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Zhiming Xing1, Zihao Jin1, Shuqi Fang1
1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200020, China.
Sensors (Basel, Switzerland)
|March 28, 2024
Summary
This study introduces a novel dual-detector algorithm for functional near-infrared spectroscopy (fNIRS) to accurately detect brain activity by filtering physiological noise. The method enhances brain signal detection, even in low signal-to-noise ratio environments.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Functional near-infrared spectroscopy (fNIRS) measures brain activity via hemodynamic responses.
- Detecting brain activity in deep cortical regions is challenging due to physiological interference.
- Accuracy is crucial for understanding cognitive states and neurological conditions.
Purpose of the Study:
- To develop an effective algorithm for extracting genuine brain activity information using fNIRS.
- To improve the accuracy of brain activity detection by mitigating physiological noise.
- To validate the algorithm's performance against existing methods.
Main Methods:
- Utilized a dual-detector measurement approach for fNIRS.
- Employed short-distance channel measurements as reference signals to eliminate interference.
- Compared the proposed method with EEMD-RLS, RLS, and fast-ICA using simulated and real signals.
Main Results:
- The proposed algorithm effectively suppressed physiological interference in fNIRS signals.
- Improved detection accuracy of brain activity signals, particularly under low signal-to-noise ratio (SNR) conditions.
- Quantified performance using correlation coefficient (R), root-mean-square error (RMSE), and mean absolute error (MAE), showing superior results.
Conclusions:
- The novel dual-detector algorithm significantly enhances the reliability of fNIRS for brain activity monitoring.
- This method offers a robust solution for accurate brain signal extraction in the presence of physiological noise.
- The findings have implications for advancing non-invasive neuroimaging techniques.
