Psychophysiological methods gained a lot of interest in recent years as a potential remedy for the inherent flaws of overt psychophysical quality assessment methods. Among the psychophysiological monitoring methods, electroencephalography showed to be a promising choice. Specifically, the steady state visually evoked potential (SSVEP) was shown to provide a reliable neural correlate of perceived visual quality for degraded texture image patches. This paper evaluates the feasibility of the SSVEP-based quality assessment approach for more realistic and practically relevant images. To this end, we collected overt, psychophysical responses and neural, psychophysiological responses of 14 participants to 6 HD images each compressed at 4 distortion levels. The psychophysical part followed the Degradation Category Rating procedure. In the subsequent psychophysiological part, the subjects were presented with distorted and reference images alternating at a fixed rate of $f_{stim}\,=5$ Hz to elicit the SSVEP. We show that the amplitude of the $1 ^{st}$ harmonic of the SSVEP correlates significantly with the psychophysical responses $(\vert \rho \vert = 0.85, p \lt 0.05)$ in a single channel analysis at the Oz electrode.

In this paper, we investigate the perception of realness in rendered face images experimentally using electroencephalography. To this end, we presented ten subjects with 36 character images based on six different faces (varying in gender and emotional expression) rendered at six different levels of realness ranging from abstract, cartoon-like renderings to real photographs. In the first psychophysical part of our study, we asked participants to rate perceived realness, appeal, familiarity, reassurance, and attractiveness for the presented characters. In the second part, we recorded the electroencephalogram when presenting the character images at a stimulation frequency of fstim= 5 Hz. We show that the amplitudes of the odd harmonics of the elicited steady-state visual evoked potential correlate with the psychophysical responses (|ρ| = 0.83, p < 0.05).

Steady-state visual evoked potentials (SSVEP) are brain responses elicited by periodic visual stimuli. Recently it was shown that the use of SSVEP in quality studies allows for accurate psychophysiological assessment of perceived visual quality, but the influence of the stimulation frequency is still unclear. This paper studies experimentally the relation between the SNR of the neural signal and the stimulation frequency in an psychophysiological quality assessment setup. For various source images tested at different distortion magnitudes over the range of 6 different stimulation frequencies, we show physiologically plausible results that provide insights into the temporal dynamics of neural distortion processing. Our findings inform a rational choice of stimulation frequency in SSVEP-based image quality assessment studies. This potentially improves the experimental setup of future image quality assessment studies exploiting the SSVEP paradigm.

Steady-state visual evoked potentials (SSVEPs) are neural responses, measurable using electroencephalography (EEG), that are directly linked to sensory processing of visual stimuli. In this paper, SSVEP is used to assess the perceived quality of texture images. The EEG-based assessment method is compared with conventional methods, and recorded EEG data are correlated to obtained mean opinion scores (MOSs). A dimensionality reduction technique for EEG data called spatio-spectral decomposition (SSD) is adapted for the SSVEP framework and used to extract physiologically meaningful and plausible neural components from the EEG recordings. It is shown that the use of SSD not only increases the correlation between neural features and MOS to r = -0.93, but also solves the problem of channel selection in an EEG-based image-quality assessment.

Conventionally, the quality of images and related codecs are assessed using subjective tests, such as Degradation Category Rating. These quality assessments consider the behavioral level only. Recently, it has been proposed to complement this approach by investigating how quality is processed in the brain of a user (using electroencephalography, EEG), potentially leading to results that are less biased by subjective factors. In this paper, a novel method is presented for assessing how image quality is processed on a neural level, using Steady-State Visual Evoked Potentials (SSVEPs) as EEG features. We tested our approach in an EEG study with 16 participants who were presented with distorted images of natural textures. Subsequently, we compared our approach analogously to the standardized Degradation Category Rating quality assessment. Remarkably, our novel method yields a correlation of |r| = 0.93 to MOS on the recorded dataset.

An approach to the direct measurement of perception of video quality change using electroencephalography (EEG) is presented. Subjects viewed 8-s video clips while their brain activity was registered using EEG. The video signal was either uncompressed at full length or changed from uncompressed to a lower quality level at a random time point. The distortions were introduced by a hybrid video codec. Subjects had to indicate whether they had perceived a quality change. In response to a quality change, a positive voltage change in EEG (the so-called P3 component) was observed at latency of about 400-600 ms for all subjects. The voltage change positively correlated with the magnitude of the video quality change, substantiating the P3 component as a graded neural index of the perception of video quality change within the presented paradigm. By applying machine learning techniques, we could classify on a single-trial basis whether a subject perceived a quality change. Interestingly, some video clips wherein changes were missed (i.e., not reported) by the subject were classified as quality changes, suggesting that the brain detected a change, although the subject did not press a button. In conclusion, abrupt changes of video quality give rise to specific components in the EEG that can be detected on a single-trial basis. Potentially, a neurotechnological approach to video assessment could lead to a more objective quantification of quality change detection, overcoming the limitations of subjective approaches (such as subjective bias and the requirement of an overt response). Furthermore, it allows for real-time applications wherein the brain response to a video clip is monitored while it is being viewed.

Video quality has turned out to be a crucial aspect of multimodal transmission services. Most common video quality tests rely on a conscious judgment of test participants reflecting their internal quality perception. But it is not completely clear how this conscious rating is formed, neither in the auditory nor in the visual domain. Initial audio tests with Electroencephalography (EEG) have shown that EEG recordings can be used as a sensitive and non-intrusive method for quality assessment. In this paper we conducted first experiments of pure video quality tests with EEG to complement this approach in the visual domain. One of the goals for this experiment was to show that there is a different pattern in the EEG data for cases with no distortion compared to cases when there was a distortion and the subject recognized this in the subjective test.

