The study presents a recursive least‑squares estimation method with an exponen‑ tial forgetting factor for noise removal in functional near‑infrared spectroscopy data and extraction of hemodynamic responses (HRs) from the measured data. The HR is modeled as a linear regression form in which the expected HR, the first and second derivatives of the expected HR, a short‑separation measurement data, three physi‑ ological noises, and the baseline drift are included as components in the regression vector. The proposed method is applied to left‑motor‑cortex experiments on the right thumb and little finger movements in five healthy male participants. The algorithm is evaluated with respect to its performance improvement in terms of contrast‑to‑noise ratio in comparison with Kalman filter, low‑pass filtering, and independent component method. The experimental results show that the proposed model achieves reductions of 77% and 99% in terms of the number of channels exhibiting higher contrast‑to‑ noise ratios in oxy‑hemoglobin and deoxy‑hemoglobin, respectively. The approach is robust in obtaining consistent HR data. The proposed method is applied for both offline and online noise removal.