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Understanding and detecting human emotions is crucial for enhancing mental health, cognitive performance and human–computer interactions. This field in affective computing is relatively unexplored, and gaining knowledge about which external factors impact emotions could enhance communication between users and machines. Furthermore, it could also help us to manage affective disorders or understand affective physiological responses to human spatial and digital environments. The main objective of the current study was to investigate the influence of external stimulation, specifically the influence of different light conditions, on brain activity while observing affect-eliciting pictures and their classification. In this context, a multichannel electroencephalography (EEG) was recorded in 30 participants as they observed images from the Nencki Affective Picture System (NAPS) database in an art-gallery-style Virtual Reality (VR) environment. The elicited affect states were classified into three affect classes within the two-dimensional valence–arousal plane. Valence (positive/negative) and arousal (high/low) values were reported by participants on continuous scales. The experiment was conducted in two experimental conditions: a warm light condition and a cold light condition. Thus, three classification tasks arose with regard to the recorded brain data: classification of an affect state within a warm-light condition, classification of an affect state within a cold light condition, and warm light vs. cold light classification during observation of affect-eliciting images. For all classification tasks, Linear Discriminant Analysis, a Spatial Filter Model, a Convolutional Neural Network, the EEGNet, and the SincNet were compared. The EEGNet architecture performed best in all tasks. It could significantly classify three affect states with 43.12% accuracy under the influence of warm light. Under the influence of cold light, no model could achieve significant results. The classification between visual stimulus with warm light vs. cold light could be classified significantly with 76.65% accuracy from the EEGNet, well above any other machine learning or deep learning model. No significant differences could be detected between affect recognition in different light conditions, but the results point towards the advantage of gradient-based learning methods for data-driven experimental designs for the problem of affect decoding from EEG, providing modern tools for affective computing in digital spaces. Moreover, the ability to discern externally driven affective states through deep learning not only advances our understanding of the human mind but also opens avenues for developing innovative therapeutic interventions and improving human–computer interaction.

Abstract In the recent past, many organizations and people have substituted face-to-face meetings with videoconferences. Among others, tools like Zoom, Teams, and Webex have become the “new normal” of human social interaction in many domains (e.g., business, education). However, this radical adoption and extensive use of videoconferencing tools also has a dark side, referred to as videoconference fatigue (VCF). To date only self-report evidence has shown that VCF is a serious issue. However, based on self-reports alone it is hardly possible to provide a comprehensive understanding of a cognitive phenomenon like VCF. Against this background, we examined VCF also from a neurophysiological perspective. Specifically, we collected and analyzed electroencephalography (continuous and event-related) and electrocardiography (heart rate and heart rate variability) data to investigate whether VCF can also be proven on a neurophysiological level. We conducted a laboratory experiment based on a within-subjects design (N = 35). The study context was a university lecture, which was given in a face-to-face and videoconferencing format. In essence, the neurophysiological data—together with questionnaire data that we also collected—show that 50 min videoconferencing, if compared to a face-to-face condition, results in changes in the human nervous system which, based on existing literature, can undoubtedly be interpreted as fatigue. Thus, individuals and organizations must not ignore the fatigue potential of videoconferencing. A major implication of our study is that videoconferencing should be considered as a possible complement to face-to-face interaction, but not as a substitute.

For their ease of accessibility and low cost, current Brain-Computer Interfaces (BCI) used to detect subjective emotional and affective states rely largely on electroencephalographic (EEG) signals. Public datasets are available for researchers to design models for affect detection from EEG. However, few designs focus on optimally exploiting the nature of the stimulus elicitation to improve accuracy. The RSVP protocol is used in this experiment to present human faces of emotion to 28 participants while EEG was measured. We found that artificially enhanced human faces with exaggerated, cartoonish visual features significantly improve some commonly used neural correlates of emotion as measured by event-related potentials (ERPs). These images elicit an enhanced N170 component, well known to relate to the facial visual encoding process. Our findings suggest that the study of emotion elicitation could exploit consistent, high detail, AI generated stimuli transformations to study the characteristics of electrical brain activity related to visual affective stimuli. Furthermore, this specific result might be useful in the context of affective BCI design, where a higher accuracy in affect decoding from EEG can improve the experience of a user.

