A new generation of brain-computer interfaces driven by discovery of latent EEG-fMRI linkages using tensor decomposition

Gopikrishna Deshpande, D. Rangaprakash, Luke Oeding, Andrzej Cichocki, Xiaoping P. Hu

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)

    Abstract

    A Brain-Computer Interface (BCI) is a setup permitting the control of external devices by decoding brain activity. Electroencephalography (EEG) has been extensively used for decoding brain activity since it is non-invasive, cheap, portable, and has high temporal resolution to allow real-time operation. Due to its poor spatial specificity, BCIs based on EEG can require extensive training and multiple trials to decode brain activity (consequently slowing down the operation of the BCI). On the other hand, BCIs based on functional magnetic resonance imaging (fMRI) are more accurate owing to its superior spatial resolution and sensitivity to underlying neuronal processes which are functionally localized. However, due to its relatively low temporal resolution, high cost, and lack of portability, fMRI is unlikely to be used for routine BCI. We propose a new approach for transferring the capabilities of fMRI to EEG, which includes simultaneous EEG/fMRI sessions for finding a mapping from EEG to fMRI, followed by a BCI run from only EEG data, but driven by fMRI-like features obtained from the mapping identified previously. Our novel data-driven method is likely to discover latent linkages between electrical and hemodynamic signatures of neural activity hitherto unexplored using model-driven methods, and is likely to serve as a template for a novel multi-modal strategy wherein cross-modal EEG-fMRI interactions are exploited for the operation of a unimodal EEG system, leading to a new generation of EEG-based BCIs.

    Original languageEnglish
    Article number246
    JournalFrontiers in Neuroscience
    Volume11
    Issue numberJUN
    DOIs
    Publication statusPublished - 7 Jun 2017

    Keywords

    • Brain-computer interface
    • EEG
    • Functional MRI
    • Simultaneous EEG/fMRI
    • Tensor decomposition

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