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A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy

Published on Jun 1, 2019in NeuroImage5.81
· DOI :10.1016/j.neuroimage.2019.06.021
Alexander von Lühmann (BU: Boston University), Zois Boukouvalas3
Estimated H-index: 3
(UMD: University of Maryland, College Park)
+ 1 AuthorsTülay Adali52
Estimated H-index: 52
(UMBC: University of Maryland, Baltimore County)
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Abstract
Abstract In the analysis of functional Near-Infrared Spectroscopy (fNIRS) signals from real-world scenarios, artifact rejection is essential. However, currently there exists no gold-standard. Although a plenitude of methodological approaches implicitly assume the presence of latent processes in the signals, elaborate Blind-Source-Separation methods have rarely been applied. A reason are challenging characteristics such as Non-instantaneous and non-constant coupling, correlated noise and statistical dependencies between signal components. We present a novel suitable BSS framework that tackles these issues by incorporating A) Independent Component Analysis methods that exploit both higher order statistics and sample dependency, B) multimodality, i.e., fNIRS with accelerometer signals, and C) Canonical-Correlation Analysis with temporal embedding. This enables analysis of signal components and rejection of motion-induced physiological hemodynamic artifacts that would otherwise be hard to identify. We implement a method for Blind Source Separation and Accelerometer based Artifact Rejection and Detection (BLISS A 2 RD). It allows the analysis of a novel n-back based cognitive workload paradigm in freely moving subjects, that is also presented in this manuscript. We evaluate on the corresponding data set and simulated ground truth data, making use of metrics based on 1st and 2nd order statistics and SNR and compare with three established methods: PCA, Spline and Wavelet-based artifact removal. Across 17 subjects, the method is shown to reduce movement induced artifacts by up to two orders of magnitude, improves the SNR of continuous hemodynamic signals in single channels by up to 10 d B , and significantly outperforms conventional methods in the extraction of simulated Hemodynamic Response Functions from strongly contaminated data. The framework and methods presented can serve as an introduction to a new type of multivariate methods for the analysis of fNIRS signals and as a blueprint for artifact rejection in complex environments beyond the applied paradigm.
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References72
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Published on Feb 13, 2018in Scientific Data
Jaeyoung Shin6
Estimated H-index: 6
(Hanyang University),
Alexander von Lühmann3
Estimated H-index: 3
(Technical University of Berlin)
+ 3 AuthorsKlaus-Robert Müller82
Estimated H-index: 82
Published on Jun 1, 2018
Zois Boukouvalas2
Estimated H-index: 2
(UMD: University of Maryland, College Park),
Yuri Levin-Schwartz5
Estimated H-index: 5
(ISMMS: Icahn School of Medicine at Mount Sinai)
+ 2 AuthorsTülay Adali52
Estimated H-index: 52
(UMBC: University of Maryland, Baltimore County)
Independent component analysis (ICA) is one of the most popular methods for blind source separation with a diverse set of applications, such as: biomedical signal processing, video and image analysis, and communications. The success of ICA is tied to proper characterization of the probability density function (PDF) of the latent sources; information that is generally unknown. In this work, we propose a new and efficient ICA algorithm based on entropy maximization with kernels, (ICA-EMK), which u...
Published on Apr 1, 2018 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
Qunfang Long1
Estimated H-index: 1
(UMBC: University of Maryland, Baltimore County),
Chunying Jia (UMBC: University of Maryland, Baltimore County)+ 3 AuthorsTülay Adali52
Estimated H-index: 52
(UMBC: University of Maryland, Baltimore County)
Independent component analysis (ICA) has found wide application in a variety of areas, and analysis of functional magnetic resonance imaging (fMRI) data has been a particularly fruitful one. Maximum likelihood provides a natural formuiation for ICA and allows one to take into account multiple statistical properties of the data-forms of diversity. While use of multiple types of diversity allows for additional flexibility, it comes at a cost, leading to high variability in the solution space. In t...
Published on Dec 1, 2017in Journal of Neural Engineering4.55
Wojciech Samek18
Estimated H-index: 18
,
Shinichi Nakajima13
Estimated H-index: 13
+ 1 AuthorsKlaus-Robert Müller82
Estimated H-index: 82
Jaeyoung Shin6
Estimated H-index: 6
,
Alexander von Lühmann3
Estimated H-index: 3
+ 4 AuthorsKobold Wouter Felix Muller1
Estimated H-index: 1
We provide an open access dataset for hybrid brain–computer interfaces (BCIs) using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we conducted two BCI experiments (left versus right hand motor imagery; mental arithmetic versus resting state). The dataset was validated using baseline signal analysis methods, with which classification performance was evaluated for each modality and a combination of both modalities. As already shown in previous literature, the capabi...
Zois Boukouvalas3
Estimated H-index: 3
(UMBC: University of Maryland, Baltimore County),
Yuri Levin-Schwartz5
Estimated H-index: 5
(UMBC: University of Maryland, Baltimore County)
+ 1 AuthorsTülay Adali52
Estimated H-index: 52
(UMBC: University of Maryland, Baltimore County)
Abstract Because of its wide applicability in various disciplines, blind source separation (BSS), has been an active area of research. For a given dataset, BSS provides useful decompositions under minimum assumptions typically by making use of statistical properties—types of diversity—of the data. Two popular types of diversity that have proven useful for many applications are statistical independence and sparsity . Although many methods have been proposed for the solution of the BSS problem tha...
Published on Jun 1, 2017in IEEE Transactions on Biomedical Engineering4.49
Alexander von Lühmann3
Estimated H-index: 3
(Technical University of Berlin),
Heidrun Wabnitz29
Estimated H-index: 29
(German National Metrology Institute)
+ 1 AuthorsKlaus-Robert Müller82
Estimated H-index: 82
(FU: Free University of Berlin)
Objective: For the further development of the fields of telemedicine, neurotechnology, and brain–computer interfaces, advances in hybrid multimodal signal acquisition and processing technology are invaluable. Currently, there are no commonly available hybrid devices combining bioelectrical and biooptical neurophysiological measurements [here electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS)]. Our objective was to design such an instrument in a miniaturized, customizab...
Published on Mar 9, 2016in Neurophotonics3.58
Ilias Tachtsidis26
Estimated H-index: 26
(UCL: University College London),
Felix Scholkmann18
Estimated H-index: 18
We highlight a significant problem that needs to be considered and addressed when performing functional near-infrared spectroscopy (fNIRS) studies, namely the possibility of inadvertently measuring fNIRS hemodynamic responses that are not due to neurovascular coupling. These can be misinterpreted as brain activity, i.e., "false positives" (errors caused by wrongly assigning a detected hemodynamic response to functional brain activity), or mask brain activity, i.e., "false negatives" (errors caus...
Published on Mar 2, 2016in Neurophotonics3.58
Theodore J. Huppert24
Estimated H-index: 24
(University of Pittsburgh)
Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low levels of light to measure changes in cerebral blood oxygenation levels. In the majority of NIRS functional brain studies, analysis of this data is based on a statistical comparison of hemodynamic levels between a baseline and task or between multiple task conditions by means of a linear regression model: the so-called general linear model. Although these methods are similar to their implementatio...
Published on Dec 1, 2015in IEEE Transactions on Biomedical Engineering4.49
Javier Andreu-Perez7
Estimated H-index: 7
(Imperial College London),
Daniel Leff18
Estimated H-index: 18
(Imperial College London)
+ 1 AuthorsGuang-Zhong Yang55
Estimated H-index: 55
(Imperial College London)
Objective: This paper discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorized into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing com...
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