A Real-Time Machine Learning Module for Motion Artifact Detection in fNIRS

Functional Near-Infrared Spectroscopy (fNIRS) is a neuroimaging method which can be implemented with a wearable form factor. However, the data of fNIRS can be affected by motion artifact, which is conventionally processed offline using MATLAB-based software package via a bulky PC. This study trains a Support Vector Machine (SVM) algorithm and proposes a hardware design approach based on an FPGA to achieve the first real-time fNIRS motion artifact detection. The SVM hardware architecture proposed here utilizes a partially sequential-partially parallel implementation of the classification algorithm where Support Vector channels are consolidated into a single oversampled channel. A high classification accuracy of 97.42%, low FPGA resource utilization of 38,354 look-up tables and 6024 flip-flops with 10.92 us latency is achieved, outperforming conventional CPU SVM methods. These results show that an FPGA-based fNIRS motion artifact detector can be exploited whilst meeting real-time and resource constraints that are crucial in high-performance reconfigurable hardware systems.