In the past years motor imagery (MI) turned out to be also an innovative and effective tool for motor learning and improvement of sports performance. Whereas many studies investigating sports MI focusing on upper or lower limbs involvement, knowledge about involved neural structures during whole-body movements is still limited. In the present study we investigated brain activity of climbers during a kinesthetic motor imagery (KMI) climbing task with different difficulties by means of functional near infrared spectroscopy (fNIRS). Twenty healthy participants were split into two groups according to their climbing skill level. The aim of the current study is investigating neural correlates of a whole-body sports MI task with an additional focus on skill level dependency. Climbing experts and non-experts imagined bouldering an “easy” and “difficult” route from a first-person perspective while hemodynamic responses were recorded simultaneously. We found significant differences between the two climbing routes, easy and difficult within participants as well as between the two groups of different climbing skill levels. Overall beginners showed increased hemodynamic responses compared to experts in all defined regions of interest (ROI) supporting the claim of the neural efficiency hypothesis (NEH). Even though climbing is a complex, coordinated movement of upper and lower limbs we found a stronger activation focus of the upper limbs, especially of the dominant hand-area, while the foot area seems to be deactivated or inhibited simultaneously. Summarizing, these findings provide novel insights into brain activation during the imagery of a whole-body movement and its relation to climbing expertise.

Introduction: Advantageous effects of biological motion (BM) detection, a low-perceptual mechanism that allows the rapid recognition and understanding of spatiotemporal characteristics of movement via salient kinematics information, can be amplified when combined with motor imagery (MI), i.e., the mental simulation of motor acts. According to Jeannerod’s neurostimulation theory, asynchronous firing and reduction of mu and beta rhythm oscillations, referred to as suppression over the sensorimotor area, are sensitive to both MI and action observation (AO) of BM. Yet, not many studies investigated the use of BM stimuli using combined AO-MI tasks. In this study, we assessed the neural response in the form of event-related synchronization and desynchronization (ERD/S) patterns following the observation of point-light-walkers and concordant MI, as compared to MI alone.Methods: Twenty right-handed healthy participants accomplished the experimental task by observing BM stimuli and subsequently performing the same movement using kinesthetic MI (walking, cycling, and jumping conditions). We recorded an electroencephalogram (EEG) with 32 channels and performed time-frequency analysis on alpha (8–13 Hz) and beta (18–24 Hz) frequency bands during the MI task. A two-way repeated-measures ANOVA was performed to test statistical significance among conditions and electrodes of interest.Results: The results revealed significant ERD/S patterns in the alpha frequency band between conditions and electrode positions. Post hoc comparisons showed significant differences between condition 1 (walking) and condition 3 (jumping) over the left primary motor cortex. For the beta band, a significantly less difference in ERD patterns (p < 0.01) was detected only between condition 3 (jumping) and condition 4 (reference).Discussion: Our results confirmed that the observation of BM combined with MI elicits a neural suppression, although just in the case of jumping. This is in line with previous findings of AO and MI (AOMI) eliciting a neural suppression for simulated whole-body movements. In the last years, increasing evidence started to support the integration of AOMI training as an adjuvant neurorehabilitation tool in Parkinson’s disease (PD).Conclusion: We concluded that using BM stimuli in AOMI training could be promising, as it promotes attention to kinematic features and imitative motor learning.

The goal of this study was to implement a Riemannian geometry (RG)-based algorithm to detect high mental workload (MWL) and mental fatigue (MF) using task-induced electroencephalogram (EEG) signals. In order to elicit high MWL and MF, the participants performed a cognitively demanding task in the form of the letter n-back task. We analyzed the time-varying characteristics of the EEG band power (BP) features in the theta and alpha frequency band at different task conditions and cortical areas by employing a RG-based framework. MWL and MF were considered as too high, when the Riemannian distances of the task-run EEG reached or surpassed the threshold of the baseline EEG. The results of this study showed a BP increase in the theta and alpha frequency bands with increasing experiment duration, indicating elevated MWL and MF that impedes/hinders the task performance of the participants. High MWL and MF was detected in 8 out of 20 participants. The Riemannian distances also showed a steady increase toward the threshold with increasing experiment duration, with the most detections occurring toward the end of the experiment. To support our findings, subjective ratings (questionnaires concerning fatigue and workload levels) and behavioral measures (performance accuracies and response times) were also considered.

The aim of this work was to re-evaluate electrophysiological data from a previous study on motor imagery (MI) with a special focus on observed inter- and intra-individual differences. More concretely, we investigated event-related desynchronization/synchronization patterns during sports MI (playing tennis) compared with simple MI (squeezing a ball) and discovered high variability across participants. Thirty healthy volunteers were divided in two groups; the experimental group (EG) performed a physical exercise between two imagery sessions, and the control group (CG) watched a landscape movie without physical activity. We computed inter-individual differences by assessing the dissimilarities among subjects for each group, condition, time period, and frequency band. In the alpha band, we observe some clustering in the ranking of the subjects, therefore showing smaller distances than others. Moreover, in our statistical evaluation, we observed a consistency in ranking across time periods both for the EG and for the CG. For the latter, we also observed similar rankings across conditions. On the contrary, in the beta band, the ranking of the subjects was more similar for the EG across conditions and time periods than for the subjects of the CG. With this study, we would like to draw attention to variability measures instead of primarily focusing on the identification of common patterns across participants, which often do not reflect the whole neurophysiological reality.

