Journal
FRONTIERS IN NEUROSCIENCE
Volume 10, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2016.00175
Keywords
brain-computer interfaces; silent speech; electoencephalography; functional magnetic resonance imaging; inverse problem
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Funding
- JSPS [15K01849, 24500163, 15H01659, 26112004, 26120008]
- Japan Agency for Medical Research and Development, AMED
- Intramural Research Grant for Neurological and Psychiatric Disorders of National Center of Neurology and Psychiatry
- Grants-in-Aid for Scientific Research [15K01849, 16H03306, 26120008, 15H01659, 26112004, 24500163] Funding Source: KAKEN
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With the goal of providing assistive technology for the communication impaired, we proposed electroencephalography (EEG) cortical currents as a new approach for EEG-based brain-computer interface spellers. EEG cortical currents were estimated with a variational Bayesian method that uses functional magnetic resonance imaging (fMRI) data as a hierarchical prior. EEG and fMRI data were recorded from ten healthy participants during covert articulation of Japanese vowels /a/ and /i/, as well as during a no-imagery control task. Applying a sparse logistic regression (SLR) method to classify the three tasks, mean classification accuracy using EEG cortical currents was significantly higher than that using EEG sensor signals and was also comparable to accuracies in previous studies using electrocorticography. SLR weight analysis revealed vertices of EEG cortical currents that were highly contributive to classification for each participant, and the vertices showed discriminative time series signals according to the three tasks. Furthermore, functional connectivity analysis focusing on the highly contributive vertices revealed positive and negative correlations among areas related to speech processing. As the same findings were not observed using EEG sensor signals, our results demonstrate the potential utility of EEG cortical currents not only for engineering purposes such as brain-computer interfaces but also for neuroscientific purposes such as the identification of neural signaling related to language processing.
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