研究目的
To present a recursive least?squares estimation method with an exponential forgetting factor for noise removal in functional near?infrared spectroscopy data and extraction of hemodynamic responses (HRs) from the measured data.
研究成果
The proposed method improved the accuracy of the estimated HR and significantly reduced physiological noises, with average noise reductions for HbO and HbR of 77% and 99%, respectively. The method is suitable for both offline and online applications.
研究不足
The study was limited to five healthy male participants, and the sampling rate for the bundled arrangement was limited to 1.81 Hz. The issues of measuring different brain regions and motion-artifact removal were not addressed.
1：Experimental Design and Method Selection:
The study utilized a recursive least?squares estimation method with an exponential forgetting factor for noise removal in functional near?infrared spectroscopy data. The HR was modeled as a linear regression form including the expected HR, its first and second derivatives, a short?separation measurement data, three physiological noises, and the baseline drift.
2：Sample Selection and Data Sources:
Five healthy male participants performed right thumb and little finger movements, with fNIRS data collected over the left motor cortex.
3：List of Experimental Equipment and Materials:
Dual-wavelength continuous-wave fNIRS (DYNOT, NIRx, USA) was used to measure the brain’s hemodynamic responses.
4：Experimental Procedures and Operational Workflow:
The experiment comprised ten trials of finger movements, each consisting of a 10 s task and a 20 s rest. The proposed method was applied to both offline and online noise removal.
5：Data Analysis Methods:
The efficacy of the proposed method was evaluated in terms of contrast?to?noise ratio compared with Kalman filter, low?pass filtering, and independent component method.
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