[Subjective certainty to increase the acceptance of automated and connected driving]. / Subjektive Sicherheit zur Steigerung der Akzeptanz des automatisierten und vernetzten Fahrens.

An important factor for the acceptance and thus the spread of automated and connected driving (ACD) is the degree of subjective uncertainty that users experience when interacting with automated vehicles. Subjective uncertainties always occur when people are not able to predict the further course of a situation or future events due to lack of experience or information. If such uncertainties occur during the use of automated vehicles, the development of trust and thus acceptance of this technology is impaired by the negative emotions accompanying subjective uncertainties. Within the AutoAkzept project (which full title translates to: Automation without uncertainty to increase the acceptance of automated and connected driving), solutions for user-focused automation have been developed that put vehicle occupants at the center of system development. User-focused systems take into account two basic human needs in human-machine interaction, the need to understand and the need to be understood. For this purpose, user-focused systems use different sensors to detect subjective uncertainties and their influencing factors in real time, integrate this information with context data and make adjustments that reduce subjective uncertainties. The systemic adaptations of user-focused systems follow a holistic approach that includes the levels of vehicle guidance, interior adaptation and information presentation as well as target guidance are included. By reducing or avoiding subjective uncertainties, the project developments contribute to a positive, comfortable user experience and help to increase the acceptance of ACD. This paper presents research results of AutoAkzept on the topics of user state and activity modelling as well as needs-based adaptation strategies, which represent key components for the implementation of user-focused automation.

Alexithymia and automatic processing of facial emotions: behavioral and neural findings.

BACKGROUND: Alexithymia is a personality trait characterized by difficulties identifying and describing feelings, an externally oriented style of thinking, and a reduced inclination to imagination. Previous research has shown deficits in the recognition of emotional facial expressions in alexithymia and reductions of brain responsivity to emotional stimuli. Using an affective priming paradigm, we investigated automatic perception of facial emotions as a function of alexithymia at the behavioral and neural level. In addition to self-report scales, we applied an interview to assess alexithymic tendencies. RESULTS: During 3 T fMRI scanning, 49 healthy individuals judged valence of neutral faces preceded by briefly shown happy, angry, fearful, and neutral facial expressions. Alexithymia was assessed using the 20-Item Toronto Alexithymia Scale (TAS-20), the Bermond-Vorst Alexithymia Questionnaire (BVAQ) and the Toronto Structured Interview for Alexithymia (TSIA). As expected, only negative correlations were found between alexithymic features and affective priming. The global level of self-reported alexithymia (as assessed by the TAS-20 and the BVAQ) was found to be related to less affective priming owing to angry faces. At the facet level, difficulties identifying feelings, difficulties analyzing feelings, and impoverished fantasy (as measured by the BVAQ) were correlated with reduced affective priming due to angry faces. Difficulties identifying feelings (BVAQ) correlated also with reduced affective priming due to fearful faces and reduced imagination (TSIA) was related to decreased affective priming due to happy faces. There was only one significant correlation between alexithymia dimensions and automatic brain response to masked facial emotions: TAS-20 alexithymia correlated with heightened brain response to masked happy faces in superior and medial frontal areas. CONCLUSIONS: Our behavioral results provide evidence that alexithymic features are related in particular to less sensitivity for covert facial expressions of anger. The perceptual alterations could reflect impaired automatic recognition or integration of social anger signals into judgemental processes and might contribute to the problems in interpersonal relationships associated with alexithymia. Our findings suggest that self-report measures of alexithymia may have an advantage over interview-based tests as research tools in the field of emotion perception at least in samples of healthy individuals characterized by rather low levels of alexithymia.

Observer-Rated Alexithymia and its Relationship with the Five-Factor-Model of Personality.

