Process Design for Optimized Respiration Identification Based on Heart Rate Variability for Efficient Respiratory Sinus Arrhythmia Biofeedback.

Respiratory sinus arrhythmia (RSA) is a phenomenon in which the heart rate (HR) changes with respiration, increasing during inspiration and decreasing during expiration. RSA biofeedback training has an effect in relieving negative mental conditions, such as anxiety and stress. Respiration is an important indicator affecting the parasympathetic activation within the body during RSA biofeedback training. Although there are existing studies that consider individual differences when selecting optimized respiration using heart rate variability, the studies that use the high frequency components of HRV, which is an indicator of parasympathetic activation, are insufficient. For this reason, this paper proposes a process to identify optimized respiration for efficient RSA feedback, consisting of three steps: (1) application, (2) optimization, and (3) validation. In the application phase, we measured PPG data against various respiratory cycles based on the HF components of HRV and calculated the proposed heart stabilization indicator (HSI) from the data. Then, we determined the optimized respiration cycle based on the HSI in the optimization step. Finally, we analyzed seven stress-related indices against the optimized respiration cycle. The experimental results show that HSI is associated with the parasympathetic nervous system activation, and the proposed method could help to determine the optimal respiratory cycle for each individual. Lastly, we expect that the proposed design could be used as an alternative to improving the efficiency of RSA biofeedback training.

Effect of a Recliner Chair with Rocking Motions on Sleep Efficiency.

In this study, we analyze the effect of a recliner chair with rocking motions on sleep quality of naps using automated sleep scoring and spindle detection models. The quality of sleep corresponding to the two rocking motions was measured quantitatively and qualitatively. For the quantitative evaluation, we conducted a sleep parameter analysis based on the results of the estimated sleep stages obtained on the brainwave and spindle estimation, and a sleep survey assessment from the participants was analyzed for the qualitative evaluation. The analysis showed that sleep in the recliner chair with rocking motions positively increased the duration of the spindles and deep sleep stage, resulting in improved sleep quality.

Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network.

Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain-computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects' ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.