Passive BCI based on sustained attention detection: An fNIRS study

Abstract

Passive brain-computer interface (BCI) can monitor cognitive function through physiological signals in human-machine system. This paper established a passive BCI based on functional near-infrared spectroscopy (fNIRS) to detect the sustained attentional load. Three levels of attentional load were adjusted by modifying the number of stimulate in feature-absence Continuous Performance Test (CPT) tasks. 15 healthy subjects were recruited in total, and 10 channels were measured in prefrontal cortex (PFC). Performance and NASA-TLX scales were also recorded as reference. The mean value of oxyhemoglobin and deoxyhemoglobin, signal slope, power spectrum and approximate entropy in 0–10 s were extracted from raw fNIRS signal for support vector machine (SVM) classification. The best performance features were selected by SVM-RFE algorithm. In conclusion over 80% average accuracy was achived between easy and hard attentional load, which demonstrated fNIRS can be a proposed method to detect sustained attention load for a passive BCI.