System Derived Spatial-Temporal CNN for High-Density fNIRS BCI

Abstract

An intuitive and generalisable approach to spatial-temporal feature extraction for high-density (HD) functional Near-Infrared Spectroscopy (fNIRS) brain-computer interface (BCI) is proposed, demonstrated here using Frequency-Domain (FD) fNIRS for motor-task classification. Enabled by the HD probe design, layered topographical maps of Oxy/deOxy Haemoglobin changes are used to train a 3D convolutional neural network (CNN), enabling simultaneous extraction of spatial and temporal features. The proposed spatial-temporal CNN is shown to effectively exploit the spatial relationships in HD fNIRS measurements to improve the classification of the functional haemodynamic response, achieving an average F1 score of 0.69 across seven subjects in a mixed subjects training scheme, and improving subject-independent classification as compared to a standard temporal CNN.INDEX TERMS fNIRS, brain-computer interface, neural network, machine learning, CNN IMPACT STATEMENT The unique and often overlooked interdependence between fNIRS probe design and neural network architecture is demonstrated. A novel and effective BCI model is presented, which can be generalised to multi-wavelength, broadband, and hybrid fNIRS data.