Brain Computer Interface (BCI) research advanced for more than forty years, providing a rich variety of sophisticated data analysis methods. Yet, most BCI studies have been restricted to the laboratory with controlled and undisturbed environment. BCI research was aiming at developing tools for communication and control. Recently, BCI research has broadened to explore novel applications for improved man-machine interaction. In the present study, we investigated the option to employ neurotechnology in an industrial environment for the psychophysiological optimization of working conditions in such settings. Our findings suggest that it is possible to use BCI-related analysis techniques to qualify responses of an operator by assessing the depth of cognitive processing on the basis of neuronal correlates of behaviourally relevant measures. This could lead to assistive technologies helping to avoid accidents in working environments by designing a collaborative workspace in which the environment takes into account the actual cognitive mental state of the operator.

Brains were built by evolution to react swiftly to environmental challenges. Thus, sensory stimuli must be processed ad hoc, i.e., independent--to a large extent--from the momentary brain state incidentally prevailing during stimulus occurrence. Accordingly, computational neuroscience strives to model the robust processing of stimuli in the presence of dynamical cortical states. A pivotal feature of ongoing brain activity is the regional predominance of EEG eigenrhythms, such as the occipital alpha or the pericentral mu rhythm, both peaking spectrally at 10 Hz. Here, we establish a novel generalized concept to measure event-related desynchronization (ERD), which allows one to model neural oscillatory dynamics also in the presence of dynamical cortical states. Specifically, we demonstrate that a somatosensory stimulus causes a stereotypic sequence of first an ERD and then an ensuing amplitude overshoot (event-related synchronization), which at a dynamical cortical state becomes evident only if the natural relaxation dynamics of unperturbed EEG rhythms is utilized as reference dynamics. Moreover, this computational approach also encompasses the more general notion of a "conditional ERD," through which candidate explanatory variables can be scrutinized with regard to their possible impact on a particular oscillatory dynamics under study. Thus, the generalized ERD represents a powerful novel analysis tool for extending our understanding of inter-trial variability of evoked responses and therefore the robust processing of environmental stimuli.

We present a method for binary on-line classification of triggered but temporally blurred events that are embedded in noisy time series in the context of on-line discrimination between left and right imaginary hand-movement. In particular the goal of the binary classification problem is to obtain the decision, as fast and as reliably as possible from the recorded EEG single trials. To provide a probabilistic decision at every time-point t the presented method gathers information from two distinct sequences of features across time. In order to incorporate decisions from prior time-points we suggest an appropriate weighting scheme, that emphasizes time instances, providing a higher discriminatory power between the instantaneous class distributions of each feature, where the discriminatory power is quantified in terms of the Bayes error of misclassification. The effectiveness of this procedure is verified by its successful application in the 3rd BCI competition. Disclosure of the data after the competition revealed this approach to be superior with single trial error rates as low as 10.7, 11.5 and 16.7% for the three different sub jects under study.

Brain-Computer Interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the clas- sification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability of multi-channel EEG single-trials. The eval- uation of 60 experiments involving 22 different subjects demonstrates the superiority of the proposed algorithm. Apart from the enhanced clas- sification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms.

Brain-Computer Interfaces (BCI) are an interesting emerging technology that is driven by the motivation to develop an effective communication in- terface translating human intentions into a control signal for devices like computers or neuroprostheses. If this can be done bypassing the usual hu- man output pathways like peripheral nerves and muscles it can ultimately become a valuable tool for paralyzed patients. Most activity in BCI re- search is devoted to finding suitable features and algorithms to increase information transfer rates (ITRs). The present paper studies the implica- tions of using more classes, e.g., left vs. right hand vs. foot, for operating a BCI. We contribute by (1) a theoretical study showing under some mild assumptions that it is practically not useful to employ more than three or four classes, (2) two extensions of the common spatial pattern (CSP) algorithm, one interestingly based on simultaneous diagonalization, and (3) controlled EEG experiments that underline our theoretical findings and show excellent improved ITRs.

Recently, interest is growing to develop an effective communication in- terface connecting the human brain to a computer, the ’Brain-Computer Interface’ (BCI). One motivation of BCI research is to provide a new communication channel substituting normal motor output in patients with severe neuromuscular disabilities. In the last decade, various neuro- physiological cortical processes, such as slow potential shifts, movement related potentials (MRPs) or event-related desynchronization (ERD) of spontaneous EEG rhythms, were shown to be suitable for BCI, and, con- sequently, different independent approaches of extracting BCI-relevant EEG-features for single-trial analysis are under investigation. Here, we present and systematically compare several concepts for combining such EEG-features to improve the single-trial classification. Feature combi- nations are evaluated on movement imagination experiments with 3 sub- jects where EEG-features are based on either MRPs or ERD, or both. Those combination methods that incorporate the assumption that the sin- gle EEG-features are physiologically mutually independent outperform the plain method of ’adding’ evidence where the single-feature vectors are simply concatenated. These results strengthen the hypothesis that MRP and ERD reflect at least partially independent aspects of cortical processes and open a new perspective to boost BCI effectiveness.

Driven by the progress in the field of single-trial analysis of EEG, there is a growing interest in brain computer interfaces (BCIs), i.e., systems that enable human subjects to control a computer only by means of their brain signals. In a pseudo-online simulation our BCI detects upcoming finger movements in a natural keyboard typing condition and predicts their lat- erality. This can be done on average 100–230 ms before the respective key is actually pressed, i.e., long before the onset of EMG. Our approach is appealing for its short response time and high classification accuracy (>96%) in a binary decision where no human training is involved. We compare discriminative classifiers like Support Vector Machines (SVMs) and different variants of Fisher Discriminant that possess favorable reg- ularization properties for dealing with high noise cases (inter-trial vari- ablity).