Rethinking Delayed Hemodynamic Responses for fNIRS Classification
Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technology for monitoring cerebral hemodynamic responses. Enhancing fNIRS classification can improve the performance of brain-computer interfaces (BCIs). Currently, deep neural networks (DNNs) do not consider the inherent d...
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Published in | IEEE transactions on neural systems and rehabilitation engineering Vol. 31; p. 1 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
United States
IEEE
01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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ISSN | 1534-4320 1558-0210 1558-0210 |
DOI | 10.1109/TNSRE.2023.3330911 |
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Abstract | Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technology for monitoring cerebral hemodynamic responses. Enhancing fNIRS classification can improve the performance of brain-computer interfaces (BCIs). Currently, deep neural networks (DNNs) do not consider the inherent delayed hemodynamic responses of fNIRS signals, which causes many optimization and application problems. Considering the kernel size and receptive field of convolutions, delayed hemodynamic responses as domain knowledge are introduced into fNIRS classification, and a concise and efficient model named fNIRSNet is proposed. We empirically summarize three design guidelines for fNIRSNet. In subject-specific and subject-independent experiments, fNIRSNet outperforms other DNNs on open-access datasets. Specifically, fNIRSNet with only 498 parameters is 6.58% higher than convolutional neural network (CNN) with millions of parameters on mental arithmetic tasks and the floating-point operations (FLOPs) of fNIRSNet are much lower than CNN. Therefore, fNIRSNet is friendly to practical applications and reduces the hardware cost of BCI systems. It may inspire more research on knowledge-driven models for fNIRS BCIs. Code is available at https://github.com/wzhlearning/fNIRSNet. |
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AbstractList | Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technology for monitoring cerebral hemodynamic responses. Enhancing fNIRS classification can improve the performance of brain-computer interfaces (BCIs). Currently, deep neural networks (DNNs) do not consider the inherent delayed hemodynamic responses of fNIRS signals, which causes many optimization and application problems. Considering the kernel size and receptive field of convolutions, delayed hemodynamic responses as domain knowledge are introduced into fNIRS classification, and a concise and efficient model named fNIRSNet is proposed. We empirically summarize three design guidelines for fNIRSNet. In subject-specific and subject-independent experiments, fNIRSNet outperforms other DNNs on open-access datasets. Specifically, fNIRSNet with only 498 parameters is 6.58% higher than convolutional neural network (CNN) with millions of parameters on mental arithmetic tasks and the floating-point operations (FLOPs) of fNIRSNet are much lower than CNN. Therefore, fNIRSNet is friendly to practical applications and reduces the hardware cost of BCI systems. It may inspire more research on knowledge-driven models for fNIRS BCIs. Code is available at https://github.com/wzhlearning/fNIRSNet. Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technology for monitoring cerebral hemodynamic responses. Enhancing fNIRS classification can improve the performance of brain-computer interfaces (BCIs). Currently, deep neural networks (DNNs) do not consider the inherent delayed hemodynamic responses of fNIRS signals, which causes many optimization and application problems. Considering the kernel size and receptive field of convolutions, delayed hemodynamic responses as domain knowledge are introduced into fNIRS classification, and a concise and efficient model named fNIRSNet is proposed. We empirically summarize three design guidelines for fNIRSNet. In subject-specific and subject-independent experiments, fNIRSNet outperforms other DNNs on open-access datasets. Specifically, fNIRSNet with only 498 parameters is 6.58% higher than convolutional neural network (CNN) with millions of parameters on mental arithmetic tasks and the floating-point operations (FLOPs) of fNIRSNet are much lower than CNN. Therefore, fNIRSNet is friendly to practical applications and reduces the hardware cost of BCI systems. It may inspire more research on knowledge-driven models for fNIRS BCIs. Code is available at https://github.com/wzhlearning/fNIRSNet.Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technology for monitoring cerebral hemodynamic responses. Enhancing fNIRS classification can improve the performance of brain-computer interfaces (BCIs). Currently, deep neural networks (DNNs) do not consider the inherent delayed hemodynamic responses of fNIRS signals, which causes many optimization and application problems. Considering the kernel size and receptive field of convolutions, delayed hemodynamic responses as domain knowledge are introduced into fNIRS classification, and a concise and efficient model named fNIRSNet is proposed. We empirically summarize three design guidelines for fNIRSNet. In subject-specific and subject-independent experiments, fNIRSNet outperforms other DNNs on open-access datasets. Specifically, fNIRSNet with only 498 parameters is 6.58% higher than convolutional neural network (CNN) with millions of parameters on mental arithmetic tasks and the floating-point operations (FLOPs) of fNIRSNet are much lower than CNN. Therefore, fNIRSNet is friendly to practical applications and reduces the hardware cost of BCI systems. It may inspire more research on knowledge-driven models for fNIRS BCIs. Code is available at https://github.com/wzhlearning/fNIRSNet. |
Author | Wang, Zenghui Zhang, Jun Fang, Jihong |
Author_xml | – sequence: 1 givenname: Zenghui orcidid: 0000-0002-0803-6206 surname: Wang fullname: Wang, Zenghui organization: School of Electrical Engineering and Automation, Anhui University, Hefei, China – sequence: 2 givenname: Jihong surname: Fang fullname: Fang, Jihong organization: Nursing Department, Anhui Provincial Children's Hospital, Hefei, China – sequence: 3 givenname: Jun orcidid: 0000-0002-5985-8023 surname: Zhang fullname: Zhang, Jun organization: School of Artificial Intelligence, Anhui University, Hefei, China |
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Snippet | Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technology for monitoring cerebral hemodynamic responses. Enhancing fNIRS... |
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SubjectTerms | Artificial neural networks Brain-Computer Interfaces brain–computer interface (BCI) Classification Computational efficiency Convolution deep neural network (DNN) delayed hemodynamic response domain knowledge Feature extraction Floating point arithmetic Functional near-infrared spectroscopy Functional near-infrared spectroscopy (fNIRS) Hemodynamic responses Hemodynamics Human-computer interface Humans Infrared spectra Infrared spectroscopy Kernel Mathematical models Mathematics Medical imaging Near infrared radiation Neural networks Neural Networks, Computer Neuroimaging Optimization Parameters Receptive field Spectroscopy Spectroscopy, Near-Infrared - methods |
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Title | Rethinking Delayed Hemodynamic Responses for fNIRS Classification |
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