Explore the vast range of titles available.

Selecting appropriate stimuli to induce emotional states is essential in affective research. Only a few standardized affective stimulus databases have been created for auditory, language, and visual materials. Numerous studies have extensively employed these databases using both behavioral and neuroimaging methods. However, some limitations of the existing databases have recently been reported, including limited numbers of stimuli in specific categories or poor picture quality of the visual stimuli. In the present article, we introduce the Nencki Affective Picture System (NAPS), which consists of 1,356 realistic, high-quality photographs that are divided into five categories (people, faces, animals, objects, and landscapes). Affective ratings were collected from 204 mostly European participants. The pictures were rated according to the valence, arousal, and approach–avoidance dimensions using computerized bipolar semantic slider scales. Normative ratings for the categories are presented for each dimension. Validation of the ratings was obtained by comparing them to ratings generated using the Self-Assessment Manikin and the International Affective Picture System. In addition, the physical properties of the photographs are reported, including luminance, contrast, and entropy. The new database, with accompanying ratings and image parameters, allows researchers to select a variety of visual stimulus materials specific to their experimental questions of interest. The NAPS system is freely accessible to the scientific community for noncommercial use by request at http://naps.nencki.gov.pl .

Visual perception is an important part of human life. In the context of facial recognition, it allows us to distinguish between emotions and important facial features that distinguish one person from another. However, subjects suffering from memory loss face significant facial processing problems. If the perception of facial features is affected by memory impairment, then it is possible to classify visual stimuli using brain activity data from the visual processing regions of the brain. This study differentiates the aspects of familiarity and emotion by the inversion effect of the face and uses convolutional neural network (CNN) models (EEGNet, EEGNet SSVEP (steady-state visual evoked potentials), and DeepConvNet) to learn discriminative features from raw electroencephalography (EEG) signals. Due to the limited number of available EEG data samples, Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) are introduced to generate synthetic EEG signals. The generated data are used to pretrain the models, and the learned weights are initialized to train them on the real EEG data. We investigate minor facial characteristics in brain signals and the ability of deep CNN models to learn them. The effect of face inversion was studied, and it was observed that the N170 component has a considerable and sustained delay. As a result, emotional and familiarity stimuli were divided into two categories based on the posture of the face. The categories of upright and inverted stimuli have the smallest incidences of confusion. The model’s ability to learn the face-inversion effect is demonstrated once more.

In this study, we proposed a new type of hybrid visual stimuli for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI), which incorporate various periodic motions into conventional flickering stimuli (FS) or pattern reversal stimuli (PRS). Furthermore, we investigated optimal periodic motions for each FS and PRS to enhance the performance of SSVEP-based BCI. Periodic motions were implemented by changing the size of the stimulus according to four different temporal functions denoted by none, square, triangular, and sine, yielding a total of eight hybrid visual stimuli. Additionally, we developed the extended version of filter bank canonical correlation analysis (FBCCA), which is a state-of-the-art training-free classification algorithm for SSVEP-based BCIs, to enhance the classification accuracy for PRS-based hybrid visual stimuli. Twenty healthy individuals participated in the SSVEP-based BCI experiment to discriminate four visual stimuli with different frequencies. An average classification accuracy and information transfer rate (ITR) were evaluated to compare the performances of SSVEP-based BCIs for different hybrid visual stimuli. Additionally, the user’s visual fatigue for each of the hybrid visual stimuli was also evaluated. As the result, for FS, the highest performances were reported when the periodic motion of the sine waveform was incorporated for all window sizes except for 3 s. For PRS, the periodic motion of the square waveform showed the highest classification accuracies for all tested window sizes. A significant statistical difference in the performance between the two best stimuli was not observed. The averaged fatigue scores were reported to be 5.3 ± 2.05 and 4.05 ± 1.28 for FS with sine-wave periodic motion and PRS with square-wave periodic motion, respectively. Consequently, our results demonstrated that FS with sine-wave periodic motion and PRS with square-wave periodic motion could effectively improve the BCI performances compared to conventional FS and PRS. In addition, thanks to its low visual fatigue, PRS with square-wave periodic motion can be regarded as the most appropriate visual stimulus for the long-term use of SSVEP-based BCIs, particularly for window sizes equal to or larger than 2 s.

