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Volume 46 Issue 3
Mar.  2024
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ZHANG Lixin, ZHOU Hongzhan, WANG Dong, MENG Jiayuan, XU Minpeng, MING Dong. Research Progress of ElectroEncephaloGraphy-Near-InfRared Spectroscopy Combined Analysis in Brain-Computer Interface[J]. Journal of Electronics & Information Technology, 2024, 46(3): 790-797. doi: 10.11999/JEIT230257
Citation: ZHANG Lixin, ZHOU Hongzhan, WANG Dong, MENG Jiayuan, XU Minpeng, MING Dong. Research Progress of ElectroEncephaloGraphy-Near-InfRared Spectroscopy Combined Analysis in Brain-Computer Interface[J]. Journal of Electronics & Information Technology, 2024, 46(3): 790-797. doi: 10.11999/JEIT230257

Research Progress of ElectroEncephaloGraphy-Near-InfRared Spectroscopy Combined Analysis in Brain-Computer Interface

doi: 10.11999/JEIT230257
Funds:  The National Key Research and Development Program of China(2021YFF1200600), The National Natural Science Foundation of China(62106173, 81925020),General Projects of Postdoctoral Science Foundation of China(2022M712364)
  • Received Date: 2023-04-12
  • Rev Recd Date: 2023-07-26
  • Available Online: 2023-08-02
  • Publish Date: 2024-03-27
  • Brain-Computer Interface (BCI) can convert the brain activity related to the subject's intention into external device control instructions, which have high application potential in treating neurological diseases, motor rehabilitation, and other aspects. Considering that the materialization of BCI needs to obtain meaningful signals from the human brain, ElectroEncephaloGraphy (EEG) and Near-InfRared Spectroscopy (NIRS) has become important signal acquisition methods for BCI because they are non-invasive, convenient to wear, and relatively cheap. EEG reflects neural electrical activity and is widely applied in BCI systems with high real-time response requirements; NIRS mainly reflects the level of hemodynamics and is mainly utilized in research with precise localization of active brain regions, such as identifying neurophysiological status. Compared with the single-mode BCI system, the BCI system based on EEG-NIRS combined analysis has attracted interest and research in physiological state detection, motor imagination, etc., because of its richer signal characteristics. This review begins with the application of EEG-NIRS combined data analysis in BCI, summarizes the current development on the data and feature fusion level, and looks forward to the research prospects of EEG-NIRS signal processing methods.
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