Studies examining the relationship between alexithymia and personality exclusively employed self-report measures of alexithymia. In the present study, we examined the relationship of both observer-rated and self-reported alexithymia with the Big Five personality dimensions. We administered the Toronto Structured Interview for Alexithymia (TSIA) as an interview-based measure of alexithymia and, in addition, two self-report questionnaires, the 20-item Toronto Alexithymia Scale (TAS-20) and the Bermond-Vorst Alexithymia Questionnaire (BVAQ). Fifty-one university students were interviewed and completed the alexithymia scales and the NEO Five-Factor Inventory. In contrast to TAS-20 and BVAQ, the Difficulty identifying feelings (DIF) scale of the TSIA was found to be unrelated to neuroticism, suggesting that the frequently reported association between DIF and neuroticism could be due to the use of self-report scales. In contrast, the affective dimension of alexithymia, measured by the BVAQ, was even negatively related with neuroticism. Thus, a paucity of fantasy and little emotional arousal goes together with increased emotional stability. Furthermore, we revealed negative correlations between interview-based alexithymia scores and openness to experience and agreeableness, which cross-validated the self-report findings. Finally, extraversion and conscientiousness each showed only one negative correlation, namely with subscales of the BVAQ. Taken together, our findings show that on the basis of interviews there is no evidence for a relation of DIF with neuroticism, while associations of alexithymia with low openness to experience and low agreeableness emerged irrespective of assessment approach. The relations of alexithymia with personality are discussed in the light of different measurement approaches.

Implicit affectivity and rapid processing of affective body language: An fMRI study.

Previous research has revealed affect-congruity effects for the recognition of affects from faces. Little is known about the impact of affect on the perception of body language. The aim of the present study was to investigate the relationship of implicit (versus explicit) affectivity with the recognition of briefly presented affective body expressions. Implicit affectivity, which can be measured using indirect assessment methods, has been found to be more predictive of spontaneous physiological reactions than explicit (self-reported) affect. Thirty-four healthy women had to label the expression of body postures (angry, fearful, happy, or neutral) presented for 66 ms and masked by a neutral body posture in a forced-choice format while undergoing functional magnetic resonance imaging (fMRI). Participants' implicit affectivity was assessed using the Implicit Positive and Negative Affect Test. Measures of explicit state and trait affectivity were also administered. Analysis of the fMRI data was focused on a subcortical network involved in the rapid perception of affective body expressions. Only implicit negative affect (but not explicit affect) was correlated with correct labeling performance for angry body posture. As expected, implicit negative affect was positively associated with activation of the subcortical network in response to fearful and angry expression (compared to neutral expression). Responses of the caudate nucleus to affective body expression were especially associated with its recognition. It appears that processes of rapid recognition of affects from body postures could be facilitated by an individual's implicit negative affect.

Alexithymia and the labeling of facial emotions: response slowing and increased motor and somatosensory processing.

BACKGROUND: Alexithymia is a personality trait that is characterized by difficulties in identifying and describing feelings. Previous studies have shown that alexithymia is related to problems in recognizing others' emotional facial expressions when these are presented with temporal constraints. These problems can be less severe when the expressions are visible for a relatively long time. Because the neural correlates of these recognition deficits are still relatively unexplored, we investigated the labeling of facial emotions and brain responses to facial emotions as a function of alexithymia. RESULTS: Forty-eight healthy participants had to label the emotional expression (angry, fearful, happy, or neutral) of faces presented for 1 or 3 seconds in a forced-choice format while undergoing functional magnetic resonance imaging. The participants' level of alexithymia was assessed using self-report and interview. In light of the previous findings, we focused our analysis on the alexithymia component of difficulties in describing feelings. Difficulties describing feelings, as assessed by the interview, were associated with increased reaction times for negative (i.e., angry and fearful) faces, but not with labeling accuracy. Moreover, individuals with higher alexithymia showed increased brain activation in the somatosensory cortex and supplementary motor area (SMA) in response to angry and fearful faces. These cortical areas are known to be involved in the simulation of the bodily (motor and somatosensory) components of facial emotions. CONCLUSION: The present data indicate that alexithymic individuals may use information related to bodily actions rather than affective states to understand the facial expressions of other persons.

Assessing alexithymia and emotional awareness: relations between measures in a German non-clinical sample.

The aim of this study was to evaluate psychometric properties and relations between two different methods of measuring alexithymia and one measure of emotional awareness in a German non-clinical sample. The 20-Item Toronto Alexithymia Scale (TAS-20), the Toronto Structured Interview for Alexithymia (TSIA), and the Levels of Emotional Awareness Scale (LEAS), which is a performance-based measure of emotional awareness, were administered to 84 university students. Both internal reliability and inter-rater reliability for the TSIA were acceptable. Results from exploratory factor analysis (EFA) based on all measures supported a three factorial solution previously obtained in an American sample using multiple methods of alexithymia and emotional ability measurement. In our three factor model direct self (TAS-20), direct other (TSIA), and indirect self (LEAS) measures were differentiated. The convergent validity of the TSIA was supported by a significant correlation with the LEAS. Our findings suggest that future research on alexithymia and emotional awareness can benefit from the use of a multi-method approach and should include objective measures.

