Feature Extraction and Analysis of fNIRS Signals Based on Linear Mapping Field

YAO Yuxuan; SUN Zhaohui; GAO Yubing; WU Qi

doi:10.11999/JEIT220120

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1401-1411

YAO Yuxuan, SUN Zhaohui, GAO Yubing, WU Qi. Feature Extraction and Analysis of fNIRS Signals Based on Linear Mapping Field[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1401-1411. doi: 10.11999/JEIT220120

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YAO Yuxuan, SUN Zhaohui, GAO Yubing, WU Qi. Feature Extraction and Analysis of fNIRS Signals Based on Linear Mapping Field[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1401-1411. doi: 10.11999/JEIT220120

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Feature Extraction and Analysis of fNIRS Signals Based on Linear Mapping Field

doi: 10.11999/JEIT220120

1.
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2.
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Funds: The National Natural Science Foundation of China (U1933125, 62171274), The Air Force Medical Research Major Project (2021KHYX11), The Defense Innovation Project (193-CXCY-A04-01-11-03), Shanghai Science and Technology Major Project (2021SHZDZX)

Received Date: 2022-01-27
Accepted Date: 2022-07-14
Rev Recd Date: 2022-06-29

Available Online: 2022-07-19

Publish Date: 2023-04-10

Abstract

Abstract

There are two major problems in the research on brain functional activation: feature extraction relies on experience; and it is difficult to mine deep physiological information. Focusing on these two problems, this paper proposes a self-adaptive Variational Mode Decomposition (VMD) algorithm by introducing the VMD technique. The algorithm considers the physiological significance of cerebral blood oxygen signals in different frequency bands and reduces the dependence on the selection of hyperparameters. The experimental results show that the self-adaptive VMD algorithm can accurately extract meaningful components in functional Near-InfraRed Spectroscopy (fNIRS), thereby improving the effect of preprocessing. Secondly, this paper proposes Linear Mapping Field (LMF) based on the idea of mapping time series into images and using deep convolutional neural networks for learning. Based on LMF, this paper maps the fNIRS sequence into a two-dimensional image with a low amount of computation supplemented by a deep convolutional neural network, and realizes the extraction of deep features of fNIRS physiological signals. The experimental results demonstrate the performance and advantages of the proposed LMF. Finally, this paper discusses and analyzes the effectiveness of the proposed methods, indicating that different from recurrent neural networks which can only perceive the time series in a “sequential” manner, the convolutional neural networks’ characteristic of "jumping" perception is the key to achieving excellent results.

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References(30)

References

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