Brain–computer interfaces increase whole-brain signal to noise
Open Access
- 30 July 2013
- journal article
- research article
- Published by Proceedings of the National Academy of Sciences in Proceedings of the National Academy of Sciences of the United States of America
- Vol. 110 (33), 13630-13635
- https://doi.org/10.1073/pnas.1210738110
Abstract
Brain–computer interfaces (BCIs) can convert mental states into signals to drive real-world devices, but it is not known if a given covert task is the same when performed with and without BCI-based control. Using a BCI likely involves additional cognitive processes, such as multitasking, attention, and conflict monitoring. In addition, it is challenging to measure the quality of covert task performance. We used whole-brain classifier-based real-time functional MRI to address these issues, because the method provides both classifier-based maps to examine the neural requirements of BCI and classification accuracy to quantify the quality of task performance. Subjects performed a covert counting task at fast and slow rates to control a visual interface. Compared with the same task when viewing but not controlling the interface, we observed that being in control of a BCI improved task classification of fast and slow counting states. Additional BCI control increased subjects’ whole-brain signal-to-noise ratio compared with the absence of control. The neural pattern for control consisted of a positive network comprised of dorsal parietal and frontal regions and the anterior insula of the right hemisphere as well as an expansive negative network of regions. These findings suggest that real-time functional MRI can serve as a platform for exploring information processing and frontoparietal and insula network-based regulation of whole-brain task signal-to-noise ratio.This publication has 47 references indexed in Scilit:
- Cortical state and attentionNature Reviews Neuroscience, 2011
- Decoding fMRI brain states in real-timeNeuroImage, 2011
- Spatial Attention Improves the Quality of Population Codes in Human Visual CortexJournal of Neurophysiology, 2010
- Attention and biased competition in multi-voxel object representationsProceedings of the National Academy of Sciences of the United States of America, 2009
- Advances in visual perceptual learning and plasticityNature Reviews Neuroscience, 2009
- Correlations and brain states: from electrophysiology to functional imagingCurrent Opinion in Neurobiology, 2009
- Training Improves Multitasking Performance by Increasing the Speed of Information Processing in Human Prefrontal CortexNeuron, 2009
- A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networksProceedings of the National Academy of Sciences of the United States of America, 2008
- The Reorienting System of the Human Brain: From Environment to Theory of MindNeuron, 2008
- Motor ImageryStroke, 2006