Slow-paced breathing (SB) reduces anxiety, but its effects on frontal alpha asymmetry (also termed relative left frontal activity, rLFA) and the persistence of these effects after aversive stimuli remain unclear. This study investigated whether SB reduces state anxiety and enhances rLFA, and whether these effects persist immediately after exposure to aversive images from the International Affective Picture System (IAPS) following the breathing task. Seventeen healthy participants (7 females) completed sessions of SB (4 s inhalation, 6 s exhalation) and resting breathing (RB). Electroencephalography (EEG), heart rate variability (HRV), respiratory parameters, and State-Trait Anxiety Inventory-State (STAI-S) scores were measured at baseline (pre-task), post-task, and post-stimuli. HRV was evaluated by the root mean square of successive differences (RMSSD) and the low-frequency/high-frequency ratio (LF/HF ratio). Respiratory measurements included respiratory rate, coefficient of variation of respiratory intervals (CVRR), and end-tidal CO (ETCO ). rLFA, measured by alpha wave activity, was calculated at midfrontal (F4-F3) and lateral frontal (F8-F7) EEG sites. STAI-S scores in SB condition were significantly lower than in RB condition, both post-task (  < 0.001, Cohen's d = -1.46) and post-stimuli (  < 0.001, Cohen's d = -1.25). Midfrontal rLFA (F4-F3) also significantly increased with SB post-task (  < 0.01, Cohen's d = 1.03) and post-stimuli (  < 0.05, Cohen's d = 0.84), whereas lateral frontal rLFA (F8-F7) showed no significant changes. A significant interaction between intervention and time was observed for RMSSD (  < 0.01, η G = 0.18). Post-task RMSSD was significantly lower in SB condition compared to RB condition (  < 0.001), but this difference was absent post-stimuli. These findings suggest that SB effectively reduces state anxiety while enhancing rLFA, with these effects persisting after exposure to visual stressors. The anxiety-buffering effect of SB may be mediated by enhanced rLFA in the midfrontal region, reflecting improved prefrontal regulatory control over emotion. This indicates that SB could be a practical intervention to enhance neurophysiological resilience against acute stress.

Virtual reality environments offer great opportunities to study the performance of brain-computer interfaces (BCIs) in real-world contexts. As real-world stimuli are typically multimodal, their neuronal integration elicits complex response patterns. To investigate the effect of additional auditory cues on the processing of visual information, we used virtual reality to mimic safety-related events in an industrial environment while we concomitantly recorded electroencephalography (EEG) signals. We simulated a box traveling on a conveyor belt system where two types of stimuli – an exploding and a burning box – interrupt regular operation. The recordings from 16 subjects were divided into two subsets, a visual-only and an audio-visual experiment. In the visual-only experiment, the response patterns for both stimuli elicited a similar pattern – a visual evoked potential (VEP) followed by an event-related potential (ERP) over the occipital-parietal lobe. Moreover, we found the perceived severity of the event to be reflected in the signal amplitude. Interestingly, the additional auditory cues had a twofold effect on the previous findings: The P1 component was significantly suppressed in the case of the exploding box stimulus, whereas the N2c showed an enhancement for the burning box stimulus. This result highlights the impact of multisensory integration on the performance of realistic BCI applications. Indeed, we observed alterations in the offline classification accuracy for a detection task based on a mixed feature extraction (variance, power spectral density, and discrete wavelet transform) and a support vector machine classifier. In the case of the explosion, the accuracy slightly decreased by -1.64 % p. in an audio-visual experiment compared to the visual-only. Contrarily, the classification accuracy for the burning box increased by 5.58% p. when additional auditory cues were present. Hence, we conclude, that especially in challenging detection tasks, it is favorable to consider the potential of multisensory integration when BCIs are supposed to operate under (multimodal) real-world conditions.

Theory and modelling suggest that detection of neuronal activity may be feasible using phase sensitive MRI methods. Successful detection of neuronal activity both in vitro and in vivo has been described while others have reported negative results. Magnetic resonance electrical properties tomography may be a route by which signal changes can be identified. Here, we report successful and repeatable detection at 3 Tesla of human brain activation in response to visual and somatosensory stimuli using a functional version of tissue conductivity imaging (funCI). This detects activation in both white and grey matter with apparent tissue conductivity changes of 0.1 S/m (17–20%, depending on the tissue baseline conductivity measure) allowing visualization of complete system circuitry. The degree of activation scales with the degree of the stimulus (duration or contrast). The conductivity response functions show a distinct timecourse from that of traditional fMRI haemodynamic (BOLD or Blood Oxygenation Level Dependent) response functions, peaking within milliseconds of stimulus cessation and returning to baseline within 3–4 s. We demonstrate the utility of the funCI approach by showing robust activation of the lateral somatosensory circuitry on stimulation of an index finger, on stimulation of a big toe or of noxious (heat) stimulation of the face as well as activation of visual circuitry on visual stimulation in up to five different individuals. The sensitivity and repeatability of this approach provides further evidence that magnetic resonance imaging approaches can detect brain activation beyond changes in blood supply.

