Title: Using EEG and NIRS for brain-computer interface and cognitive performance measures: a pilot study

Authors: Cota Navin Gupta; Ramaswamy Palaniappan

Addresses: The Mind Research Network, Albuquerque, New Mexico, 87106 USA; School of Computer Science and Electronics Engineering, University of Essex, Colchester, UK ' Department of Engineering, School of Technology, University of Wolverhampton, Room SC035, Shifnal Road, Priorslee, TF2 9NT, Telford, UK

Abstract: This study addresses two important problem statements, namely, selection of training datasets for online Brain-Computer Interface (BCI) classifier training and determination of participant concentration levels during an experiment. The work also attempted a pilot study to integrate electroencephalograms (EEGs) and Near Infra Red Spectroscopy (NIRS) for possible applications such as the BCI and for measuring cognitive levels. Two experiments are presented, the first being a mathematical task interleaved with rest states using NIRS only. In the next, integration of the EEG-NIRS with reference to P300-based BCI systems as well as the experimental conditions designed to elicit the concentration levels (denoted as ON and OFF states here) during the paradigm, are presented. The first experiment indicates that NIRS can be used to differentiate a concentrated (i.e., mental activity) level from the rest. However, the second experiment reveals statistically significant results using the EEG only. We present details about the equipment used, the participants as well as the signal processing and machine learning techniques implemented to analyse the EEG and NIRS data. After discussing the results, we conclude by describing the research scope as well as the possible pitfalls in this work from a NIRS viewpoint, which presents an opportunity for future research exploration for BCI and cognitive performance measures.

Keywords: BCI; brain-computer interface; cognitive performance; EEG; electroencephalograms; NIRS; near infra red spectroscopy; P300; performance measures; classifier training; concentration levels; signal processing; machine learning.

DOI: 10.1504/IJCPS.2013.053576

International Journal of Cognitive Performance Support, 2013 Vol.1 No.1, pp.69 - 81

Received: 02 May 2012
Accepted: 07 Oct 2012
Published online: 18 Sep 2014 *