Implementation of a real-time fNIRS signal quality assessment

Signal quality is crucial in any signal analysis. Typically, the reason for bad signal quality is inappropriate sensor placement which is also highly dependent on the measurement location. It is usually quite easy to get a good optical signal from finger, but not from the brain. This study aims to provide a real-time signal quality assessment method to help clinical personnel in placement of the fNIRS sensors on head to ensure good signal quality. Signal was segmented for each 10 seconds and a band-pass filter at 0.5-3 Hz was applied to isolate signal in cardiac band. Each segmented signal was subject to visual quality assessment to get bad, fair, and good labels. We used maximum to mean power ratio to generate signal quality index (SQI) score. Other methods included were skewness and kurtosis of the heart rate variability (HRV). Results showed that power ratio provides better consistency and separation among three different labels. Both skewness and kurtosis failed to separate fair and good segments. Using two threshold values, indices from power ration can be transformed into red (bad), yellow (fair), and green (good) alarm to help healthcare practitioners, who have no expertise to assess signal quality, to fix sensor placement to get good or acceptable signals.