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Interpersonal physiological synchrony (PS), or the similarity of physiological signals between individuals over time, may be used to detect attentionally engaging moments in time. We here investigated whether PS in the electroencephalogram (EEG), electrodermal activity (EDA), heart rate and a multimodal metric signals the occurrence of attentionally relevant events in time in two groups of participants. Both groups were presented with the same auditory stimulus, but were instructed to attend either to the narrative of an audiobook (audiobook-attending: AA group) or to interspersed emotional sounds and beeps (stimulus-attending: SA group). We hypothesized that emotional sounds could be detected in both groups as they are expected to draw attention involuntarily, in a bottom-up fashion. Indeed, we found this to be the case for PS in EDA or the multimodal metric. Beeps, that are expected to be only relevant due to specific "top-down" attentional instructions, could indeed only be detected using PS among SA participants, for EDA, EEG and the multimodal metric. We further hypothesized that moments in the audiobook accompanied by high PS in either EEG, EDA, heart rate or the multimodal metric for AA participants would be rated as more engaging by an independent group of participants compared to moments corresponding to low PS. This hypothesis was not supported. Our results show that PS can support the detection of attentionally engaging events over time. Currently, the relation between PS and engagement is only established for well-defined, interspersed stimuli, whereas the relation between PS and a more abstract self-reported metric of engagement over time has not been established. As the relation between PS and engagement is dependent on event type and physiological measure, we suggest to choose a measure matching with the stimulus of interest. When the stimulus type is unknown, a multimodal metric is most robust., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Stuldreher, Thammasan, van Erp and Brouwer.)

Previous research showed that a simple target interception task reveals differences between younger adults (YA) and older adults (OA) on a large screen under laboratory conditions. Participants intercept downward moving objects while a horizontally moving background creates an illusion of the object moving in the opposite direction of the background. OA are more influenced by this illusory motion than YA. OA seem to be less able to ignore irrelevant sensory information than YA. Since sensory integration relates to the ability to perform Activities of Daily Living (ADL), this interception task can potentially signal ADL issues. Here we investigated whether the results of the target interception task could be replicated using a more portable setup, i.e., a tablet instead of a large touch screen. For YA from the same, homogeneous population, the main effects were replicated although the task was more difficult in the tablet set-up. After establishing the tablet's validity, we analyzed the response patterns of OA that were less fit than the OA in previous research. We identified three different illusion patterns: a (large) illusion effect (indicating over integration), a reverse illusion effect, and no illusion effect. These different patterns are much more nuanced than previously reported for fit OA who only show over integration. We propose that the patterns are caused by differences in the samples of OA (OA in the current sample were older and had lower ADL scores), possibly modulated by increased task difficulty in the tablet setup. We discuss the effects of illusory background motion as a function of ADL scores using a transitional model. The first pattern commences when sensory integration capability starts to decrease, leading to a pattern of over-integration (illusion effect). The second pattern commences when compensatory mechanisms are not sufficient to counteract the effect of the background motion, leading to direction errors in the same direction as the background motion (reverse illusion). The third pattern commences when the task requirements are too high, leading OA to implement a probabilistic strategy by tapping toward the center of the screen.

Estimating cognitive or affective state from neurophysiological signals and designing applications that make use of this information requires expertise in many disciplines such as neurophysiology, machine learning, experimental psychology, and human factors. This makes it difficult to perform research that is strong in all its aspects as well as to judge a study or application on its merits. On the occasion of the special topic "Using neurophysiological signals that reflect cognitive or affective state" we here summarize often occurring pitfalls and recommendations on how to avoid them, both for authors (researchers) and readers. They relate to defining the state of interest, the neurophysiological processes that are expected to be involved in the state of interest, confounding factors, inadvertently "cheating" with classification analyses, insight on what underlies successful state estimation, and finally, the added value of neurophysiological measures in the context of an application. We hope that this paper will support the community in producing high quality studies and well-validated, useful applications.

While studies exist that compare different physiological variables with respect to their association with mental workload, it is still largely unclear which variables supply the best information about momentary workload of an individual and what is the benefit of combining them. We investigated workload using the n-back task, controlling for body movements and visual input. We recorded EEG, skin conductance, respiration, ECG, pupil size and eye blinks of 14 subjects. Various variables were extracted from these recordings and used as features in individually tuned classification models. Online classification was simulated by using the first part of the data as training set and the last part of the data for testing the models. The results indicate that EEG performs best, followed by eye related measures and peripheral physiology. Combining variables from different sensors did not significantly improve workload assessment over the best performing sensor alone. Best classification accuracy, a little over 90%, was reached for distinguishing between high and low workload on the basis of 2 min segments of EEG and eye related variables. A similar and not significantly different performance of 86% was reached using only EEG from single electrode location Pz.

We here introduce a new experimental paradigm to induce mental stress in a quick and easy way while adhering to ethical standards and controlling for potential confounds resulting from sensory input and body movements. In our Sing-a-Song Stress Test, participants are presented with neutral messages on a screen, interleaved with 1-min time intervals. The final message is that the participant should sing a song aloud after the interval has elapsed. Participants sit still during the whole procedure. We found that heart rate and skin conductance during the 1-min intervals following the sing-a-song stress message are substantially higher than during intervals following neutral messages. The order of magnitude of the rise is comparable to that achieved by the Trier Social Stress Test. Skin conductance increase correlates positively with experienced stress level as reported by participants. We also simulated stress detection in real time. When using both skin conductance and heart rate, stress is detected for 18 out of 20 participants, approximately 10 s after onset of the sing-a-song message. In conclusion, the Sing-a-Song Stress Test provides a quick, easy, controlled and potent way to induce mental stress and could be helpful in studies ranging from examining physiological effects of mental stress to evaluating interventions to reduce stress.

In this study, we investigated a Brain-Computer Interface (BCI) based on EEG responses to vibro-tactile stimuli around the waist. P300 BCIs based on tactile stimuli have the advantage of not taxing the visual or auditory system and of being potentially unnoticeable to other people. A tactile BCI could be especially suitable for patients whose vision or eye movements are impaired. In Experiment 1, we investigated its feasibility and the effect of the number of equally spaced tactors. Whereas a large number of tactors is expected to enhance the P300 amplitude since the target will be less frequent, it could also negatively affect the P300 since it will be difficult to identify the target when tactor density increases. Participants were asked to attend to the vibrations of a target tactor, embedded within a stream of distracters. The number of tactors was two, four or six. We demonstrated the feasibility of a tactile P300 BCI. We did not find a difference in SWLDA classification performance between the different numbers of tactors. In a second set of experiments we reduced the stimulus onset asynchrony (SOA) by shortening the on- and/or off-time of the tactors. The SOA for an optimum performance as measured in our experiments turned out to be close to conventional SOAs of visual P300 BCIs.