Although many studies have demonstrated the positive effects of natural visual and auditory stimuli on human physiological and psychological states, there is limited empirical evidence on the effects on subjective comfort under different thermal environments. This study used a climatic chamber experiment to evaluate the impact of three types of natural stimuli (visual, auditory, and combined audio-visual) on physiological and psychological responses under three operative temperature conditions (26 °C, 28 °C, and 32 °C). In total, 24 participants were recruited. Physiological indicators, including heart rate variability, skin conductance level (SCL), skin temperature (ST), and blood pressure, as well as psychological indicators including thermal sensation (TSV), thermal comfort (TCV), visual comfort (VCV), and acoustic comfort (ACV), were collected. The results show that TCV was significantly and positively correlated with both VCV and ACV. The visual stimuli produced the most significant decrease in TSV and the greatest increase in TCV, while combined audio-visual stimuli had the most significant impact on physiological responses. At 26 °C, the combined audio-visual stimuli group reduced heart rate by 6.08%. However, at 32 °C, most physiological and psychological restoration indicators showed no significant changes. These findings provide theoretical references for health-oriented multisensory environmental design in urban areas.

Divided attention is defined as focusing on different tasks at once, and this is described as one of the biggest problems of today’s society. Default examinations for understanding attention are questionnaires or physiological signals, like evoked potentials and electroencephalography. Physiological records were obtained using visual, auditory, and auditory-visual stimuli combinations with 48 participants—18-25-year-old university students—to find differences between sustained and divided attention. A Fourier-based filter was used to get a 0.01–30-Hz frequency band. Fractal dimensions, entropy values, power spectral densities, and Hjorth parameters from electroencephalography and P300 components from evoked potentials were calculated as features. To decrease the size of the feature set, some features, which yield less detail level for data, were eliminated. The visual and auditory stimuli in selective attention were compared with the divided attention state, and the best accuracy was found to be 88.89% on a support vector machine with linear kernel. As a result, it was seen that divided attention could be more difficult to determine from selective attention, but successful classification could be obtained with appropriate methods. Contrary to literature, the study deals with the infrastructure of attention types by working on a completely healthy and attention-high group.

Odor environments in living spaces can influence human physiological and psychological states. To elucidate the effect of volatile organic compounds (VOCs) of wood in living spaces on the modulations of cognitive processing, we built two experimental huts to simulate wooden construction living spaces. One hut was made of cedarwood ( Cryptomeria japonica ), and the other was made of resin. We used cedarwood and resin, because they are often used as construction materials in Japan. In both huts, we measured the participants’ first positivity in the early visual cortex (P1), the third positivity elicited by voluntary attention (P3b), and the automatic occipital negativity to infrequent visual stimuli in event-related potentials (ERPs), while they performed a visual discrimination task. The VOCs in both huts were measured by gas chromatography–mass spectrometry. The concentration of volatile sesquiterpenes was significantly increased in the cedarwood hut. Neither P1 nor P3b was affected by the cedarwood hut. Compared to the resin hut, we observed significantly larger occipital negativities to the infrequent stimuli in the cedarwood hut in the time windows of 280–300 ms at the mid-occipital region. These findings suggest that the increased concentration of volatile sesquiterpenes emitted from cedarwood facilitates the human brain’s response to changes in visual stimuli.

Modern computer hardware makes it possible to produce visual stimuli in ways not previously possible. Arbitrary scenes, from traditional sinusoidal gratings to naturalistic 3D scenes can now be specified on a frame-by-frame basis in realtime. A programming library called the Vision Egg that aims to make it easy to take advantage of these innovations. The Vision Egg is a free, open-source library making use of OpenGL and written in the high-level language Python with extensions in C. Careful attention has been paid to the issues of luminance and temporal calibration, and several interfacing techniques to input devices such as mice, movement tracking systems, and digital triggers are discussed. Together, these make the Vision Egg suitable for many psychophysical, electrophysiological, and behavioral experiments. This software is available for free download at visionegg.org.