title: EEG-based classification of video quality perception using steady state visual evoked potentials (SSVEPs).
abstract: OBJECTIVE
  Recent studies exploit the neural signal recorded via electroencephalography (EEG) to get a more objective measurement of perceived video quality. Most of these studies capitalize on the event-related potential component P3. We follow an alternative approach to the measurement problem investigating steady state visual evoked potentials (SSVEPs) as EEG correlates of quality changes. Unlike the P3, SSVEPs are directly linked to the sensory processing of the stimuli and do not require long experimental sessions to get a sufficient signal-to-noise ratio. Furthermore, we investigate the correlation of the EEG-based measures with the outcome of the standard behavioral assessment.
  
  
  METHODS
  As stimulus material, we used six gray-level natural images in six levels of degradation that were created by coding the images with the HM10.0 test model of the high efficiency video coding (H.265/MPEG-HEVC) using six different compression rates. The degraded images were presented in rapid alternation with the original images. In this setting, the presence of SSVEPs is a neural marker that objectively indicates the neural processing of the quality changes that are induced by the video coding. We tested two different machine learning methods to classify such potentials based on the modulation of the brain rhythm and on time-locked components, respectively.
  
  
  RESULTS
  Results show high accuracies in classification of the neural signal over the threshold of the perception of the quality changes. Accuracies significantly correlate with the mean opinion scores given by the participants in the standardized degradation category rating quality assessment of the same group of images.
  
  
  CONCLUSIONS
  The results show that neural assessment of video quality based on SSVEPs is a viable complement of the behavioral one and a significantly fast alternative to methods based on the P3 component.
authors.affiliation: Neurotechnology Group, Technische Universität Berlin, Berlin.
authors.forename: Laura
authors.initials: L
authors.lastname: Acqualagna
authors.forename: Sebastian
authors.initials: S
authors.lastname: Bosse
authors.forename: Anne K
authors.initials: AK
authors.lastname: Porbadnigk
authors.forename: Gabriel
authors.initials: G
authors.lastname: Curio
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
authors.forename: Thomas
authors.initials: T
authors.lastname: Wiegand
authors.forename: Benjamin
authors.initials: B
authors.lastname: Blankertz
mesh_terms: D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014741:Video Recording; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*; D055815:Young Adult