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

Gupta, Cota Navin, Palaniappan, Ramaswamy (2013) Using EEG and NIRS for brain-computer interface and cognitive performance measures: a pilot study. International Journal of Cognitive Performance Support, 1 (1). p. 69. ISSN 1742-7207. (doi:10.1504/IJCPS.2013.053576)

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http://dx.doi.org/10.1504/IJCPS.2013.053576

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.

Item Type: Article
DOI/Identification number: 10.1504/IJCPS.2013.053576
Uncontrolled 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.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.9.H85 Human computer interaction
R Medicine > R Medicine (General) > R858 Medical informatics
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 03 Sep 2015 17:22 UTC
Last Modified: 01 Aug 2019 10:39 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50391 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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