Biomarkers to detect Alzheimer’s disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy-to-use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity. and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (>=85% for sensitivity and 100% for specificity).
Quality of Experience (QoE) assessment of multimedia services is a challenging task and an understanding of how the user perceives quality at the physiological level would facilitate this. Physiological signals, such as the electroencephalogram (EEG), have shown promise in revealing the subject's emotion or attention in quality assessment and the correlation of this with media service quality. This paper investigated the relationships between changes in EEG features and subjective quality test scores (i.e. MOS) for High Dynamic Range (HDR) images viewed with a mobile device. Results show that changes in the gamma and beta bands correlated negatively with MOS, whereas positive correlations were observed in the alpha band. Coupling between activities in the delta and beta bands (i.e. positive correlation between power in the fast beta and slow delta frequency bands) is related to anxiety and dissatisfaction. Thus, the results suggest that increases in the degree of coupling are associated with decreases in HDR quality. This also suggests that in the HDR image QoE assessment, human emotions play a significant role. Potentially, these findings may be exploited in objective QoE perception modelling.
The development of HDR imaging is seen as an important step towards improving the visual quality of experience (QoE) of the end user in many applications. In practice, Tone-mapping operators (TMOs) provide a useful means for converting a high dynamic range (HDR) image to a low dynamic range image (LDR) in order to achieve better visualization on standard displays. Although mobile devices are becoming popular, the techniques for displaying the content of HDR images on the screens of such devices are still in the early stages. While several studies have been conducted to evaluate TMOs on conventional displays, few studies have been carried out to evaluate TMOs on small screen displays, such as those used in mobile devices. In this paper we evaluate, using subjective and objective methods, the most popular Tonemapping-operators in different mobile displays and resolutions under normal viewing conditions for the end-user. Preliminary results show that small screen displays (SSDs) have an impact on the performance of TMOs compared to computer displays. In general, the larger the mobile resolution, the better the subjective results. We also found clear differences between SSDs and LDRs performances. The best TMO for mobile displays is iCAM06 and for computer displays it is Photographic Reproduction.
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