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Distinguishing Difficulty Levels with Non-invasive Brain Activity Measurements

  • Audrey Girouard
  • Erin Treacy Solovey
  • Leanne M. Hirshfield
  • Krysta Chauncey
  • Angelo Sassaroli
  • Sergio Fantini
  • Robert J. K. Jacob
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5726)

Abstract

Passive brain-computer interfaces are designed to use brain activity as an additional input, allowing the adaptation of the interface in real time according to the user’s mental state. The goal of the present study is to distinguish between different levels of game difficulty using non-invasive brain activity measurement with functional near-infrared spectroscopy (fNIRS). The study is designed to lead to adaptive interfaces that respond to the user’s brain activity in real time. Nine subjects played two levels of the game Pacman while their brain activity was measured using fNIRS. Statistical analysis and machine learning classification results show that we can discriminate well between subjects playing or resting, and distinguish between the two levels of difficulty with some success. In contrast to most previous fNIRS studies which only distinguish brain activity from rest, we attempt to tell apart two levels of brain activity, and our results show potential for using fNIRS in an adaptive game or user interface.

Keywords

Brain-computer interface human cognition functional near-infrared spectroscopy fNIRS task classification game difficulty level 

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Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Audrey Girouard
    • 1
  • Erin Treacy Solovey
    • 1
  • Leanne M. Hirshfield
    • 1
  • Krysta Chauncey
    • 1
  • Angelo Sassaroli
    • 2
  • Sergio Fantini
    • 2
  • Robert J. K. Jacob
    • 1
  1. 1.Computer Science DepartmentUSA
  2. 2.Biomedical Engineering Department Tufts UniversityMedfordUSA

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