EEG dipole source localization with information criteria for multiple particle filters

Sho Sonoda, Keita Nakamura, Yuki Kaneda, Hideitsu Hino, Shotaro Akaho, Noboru Murata, Eri Miyauchi, Masahiro Kawasaki

    Research output: Contribution to journalArticle

    2 Citations (Scopus)

    Abstract

    Electroencephalography (EEG) is a non-invasive brain imaging technique that describes neural electrical activation with good temporal resolution. Source localization is required for clinical and functional interpretations of EEG signals, and most commonly is achieved via the dipole model; however, the number of dipoles in the brain should be determined for a reasonably accurate interpretation. In this paper, we propose a dipole source localization (DSL) method that adaptively estimates the dipole number by using a novel information criterion. Since the particle filtering process is nonparametric, it is not clear whether conventional information criteria such as Akaike's information criterion (AIC) and Bayesian information criterion (BIC) can be applied. In the proposed method, multiple particle filters run in parallel, each of which respectively estimates the dipole locations and moments, with the assumption that the dipole number is known and fixed; at every time step, the most predictive particle filter is selected by using an information criterion tailored for particle filters. We tested the proposed information criterion first through experiments on artificial datasets; these experiments supported the hypothesis that the proposed information criterion would outperform both AIC and BIC. We then analyzed real human EEG datasets collected during an auditory short-term memory task using the proposed method. We found that the alpha-band dipoles were localized to the right and left auditory areas during the auditory short-term memory task, which is consistent with previous physiological findings. These analyses suggest the proposed information criterion can work well in both model and real-world situations.

    Original languageEnglish
    Pages (from-to)68-82
    Number of pages15
    JournalNeural Networks
    Volume108
    DOIs
    Publication statusPublished - 2018 Dec 1

    Keywords

    • Auditory working memory task
    • Dipole source localization
    • Electroencephalography (EEG)
    • Information criterion
    • Particle filter

    ASJC Scopus subject areas

    • Cognitive Neuroscience
    • Artificial Intelligence

    Fingerprint Dive into the research topics of 'EEG dipole source localization with information criteria for multiple particle filters'. Together they form a unique fingerprint.

  • Cite this

    Sonoda, S., Nakamura, K., Kaneda, Y., Hino, H., Akaho, S., Murata, N., Miyauchi, E., & Kawasaki, M. (2018). EEG dipole source localization with information criteria for multiple particle filters. Neural Networks, 108, 68-82. https://doi.org/10.1016/j.neunet.2018.08.008