No abstract

BSW intervention therapy is an effective way to treat fatigue and emesis. This virtual reality system is a new therapeutic method that can be used in palliative medicine.

The goal of our research is to develop a method to assess the arousal states using facial images of drivers. Multi-dimensional physiological indices are expected to be alternative external criteria of arousal states to manual coding of facial expression which require a lot of human resources. Changes in multi dimensional physiological indices (i.e., blink categories, skin conductance, EEG alpha wave, respiration, heart rate variability) depending on the arousal states defined by the combination of "arousal level" and the presence of "effort" to wake up were studied. Multiple linear regression analysis was also executed using one of the face scores ("arousal level" or "effort") as dependent variables and the physiological indices as explanatory variables. Relatively high multiple correlation coefficients were obtained, however, the number and combination of selected indices showed great differences between individuals. To obtain common equations is an issue in future.

This article describes how a virtual forest walk system (a bedside Wellness system) is expected to help cancer patients to relax and become more positive emotionally. It is also expected that the beautiful scenery (with wind, scents, and sounds) will refresh patients and that the evoked changes in the natural world will relax them. We have demonstrated the possible uses of virtual reality (VR) technology in the care of patients' mental health; however, we still have to improve the VR technology itself and investigate how to utilize it most effectively. It is essential that VR technology may be able to construct virtual, but realistic, environments that patients can enjoy without being caused additional stress or fatigue. This type of medical VR system may be effective in improving the quality of life of not only cancer patients, but also of the elderly.

We aimed to develop an input interface by using the P3 component of visual event-related potentials (ERPs). When using electroencephalography (EEG) in daily applications, coping with ocular-motor artifacts and ensuring that the equipment is user-friendly are both important. To address the first issue, we applied a previously proposed method that applies an unmixing matrix to acquire independent components (ICs) obtained from another dataset. For the second issue, we introduced a 14-channel EEG commercial headset called the "Emotiv EEG Neuroheadset". An advantage of the Emotiv headset is that users can put it on by themselves within 1 min without any specific skills. However, only a few studies have investigated whether EEG and ERP signals are accurately measured by Emotiv. Additionally, no electrodes of the Emotiv headset are located over the centroparietal area of the head where P3 components are reported to show large amplitudes. Therefore, we first demonstrated that the P3 components obtained by the headset and by commercial plate electrodes and a multipurpose bioelectric amplifier during an oddball task were comparable. Next, we confirmed that eye-blink and ocular movement components could be decomposed by independent component analysis (ICA) using the 14-channel signals measured by the headset. We also demonstrated that artifacts could be removed with an unmixing matrix, as long as the matrix was obtained from the same person, even if they were measured on different days. Finally, we confirmed that the fluctuation of the sampling frequency of the Emotiv headset was not a major problem.