A unified analytical framework with multiple fNIRS features for mental workload assessment in the prefrontal cortex

Lam Ghai Lim, Wei Chun Ung, Yee Ling Chan, Cheng-Kai Lu, Stephanie Sutoko, Tsukasa Funane, Masashi Kiguchi, Tong Boon Tang

Research output: Contribution to journalArticleResearchpeer-review

19 Citations (Scopus)

Abstract

Knowing the actual level of mental workload is important to ensure the efficacy of brain-computer interface (BCI) based cognitive training. Extracting signals from limited area of a brain region might not reveal the actual information. In this study, a functional near-infrared spectroscopy (fNIRS) device equipped with multi-channel and multi-distance measurement capability was employed for the development of an analytical framework to assess mental workload in the prefrontal cortex (PFC). In addition to the conventional features, e.g. hemodynamic slope, we introduced a new feature - deep contribution ratio which is the proportion of cerebral hemodynamics to the fNIRS signals. Multiple sets of features were examined by a simple logical operator to suppress the false detection rate in identifying the activated channels. Using the number of activated channels as input to a linear support vector machine (SVM), the performance of the proposed analytical framework was assessed in classifying three levels of mental workload. The best set of features involves the combination of hemodynamic slope and deep contribution ratio, where the identified number of activated channels returned an average accuracy of 80.6% in predicting mental workload, compared to a single conventional feature (accuracy: 59.8%). This suggests the feasibility of the proposed analytical framework with multiple features as a means towards a more accurate assessment of mental workload in fNIRS-based BCI applications.

Original languageEnglish
Pages (from-to)2367-2376
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume28
Issue number11
DOIs
Publication statusPublished - Nov 2020
Externally publishedYes

Keywords

  • Brain-computer interface
  • deep contribution ratio
  • functional near-infrared spectroscopy
  • mental workload
  • prefrontal cortex
  • working memory

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