A wellness platform for stereoscopic 3D video systems using EEG-based visual discomfort evaluation technology. (July 2017) - Kang, Min-Koo; Cho, Hohyun; Park, Han-Mu; Jun, Sung Chan; Yoon, Kuk-Jin
Abstract: Recent advances in three-dimensional (3D) video technology have extended the range of our experience while providing various 3D applications to our everyday life. Nevertheless, the so-called visual discomfort (VD) problem inevitably degrades the quality of experience in stereoscopic 3D (S3D) displays. Meanwhile, electroencephalography (EEG) has been regarded as one of the most promising brain imaging modalities in the field of cognitive neuroscience. In an effort to facilitate comfort with S3D displays, we propose a new wellness platform using EEG. We first reveal features in EEG signals that are applicable to practical S3D video systems as an index for VD perception. We then develop a framework that can automatically determine severe perception of VD based on the EEG features during S3D video viewing by capitalizing on machine-learning-based braincomputer interface technology. The proposed platform can cooperate with advanced S3D video systems whose stereo baseline is adjustable. Thus, the optimal S3D content can be reconstructed according to a viewer's sensation of VD. Applications of the proposed platform to various S3D industries are suggested, and further technical challenges are discussed for follow-up research. Highlights: A new concept of wellness platform leading to comfort stereoscopic 3D video is proposed. The platform exploits the current EEG-based visual discomfort evaluation techniques. Technical challenges of the previous method are mainly overcome for practical use. Despite very natural viewing environment, distinct EEG components are elicited. Visual discomfort detector automatically provides feedback with 80% accuracy.