NIRS-based language learning BCI system

Ko Watanabe, Hiroshi Tanaka, Kensho Takahashi, Yoshihito Niimura, Kajiro Watanabe, Yosuke Kurihara

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


This paper describes a non-invasive, less restrictive, stable measurement system for a brain-computer interface (BCI) for second-language (L2) learning. The system outputs the arousal of the Yerkes-Dodson law. We employ non-invasive nearinfrared spectroscopy (NIRS) as a basic device to measure the blood volume. However, the blood volume measured by NIRS includes base-line drift and is not stable. Here, we introduce a new drift-free variable defined as blood flow, which is the time derivative of the blood volume. Problems to be considered are: 1) Can the blood flow represent brain activity? 2) Where are the fewest brain areas strongly influenced by the language listening? 3) What parameter expresses arousal? We also present a measurement system. To verify the system, we carried out experiments with 40 listeners (10 advanced, 15 intermediate, and 15 novice listeners). When advanced L2 listeners were listening to the first and second languages, the distribution patterns of the root mean squares of the blood flow in the prefrontal regions were close to the correlation coefficient of 0.89, which shows that blood flow can represent brain activity in language processing. The center of BA10 and the right and left BA46 in the prefrontal regions were sufficient to detect language processing. The root mean squares of the differences of the left and right BA46 from BA10 peaked at a certain L2 readability level for all L2 listeners; they can be the parameter that expresses arousal. Thus, the measurement system can function as an input measurement device for BCI.

Original languageEnglish
Article number7387693
Pages (from-to)2726-2734
Number of pages9
JournalIEEE Sensors Journal
Issue number8
Publication statusPublished - 2016 Apr 15


  • Arousal measurement
  • BCI
  • L2 learning
  • NIRS

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


Dive into the research topics of 'NIRS-based language learning BCI system'. Together they form a unique fingerprint.

Cite this