Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance

Ahmet Omurtag, Haleh Aghajani, Hasan Onur Keles

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Objective. Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system's ability to decode mental states and compare it with its unimodal components. Approach. We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data. Main results. EEG+fNIRS's decoding accuracy was greater than that of its subsystems, partly due to the new type of neurovascular features made available by hybrid data. Significance. Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics.

Original languageEnglish (US)
Article number066003
JournalJournal of neural engineering
Volume14
Issue number6
DOIs
StatePublished - Oct 31 2017

Keywords

  • category fluency
  • electroencephalography (EEG)
  • functional near-infrared spectroscopy (fNIRS)
  • hybrid functional neuroimaging
  • mental state decoding
  • multi-modal brain recording
  • support vector machine

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience

Fingerprint Dive into the research topics of 'Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance'. Together they form a unique fingerprint.

Cite this