Classification of mental tasks in the prefrontal cortex using fNIRSby C. Herff, D. Heger, F. Putze, J. Hennrich, O. Fortmann, T. Schultz
Abstract:
Functional near infrared spectroscopy (fNIRS) is rapidly gaining interest in both the Neuroscience, as well as the Brain-Computer-Interface (BCI) community. Despite these efforts, most single-trial analysis of fNIRS data is focused on motor-imagery, or mental arithmetics. In this study, we investigate the suitability of different mental tasks, namely mental arithmetics, word generation and mental rotation for fNIRS based BCIs. We provide the first systematic comparison of classification accuracies achieved in a sample study. Data was collected from 10 subjects performing these three tasks. An optode template with 8 channels was chosen which covers the prefrontal cortex and only requires less than 3 minutes for setup. Two-class accuracies of up to 71% average across all subjects for mental arithmetics, 70% for word generation and 62% for mental rotation were achieved discriminating these tasks from a relax state. We thus lay the foundation for fNIRS based BCI using additional mental strategies than motor imagery and mental arithmetics. The tasks were chosen in a way that they might be used for user state monitoring, as well.
Reference:
Classification of mental tasks in the prefrontal cortex using fNIRS (C. Herff, D. Heger, F. Putze, J. Hennrich, O. Fortmann, T. Schultz), In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, 2013.
Bibtex Entry:
@INPROCEEDINGS{6609962,
author={Herff, C. and Heger, D. and Putze, F. and Hennrich, J. and Fortmann, O. and Schultz, T.},
booktitle={Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE},
title={Classification of mental tasks in the prefrontal cortex using fNIRS},
year={2013},
pages={2160-2163},
abstract={Functional near infrared spectroscopy (fNIRS) is rapidly gaining interest in both the Neuroscience, as well as the Brain-Computer-Interface (BCI) community. Despite these efforts, most single-trial analysis of fNIRS data is focused on motor-imagery, or mental arithmetics. In this study, we investigate the suitability of different mental tasks, namely mental arithmetics, word generation and mental rotation for fNIRS based BCIs. We provide the first systematic comparison of classification accuracies achieved in a sample study. Data was collected from 10 subjects performing these three tasks. An optode template with 8 channels was chosen which covers the prefrontal cortex and only requires less than 3 minutes for setup. Two-class accuracies of up to 71% average across all subjects for mental arithmetics, 70% for word generation and 62% for mental rotation were achieved discriminating these tasks from a relax state. We thus lay the foundation for fNIRS based BCI using additional mental strategies than motor imagery and mental arithmetics. The tasks were chosen in a way that they might be used for user state monitoring, as well.},
keywords={arithmetic;biomedical equipment;brain-computer interfaces;cognition;feature extraction;fibre optic sensors;infrared spectroscopy;medical signal processing;neurophysiology;patient monitoring;signal classification;brain-computer interface;classification accuracy;fNIRS based BCI;fNIRS data single-trial analysis;functional near infrared spectroscopy;mental arithmetics;mental rotation;mental strategy;mental task classification;motor imagery;motor-imagery;neuroscience;optode template channel;prefrontal cortex;relax state;user state monitoring;word generation;Accuracy;Electroencephalography;Feature extraction;Hemodynamics;Neuroscience;Spectroscopy;Systematics},
url={https://www.csl.uni-bremen.de/cms/images/documents/publications/HerffSchultz_EMBC2013.pdf},
doi={10.1109/EMBC.2013.6609962},
ISSN={1557-170X},
month={July},}