonker TR. Continuous decoding of cognitive load from electroencephalography reveals task-general and task-specific correlates. Journal of Neural Engineering. 2020;17: 056016. https://iopscience.iop.org/article/10.1088/1741-2552/abb9bc

Multiple adjoining word- and face-selective regions in ventral temporal cortex exhibit distinct dynamics

Published in Journal of Neuroscience, 2021

Here we map the complex networks of regions that process words and faces in the human ventral stream using a combination of intracranial EEG and ultra-high resolution fMRI.

Recommended citation: Boring MJ, Silson EH, Ward MJ, Richardson RM, Fiez JA, Baker CI, Ghuman AS. Multiple adjoining word- and face-selective regions in ventral temporal cortex exhibit distinct dynamics. Journal of Neuroscience. 41 (29) 6314-6327. https://www.jneurosci.org/content/41/29/6314

Bottom-up and top-down contributions to ventral temporal cortex organization and dynamics

Published in University of Pittsburgh, 2022

My dissertation details several research projects I completed during my PhD, joined together by the theme of investigating the spatial and temporal organization of category selectivity in ventral temporal cortex.

Recommended citation: Boring MJ. Bottom-up and top-down contributions to ventral temporal cortex organization and dynamics. University of Pittsburgh. http://d-scholarship.pitt.edu/42453/

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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