Attention level assessment by means of HRV data extracted from fNIRS signals

Sustained attention is an important requirement for high performance in all cognitive processes. Quantifying the level of sustained attention to prevent attention lapses is therefore necessary for effective human-machine interfacing. Furthermore, sustained attention evaluation can help diagnose and treat attention deficit hyperactivity disorders. Attention level can be assessed by brain and heart signals. This study employed functional near infrared spectroscopy (fNIRS) and the heart rate variability (HRV) information extracted from the fNIRS signals to differentiate the rest and three levels of sustained attention states. Sustained attention states are induced by three modified versions of continuous performance tests (CPT). Eight subjects engaged in three sessions of attention tests. fNIRS brain signals were recorded from the right prefrontal and dorsolateral prefrontal cortex. HRV information was then extracted by processing the fNIRS signals. For attention classification, support vector machine (SVM), linear discriminant analysis (LDA), and random forest (RF) algorithms with mutual information based feature selection were applied on the fNIRS and HRV data both separately and together. In the classification of the three levels of attention using fNIRS and HRV data, the LDA classifier showed the best performance accuracy of (80.9 {+/-} 1.5%) and (56.2 {+/-} 1.0%), respectively. For two-class classification between the rest and the attention states (all together), the accuracies of (98.9 {+/-} 0.3%), (95.6 {+/-} 1.2%), and (99.5 {+/-} 0.2%) were obtained using the RF classifier on the fNIRS, HRV, and combined data, respectively. These results demonstrate the effectiveness of the HRV data for classifying sustained attention states. Moreover, using the combined fNIRS and HRV data provides better classification accuracy.