Functional near-infrared spectroscopy (fNIRS) measures the functional activity of the cerebral cortex. The concentration changes of oxygenated (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) can be detected and associated with activation of the cortex in the investigated area (neurovascular coupling). Recorded signals of hemodynamic responses may contain influences from physiological signals (systemic influences, physiological artifacts) which do not originate from the cerebral cortex activity. The physiological artifacts contain the blood pressure (BP), respiratory patterns, and the pulsation of the heart. In order to perform a comprehensive analysis of recorded fNIRS data, a proper correction of these physiological artifacts is necessary. This article introduces NICA – a novel toolbox for near-infrared spectroscopy calculations and analyses based on MATLAB. With NICA it is possible to process and visualize fNIRS data, including different signal processing methods for physiological artifact correction. The artifact correction methods used in this toolbox are common average reference (CAR), independent component analysis (ICA), and transfer function (TF) models. A practical example provides results from a study, where NICA was used for analyzing the measurement data, in order to demonstrate the signal processing steps and the physiological artifact correction. The toolbox was developed for fNIRS data recorded with the NIRScout 1624 measurement device and the corresponding recording software NIRStar.

Motor imagery is often used inducing changes in electroencephalographic (EEG) signals for imagery-based brain-computer interfacing (BCI). A BCI is a device translating brain signals into control signals providing severely motor-impaired persons with an additional, non-muscular channel for communication and control. In the last years, there is increasing interest using BCIs also for healthy people in terms of enhancement or gaming. Most studies focusing on improving signal processing feature extraction and classification methods, but the performance of a BCI can also be improved by optimizing the user’s control strategies, e.g., using more vivid and engaging mental tasks for control. We used multichannel EEG to investigate neural correlates of a sports imagery task (playing tennis) compared to a simple motor imagery task (squeezing a ball). To enhance the vividness of both tasks participants performed a short physical exercise between two imagery sessions. EEG was recorded from 60 closely spaced electrodes placed over frontal, central, and parietal areas of 30 healthy volunteers divided in two groups. Whereas Group 1 (EG) performed a physical exercise between the two imagery sessions, Group 2 (CG) watched a landscape movie without physical activity. Spatiotemporal event-related desynchronization (ERD) and event-related synchronization (ERS) patterns during motor imagery (MI) tasks were evaluated. The results of the EG showed significant stronger ERD patterns in the alpha frequency band (8–13 Hz) during MI of tennis after training. Our results are in evidence with previous findings that MI in combination with motor execution has beneficial effects. We conclude that sports MI combined with an interactive game environment could be a future promising task in motor learning and rehabilitation improving motor functions in late therapy processes or support neuroplasticity.

The novel BackHome system offers individuals with disabilities a range of useful services available via brain-computer interfaces (BCIs), to help restore their independence. This is the time such technology is ready to be deployed in the real world, that is, at the target end users’ home. This has been achieved by the development of practical electrodes, easy to use software, and delivering telemonitoring and home support capabilities which have been conceived, implemented, and tested within a user-centred design approach. The final BackHome system is the result of a 3-year long process involving extensive user engagement to maximize effectiveness, reliability, robustness, and ease of use of a home based BCI system. The system is comprised of ergonomic and hassle-free BCI equipment; one-click software services for Smart Home control, cognitive stimulation, and web browsing; and remote telemonitoring and home support tools to enable independent home use for nonexpert caregivers and users. BackHome aims to successfully bring BCIs to the home of people with limited mobility to restore their independence and ultimately improve their quality of life.

The P300 event-related potential is a well-known pattern in the electroencephalogram (EEG). This kind of brain signal is used for many different brain-computer interface (BCI) applications, e.g., spellers, environmental controllers, web browsers, or for painting. In recent times, BCI systems are mature enough to leave the laboratories to be used by the end-users, namely severely disabled people. Therefore, new challenges arise and the systems should be implemented and evaluated according to user-centered design (USD) guidelines. We developed and implemented a new system that utilizes the P300 pattern to compose music. Our Brain Composing system consists of three parts: the EEG acquisition device, the P300-based BCI, and the music composing software. Seventeen musical participants and one professional composer performed a copy-spelling, a copy-composing, and a free-composing task with the system. According to the USD guidelines, we investigated the efficiency, the effectiveness and subjective criteria in terms of satisfaction, enjoyment, frustration, and attractiveness. The musical participants group achieved high average accuracies: 88.24% (copy-spelling), 88.58% (copy-composing), and 76.51% (free-composing). The professional composer achieved also high accuracies: 100% (copy-spelling), 93.62% (copy-composing), and 98.20% (free-composing). General results regarding the subjective criteria evaluation were that the participants enjoyed the usage of the Brain Composing system and were highly satisfied with the system. Showing very positive results with healthy people in this study, this was the first step towards a music composing system for severely disabled people.