The Novelty Appraisal of the Feeling of Risk in Vehicles.

Nowadays, vehicle assistance systems may assess the risks of the traffic situation with the help of advanced sensor technology and optimized algorithms. However, the passengers' feelings of risk in the vehicle have been mostly neglected. According to the Component Process Model of emotions, during the feeling of risk, novelty is one of the relevant event appraisals leading to particular physiological and facial responses. In order to identify whether or not indicators for novelty appraisal may be used for detecting the feeling of risk of vehicle occupants, we investigated physiological responses and facial expressions of individuals experiencing the feeling of risk with different levels of novelty. This secondary analysis of an earlier simulator study revealed that pupil diameter amplitude, skin conductance level changes, and changes in and amplitude of activity in facial expressions (the inner and outer brow raiser, brow lowerer, upper lid raiser and lid tightener) were correlated with the reduction in the novelty, suggesting that they could indicate the novelty of the feeling of risk of vehicle occupants. Hence, this research provides evidence for the novelty appraisal of the feeling of risk. Furthermore, it informs research on affect-aware systems to identify and reduce the feeling of risk of vehicle occupants in order to help to keep trust in automated vehicles high.

Understanding the Multidimensional and Dynamic Nature of Facial Expressions Based on Indicators for Appraisal Components as Basis for Measuring Drivers' Fear.

Facial expressions are one of the commonly used implicit measurements for the in-vehicle affective computing. However, the time courses and the underlying mechanism of facial expressions so far have been barely focused on. According to the Component Process Model of emotions, facial expressions are the result of an individual's appraisals, which are supposed to happen in sequence. Therefore, a multidimensional and dynamic analysis of drivers' fear by using facial expression data could profit from a consideration of these appraisals. A driving simulator experiment with 37 participants was conducted, in which fear and relaxation were induced. It was found that the facial expression indicators of high novelty and low power appraisals were significantly activated after a fear event (high novelty: Z = 2.80, p < 0.01, r contrast = 0.46; low power: Z = 2.43, p < 0.05, r contrast = 0.50). Furthermore, after the fear event, the activation of high novelty occurred earlier than low power. These results suggest that multidimensional analysis of facial expression is suitable as an approach for the in-vehicle measurement of the drivers' emotions. Furthermore, a dynamic analysis of drivers' facial expressions considering of effects of appraisal components can add valuable information for the in-vehicle assessment of emotions.

Corrigendum: Assessing the Driver's Current Level of Working Memory Load With High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study.

[This corrects the article DOI: 10.3389/fnhum.2017.00167.].

Demonstrating Brain-Level Interactions Between Visuospatial Attentional Demands and Working Memory Load While Driving Using Functional Near-Infrared Spectroscopy.

Driving is a complex task concurrently drawing on multiple cognitive resources. Yet, there is a lack of studies investigating interactions at the brain-level among different driving subtasks in dual-tasking. This study investigates how visuospatial attentional demands related to increased driving difficulty interacts with different working memory load (WML) levels at the brain level. Using multichannel whole-head high density functional near-infrared spectroscopy (fNIRS) brain activation measurements, we aimed to predict driving difficulty level, both separate for each WML level and with a combined model. Participants drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. In half of the time, the course led through a construction site with reduced lane width, increasing visuospatial attentional demands. Concurrently, participants performed a modified version of the n-back task with five different WML levels (from 0-back up to 4-back), forcing them to continuously update, memorize, and recall the sequence of the previous 'n' speed signs and adjust their speed accordingly. Using multivariate logistic ridge regression, we were able to correctly predict driving difficulty in 75.0% of the signal samples (1.955 Hz sampling rate) across 15 participants in an out-of-sample cross-validation of classifiers trained on fNIRS data separately for each WML level. There was a significant effect of the WML level on the driving difficulty prediction accuracies [range 62.2-87.1%; χ2(4) = 19.9, p < 0.001, Kruskal-Wallis H test] with highest prediction rates at intermediate WML levels. On the contrary, training one classifier on fNIRS data across all WML levels severely degraded prediction performance (mean accuracy of 46.8%). Activation changes in the bilateral dorsal frontal (putative BA46), bilateral inferior parietal (putative BA39), and left superior parietal (putative BA7) areas were most predictive to increased driving difficulty. These discriminative patterns diminished at higher WML levels indicating that visuospatial attentional demands and WML involve interacting underlying brain processes. The changing pattern of driving difficulty related brain areas across WML levels could indicate potential changes in the multitasking strategy with level of WML demand, in line with the multiple resource theory.

