Incorporating modern neuroscience findings to improve brain-computer interfaces: tracking auditory attention
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Updated: Mar 15, 2026
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
Published on: October 24, 2012
Mark Wronkiewicz1, Eric Larson2, Adrian Kc Lee1,2,3
1Graduate Program in Neuroscience, University of Washington, Box 357270, Seattle, WA 98195, USA.
Objective:
Brain-computer interface (BCI) technology allows users to generate actions based solely on their brain signals. However, current non-invasive BCIs generally classify brain activity recorded from surface electroencephalography (EEG) electrodes, which can hinder the application of findings from modern neuroscience research.
Approach:
In this study, we use source imaging-a neuroimaging technique that projects EEG signals onto the surface of the brain-in a BCI classification framework. This allowed us to incorporate prior research from functional neuroimaging to target activity from a cortical region involved in auditory attention.
Main Results:
Classifiers trained to detect attention switches performed better with source imaging projections than with EEG sensor signals. Within source imaging, including subject-specific anatomical MRI information (instead of using a generic head model) further improved classification performance. This source-based strategy also reduced accuracy variability across three dimensionality reduction techniques-a major design choice in most BCIs.
Significance:
Our work shows that source imaging provides clear quantitative and qualitative advantages to BCIs and highlights the value of incorporating modern neuroscience knowledge and methods into BCI systems.
