Distinct Spectral Profiles of Awake Resting EEG in Disorders of Consciousness: The Role of Frequency and Topography of Oscillations.

The prolonged disorders of consciousness (PDOC) pose a challenge for an accurate clinical diagnosis, mainly due to patients' scarce or ambiguous behavioral responsiveness. Measurement of brain activity can support better diagnosis, independent of motor restrictions. Methods based on spectral analysis of resting-state EEG appear as a promising path, revealing specific changes within the internal brain dynamics in PDOC patients. In this study we used a robust method of resting-state EEG power spectrum parameter extraction to identify distinct spectral properties for different types of PDOC. Sixty patients and 37 healthy volunteers participated in this study. Patient group consisted of 22 unresponsive wakefulness patients, 25 minimally conscious patients and 13 patients emerging from the minimally conscious state. Ten minutes of resting EEG was acquired during wakefulness and transformed into individual power spectra. For each patient, using the spectral decomposition algorithm, we extracted maximum peak frequency within 1-14 Hz range in the centro-parietal region, and the antero-posterior (AP) gradient of the maximal frequency peak. All patients were behaviorally diagnosed using coma recovery scale-revised (CRS-R). The maximal peak frequency in the 1-14 Hz range successfully predicted both neurobehavioral capacity of patients as indicated by CRS-R total score and PDOC diagnosis. Additionally, in patients in whom only one peak within the 1-14 Hz range was observed, the AP gradient significantly contributed to the accuracy of prediction. We have identified three distinct spectral profiles of patients, likely representing separate neurophysiological modes of thalamocortical functioning. Etiology did not have significant influence on the obtained results.

How Does Temporal Blurring Alter Movement Timing?

Subjective uncertainty arises because the estimation of the timing of an event into the future is error prone. This impact of stimulus-bound uncertainty on movement preparation has often been investigated using reaction time tasks where a warning stimulus (WS) predicts the occurrence of a "go" signal. The timing of the "go" signal can be chosen from a particular probability distribution with a given variance or uncertainty. It has been repeatedly shown that reaction times covary with the shape of the used "go" signal distribution. This is interpreted as evidence for temporal preparation. Moreover, the variance of the response time should always increase with the duration of the delay between the WS and the "go" signal. This increasing variance has been interpreted as a consequence of the temporal "blurring" of future events (scalar expectancy). The present paper tested the validity of the temporal "blurring" hypothesis in humans with a simple oculomotor reaction time task where subjective and stimulus-bound uncertainties were increased. Subjective uncertainty about the timing of a "go" signal was increased by lengthening the delay between the WS and the "go" signal. Objective uncertainty was altered by increasing the variance of "go" signal timing. Contrary to temporal blurring hypotheses, the study has shown that increasing the delay between events did not significantly increase movement timing variability. These results suggest that temporal blurring could not be a property of movement timing in an implicit timing context.

BIRAFFE2, a multimodal dataset for emotion-based personalization in rich affective game environments.

Generic emotion prediction models based on physiological data developed in the field of affective computing apparently are not robust enough. To improve their effectiveness, one needs to personalize them to specific individuals and incorporate broader contextual information. To address the lack of relevant datasets, we propose the 2nd Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems (BIRAFFE2) dataset. In addition to the classical procedure in the stimulus-appraisal paradigm, it also contains data from an affective gaming session in which a range of contextual data was collected from the game environment. This is complemented by accelerometer, ECG and EDA signals, participants' facial expression data, together with personality and game engagement questionnaires. The dataset was collected on 102 participants. Its potential usefulness is presented by validating the correctness of the contextual data and indicating the relationships between personality and participants' emotions and between personality and physiological signals.

The oculomotor signature of expected surprise.

Expected surprise, defined as the anticipation of uncertainty associated with the occurrence of a future event, plays a major role in gaze shifting and spatial attention. In the present study, we analyzed its impact on oculomotor behavior. We hypothesized that the occurrence of anticipatory saccades could decrease with increasing expected surprise and that its influence on visually-guided responses could be different given the presence of sensory information and perhaps competitive attentional effects. This hypothesis was tested in humans using a saccadic reaction time task in which a cue indicated the future stimulus position. In the 'no expected surprise' condition, the visual target could appear only at one previously cued location. In other conditions, more likely future positions were cued with increasing expected surprise. Anticipation was more frequent and pupil size was larger in the 'no expected surprise' condition compared with all other conditions, probably due to increased arousal. The latency of visually-guided saccades increased linearly with the logarithm of surprise (following Hick's law) but their maximum velocity repeated the arousal-related pattern. Therefore, expected surprise affects anticipatory and visually-guided responses differently. Moreover, these observations suggest a causal chain linking surprise, attention and saccades that could be disrupted in attentional or impulse control disorders.

Personality-Based Affective Adaptation Methods for Intelligent Systems.

In this article, we propose using personality assessment as a way to adapt affective intelligent systems. This psychologically-grounded mechanism will divide users into groups that differ in their reactions to affective stimuli for which the behaviour of the system can be adjusted. In order to verify the hypotheses, we conducted an experiment on 206 people, which consisted of two proof-of-concept demonstrations: a "classical" stimuli presentation part, and affective games that provide a rich and controllable environment for complex emotional stimuli. Several significant links between personality traits and the psychophysiological signals (electrocardiogram (ECG), galvanic skin response (GSR)), which were gathered while using the BITalino (r)evolution kit platform, as well as between personality traits and reactions to complex stimulus environment, are promising results that indicate the potential of the proposed adaptation mechanism.