Towards Improving the“Detection” Power of Brain Imaging Experiments Using fNIRS

Zhu, Li; Peifer, Maria; Najafizadeh, Laleh

doi:10.1364/biomed.2014.bm3a.29

Cited by 2 publications

(2 citation statements)

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“…All volunteers gave their informed consent approved by the Rutgers University Institutional Review Board prior to the experiment. 38 The paradigm for this experiment was the 2-back task [see Fig. 3(a)], which has been widely used in determining brain Table 1 Summary of steps involved in DTW-based averaging procedure using a reference signal.…”

Section: Methodsmentioning

confidence: 99%

“…In reality, such inaccuracies in the averaged signal can lead to misunderstandings about the brain function. 38 To address the problem of temporal variation across fNIRS signals, in this paper, a dynamic time warping (DTW)-based averaging technique is presented for fNIRS-recorded time series. DTW algorithm was originally introduced in the field of speech processing 39 and has been widely used for measuring the similarity of two time series in various fields of research, such as biometric, data mining, gene expression analysis, human motion recognition, and EEG signal analysis.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic time warping-based averaging framework for functional near-infrared spectroscopy brain imaging studies

Zhu

Najafizadeh

2017

J. Biomed. Opt

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We investigate the problem related to the averaging procedure in functional near-infrared spectroscopy (fNIRS) brain imaging studies. Typically, to reduce noise and to empower the signal strength associated with task-induced activities, recorded signals (e.g., in response to repeated stimuli or from a group of individuals) are averaged through a point-by-point conventional averaging technique. However, due to the existence of variable latencies in recorded activities, the use of the conventional averaging technique can lead to inaccuracies and loss of information in the averaged signal, which may result in inaccurate conclusions about the functionality of the brain. To improve the averaging accuracy in the presence of variable latencies, we present an averaging framework that employs dynamic time warping (DTW) to account for the temporal variation in the alignment of fNIRS signals to be averaged. As a proof of concept, we focus on the problem of localizing task-induced active brain regions. The framework is extensively tested on experimental data (obtained from both block design and event-related design experiments) as well as on simulated data. In all cases, it is shown that the DTW-based averaging technique outperforms the conventional-based averaging technique in estimating the location of task-induced active regions in the brain, suggesting that such advanced averaging methods should be employed in fNIRS brain imaging studies.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%