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Remove the motion artifacts in fNIRS using neural networks

Functional near-infrared spectroscopy (fNIRS) is an emerging technique for portable brain monitoring or real-time brain-computer interfaces. An attenuation of motion artifacts while conserving the original shape of motionless signals is crucial to obtaining an in-time and accurate user experience. Aiming to enable real-time filtering motion artifacts without additional hardware and not distorting the motionless signals, we proposed a solution combining simplified residual fully connected neural networks (sResFCNN) and a lowpass finite impulse response (FIR) filter. A set of fNIRS signals was simulated using a balloon model. Physiological noise, spike-like, and step-like motion artifacts were linearly added to the signals. By iteration over the input neuron numbers and the hidden layer numbers, the structure of sResFCNN was decided with the training and testing losses. The proposed solution was compared with spline interpolation-based filter (MARA), wavelet-based filter, dual-stage median filter (DSMF), and sResFCNN. Results showed that the proposed solution outperforms all other filters at one wavelength in both senses of noise suppression and signals distortion. At the other wavelength, the proposed solution has comparable performance as DSMF yet is not as good as MARA. Considering that MARA is an offline solution, the results suggest that MARA-filtered signals can be used as the reference signals during the evaluation of the proposed solution on experimental data in a future study.