Purpose: Since the advent of smart phones, the amount of time spending on their use has rapidly increased and there are several health concerns regarding sight, causing prolonged smart phones viewing. The purpose of this study was to estimate how much eyes become fatigued based on quantitative electroencephalogram(qEEG) and to analyze the correlation between the fatigue and attention using event-related potentials(ERPs) as objective assessments. Methods: here were thirty, healthy, right-handed subjects(male 15, female 15) participating in this study. 64-channel of qEEG data with their eye-closed was collected and they performed Go/Nogo tasks before and after watching smart phones. A questionnaire regarding the visual fatigue was also collected in both conditions. The changes of pre- and post-task of watching smart phones were analyzed and compared in terms of four wavebands and algorithms, delta(δ), theta(θ), alpha(α), beta(β), (α+ θ)/ β, α/ β, (α+θ)/(α+β), and θ/ β for fatigue detection. Results: The results clearly show that all four energy values of qEEG and algorithms related to the fatigue in the post-viewing condition significantly changed than those in the pre-viewing condition. As seen in the results of ERP, nogo-N2 amplitude only on Fz electrode was slightly higher and nogo-P3 amplitudes on Fz and Cz were considerably lower in the post-task than in the pre-watching. However, there were no significant differences of go-N2 and P3 found at any electrodes. The results of behavioral performance demonstrate that the error rates(ER) of nogo-condition were obviously increased after using smart phones and there was a tendency for reaction time(RT) to be delayed compared with before watching it. Conclusions: This research denotes that it can be a fairly useful measure for using qEEG and ERP when assessing the visual fatigue. Its findings suggest that a sustained smart phone viewing leads to the visual fatigue and it can have adverse effects on distraction.

The underlying changes in biological processes that are associated with reported changes in mental and physical health in response to yoga breathing (prānāyāma) have not been systematically explored yet. In this study, the effects of a yoga breathing program on prefrontal EEG were tested with middle-aged women. Participants were collected as volunteers and controlled into two groups. Two channel EEG was recorded in the prefrontal region (Fp1, Fp2) from the yoga breathing group (n=17) and control group (n=17). QEEG quotients were transformed from the EEGs and analyzed by the ANOVAs on gain scores. As a result, α/δ (left, right) and CQ (correlation quotient) for yoga breathing participants were significantly decreased compared to control group (p〈.05). α/βH+α/δ (left, right) were increased significantly (p〈.05). For those significantly changed QEEG quotients, the interaction effects of Group x prefrontal alpha (α) and beta (β) asymmetry were tested. Only the α asymmetry showed main effect on the gain score of α/βH+α/δ (right) with F (1, 34)＝5.694 (p〈.05). Pearson's correlation coefficient between α asymmetry and gain score of α/βH+α/δ (right) was .374 (p〈.05). The gain score of α/βH+α/δ (right) was increased for the right α dominance of yoga breathing group. On the contrary it was decreased for the left α dominance of yoga breathing group as well as the control regardless of the dominance. The result of this study implies that yoga breathing increases stress resistance and is effective in the management of physical stress. Emotionally relaxed people may have greater instantaneous stress reduction after yoga breathing. Moreover, yoga breathing could be also beneficial for depressed who may be more vulnerable to stress.