Depth electrode neurofeedback with a virtual reality interface*

Hagar Grazya Yamin*, Tomer Gazit, Natalia Tchemodanov, Gal Raz, Gilan Jackont, Fred Charles, Itzhak Fried, Talma Hendler, Marc Cavazza

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Invasive brain–computer interfaces (BCI) provide better signal quality in terms of spatial localization, frequencies and signal/noise ratio, in addition to giving access to deep brain regions that play important roles in cognitive or affective processes. Despite some anecdotal attempts, little work has explored the possibility of integrating such BCI input into more sophisticated interactive systems like those which can be developed with game engines. In this article, we integrated an amygdala depth electrode recorder with a virtual environment controlling a virtual crowd. Subjects were asked to down regulate their amygdala using the level of unrest in the virtual room as feedback on how successful they were. We report early results which suggest that users adapt very easily to this paradigm and that the timing and fluctuations of amygdala activity during self-regulation can be matched by crowd animation in the virtual room. This suggests that depth electrodes could also serve as high-performance affective interfaces, notwithstanding their strictly limited availability, justified on medical grounds only.

Original languageEnglish
Pages (from-to)201-213
Number of pages13
JournalBrain-Computer Interfaces
Volume4
Issue number4
DOIs
StatePublished - 2 Oct 2017

Funding

FundersFunder number
European Union’s Seventh Framework Programme for research, technological development and demonstration
Sagol family fund
Seventh Framework Programme602186
Ministry of Science, Technology and Space3-11170

    Keywords

    • Application development and evaluation
    • Brain–computer interface (BCI)
    • electroencephalogram (EEG)
    • intracranial depth electrodes
    • neurofeedback (NF)
    • neurosurgical approaches and methods, affective computing
    • signal acquisition: EEG (other)

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