Real-time decoding of the direction of covert visuospatial attention

J.P. Andersson, N.F. Ramsey, M. Raemaekers, M.A. Viergever, J.P.W. Pluim

Research output: Contribution to journalArticleAcademicpeer-review

24 Citations (Scopus)
13 Downloads (Pure)

Abstract

Brain-computer interfaces (BCIs) make it possible to translate a person's intentions into actions without depending on the muscular system. Brain activity is measured and classified into commands, thereby creating a direct link between the mind and the environment, enabling, e.g., cursor control or navigation of a wheelchair or robot. Most BCI research is conducted with scalp EEG but recent developments move toward intracranial electrodes for paralyzed people. The vast majority of BCI studies focus on the motor system as the appropriate target for recording and decoding movement intentions. However, properties of the visual system may make the visual system an attractive and intuitive alternative. We report on a study investigating feasibility of decoding covert visuospatial attention in real time, exploiting the full potential of a 7 T MRI scanner to obtain the necessary signal quality, capitalizing on earlier fMRI studies indicating that covert visuospatial attention changes activity in the visual areas that respond to stimuli presented in the attended area of the visual field. Healthy volunteers were instructed to shift their attention from the center of the screen to one of four static targets in the periphery, without moving their eyes from the center. During the first part of the fMRI-run, the relevant brain regions were located using incremental statistical analysis. During the second part, the activity in these regions was extracted and classified, and the subject was given visual feedback of the result. Performance was assessed as the number of trials where the real-time classifier correctly identified the direction of attention. On average, 80% of trials were correctly classified (chance level
Original languageEnglish
Article number045004
Pages (from-to)045004-1/10
JournalJournal of Neural Engineering
Volume9
Issue number4
DOIs
Publication statusPublished - 2012

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