Estimation of the reaction times in tasks of varying difficulty from the phase coherence of the auditory steady-state response using the least absolute shrinkage and selection operator analysis

Quantitative estimation of the workload in the brain is an important factor for helping to predict the behavior of humans. The reaction time when performing a difficult task is longer than that when performing an easy task. Thus, the reaction time reflects the workload in the brain. In this study, we employed an N-back task in order to regulate the degree of difficulty of the tasks, and then estimated the reaction times from the brain activity. The brain activity that we used to estimate the reaction time was the auditory steady-state response (ASSR) evoked by a 40-Hz click sound. Fifteen healthy participants participated in the present study and magnetoencephalogram (MEG) responses were recorded using a 148-channel magnetometer system. The least absolute shrinkage and selection operator (LASSO), which is a type of sparse modeling, was employed to estimate the reaction times from the ASSR recorded by MEG. The LASSO showed higher estimation accuracy than the least squares method. This result indicates that LASSO overcame the over-fitting to the learning data. Furthermore, the LASSO selected channels in not only the parietal region, but also in the frontal and occipital regions. Since the ASSR is evoked by auditory stimuli, it is usually large in the parietal region. However, since LASSO also selected channels in regions outside the parietal region, this suggests that workload-related neural activity occurs in many brain regions. In the real world, it is more practical to use a wearable electroencephalography device with a limited number of channels than to use MEG. Therefore, determining which brain areas should be measured is essential. The channels selected by the sparse modeling method are informative for determining which brain areas to measure.