Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9052
Main Title: Improving the analysis of near-infrared spectroscopy data with multivariate classification of hemodynamic patterns: a theoretical formulation and validation
Author(s): Gemignani, Jessica
Middell, Eike
Barbour, Randall L.
Graber, Harry L.
Blankertz, Benjamin
Type: Article
Language Code: en
Abstract: Objective. The statistical analysis of functional near infrared spectroscopy (fNIRS) data based on the general linear model (GLM) is often made difficult by serial correlations, high inter-subject variability of the hemodynamic response, and the presence of motion artifacts. In this work we propose to extract information on the pattern of hemodynamic activations without using any a priori model for the data, by classifying the channels as 'active' or 'not active' with a multivariate classifier based on linear discriminant analysis (LDA). Approach. This work is developed in two steps. First we compared the performance of the two analyses, using a synthetic approach in which simulated hemodynamic activations were combined with either simulated or real resting-state fNIRS data. This procedure allowed for exact quantification of the classification accuracies of GLM and LDA. In the case of real resting-state data, the correlations between classification accuracy and demographic characteristics were investigated by means of a Linear Mixed Model. In the second step, to further characterize the reliability of the newly proposed analysis method, we conducted an experiment in which participants had to perform a simple motor task and data were analyzed with the LDA-based classifier as well as with the standard GLM analysis. Main results. The results of the simulation study show that the LDA-based method achieves higher classification accuracies than the GLM analysis, and that the LDA results are more uniform across different subjects and, in contrast to the accuracies achieved by the GLM analysis, have no significant correlations with any of the demographic characteristics. Findings from the real-data experiment are consistent with the results of the real-plus-simulation study, in that the GLM-analysis results show greater inter-subject variability than do the corresponding LDA results. Significance. The results obtained suggest that the outcome of GLM analysis is highly vulnerable to violations of theoretical assumptions, and that therefore a data-driven approach such as that provided by the proposed LDA-based method is to be favored.
URI: https://depositonce.tu-berlin.de/handle/11303/10061
http://dx.doi.org/10.14279/depositonce-9052
Issue Date: 9-May-2018
Date Available: 4-Oct-2019
DDC Class: 006 Spezielle Computerverfahren
612 Humanphysiologie
Subject(s): functional Near Infrared Spectroscopy
General Linear Model
Linear Discriminant Analysis
fNIRS
GLM
LDA
hemodynamic
HRF
Sponsor/Funder: EC/H2020/641858/EU/Understanding and predicting developmental language abilities and disorders in multilingual Europe/PREDICTABLE
License: https://creativecommons.org/licenses/by-nc-nd/3.0/
Journal Title: Journal of Neural Engineering
Publisher: Institute of Physics Publishing (IOP)
Publisher Place: Bristol
Volume: 15
Issue: 4
Article Number: 045001
Publisher DOI: 10.1088/1741-2552/aabb7c
EISSN: 1741-2552
ISSN: 1741-2560
Appears in Collections:FG Neurotechnologie » Publications

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