Volumetric Associations Between Amygdala, Nucleus Accumbens, and Socially Anxious Tendencies in Healthy Women.

Socially anxious individuals report higher social fears and feelings of distress in interpersonal interactions. Structural neuroimaging studies indicate brain morphological abnormalities in patients with social anxiety disorder (SAD), but findings are heterogeneous and partially discrepant. Studies on structural correlates of socially anxious tendencies in participants without clinical diagnoses are scarce. Using structural magnetic resonance imaging, the present study examined the relationship between social interaction anxiety and gray matter (GM) volume in 38 healthy women. The amygdala and nucleus accumbens (NAcc) were defined as a priori regions of interest. Moreover, exploratory whole-brain analyses were conducted. Higher levels of social anxiety significantly predicted increased GM volume in the right amygdala [k = 262 voxels, voxel-level threshold at p < .05 (uncorrected), with a cluster-corrected significance level of p = 0.05 calculated by Monte Carlo Simulations] and bilateral NAcc [left: k = 52 voxels, right: k = 49 voxels; at p < .05 (corrected for search volume)]. These relationships remained significant when controlling for a potential influence of trait anxiety. Additionally, socially anxious tendencies were associated with an enlarged striatum [i.e., putamen and caudate; left: k = 567 voxels, right: k = 539 voxels; at p < .001 (uncorrected)]. Our findings indicate that higher social interaction anxiety in healthy individuals is related to amygdalar and striatal volumetric increases. These brain regions are known to be involved in social perception, anxiety, and the avoidance of harm. Future studies may clarify whether the observed morphological alterations constitute a structural vulnerability factor for SAD.

Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy.

Experiencing frustration while driving can harm cognitive processing, result in aggressive behavior and hence negatively influence driving performance and traffic safety. Being able to automatically detect frustration would allow adaptive driver assistance and automation systems to adequately react to a driver's frustration and mitigate potential negative consequences. To identify reliable and valid indicators of driver's frustration, we conducted two driving simulator experiments. In the first experiment, we aimed to reveal facial expressions that indicate frustration in continuous video recordings of the driver's face taken while driving highly realistic simulator scenarios in which frustrated or non-frustrated emotional states were experienced. An automated analysis of facial expressions combined with multivariate logistic regression classification revealed that frustrated time intervals can be discriminated from non-frustrated ones with accuracy of 62.0% (mean over 30 participants). A further analysis of the facial expressions revealed that frustrated drivers tend to activate muscles in the mouth region (chin raiser, lip pucker, lip pressor). In the second experiment, we measured cortical activation with almost whole-head functional near-infrared spectroscopy (fNIRS) while participants experienced frustrating and non-frustrating driving simulator scenarios. Multivariate logistic regression applied to the fNIRS measurements allowed us to discriminate between frustrated and non-frustrated driving intervals with higher accuracy of 78.1% (mean over 12 participants). Frustrated driving intervals were indicated by increased activation in the inferior frontal, putative premotor and occipito-temporal cortices. Our results show that facial and cortical markers of frustration can be informative for time resolved driver state identification in complex realistic driving situations. The markers derived here can potentially be used as an input for future adaptive driver assistance and automation systems that detect driver frustration and adaptively react to mitigate it.

Assessing the Driver's Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study.

Cognitive overload or underload results in a decrease in human performance which may result in fatal incidents while driving. We envision that driver assistive systems which adapt their functionality to the driver's cognitive state could be a promising approach to reduce road accidents due to human errors. This research attempts to predict variations of cognitive working memory load levels in a natural driving scenario with multiple parallel tasks and to reveal predictive brain areas. We used a modified version of the n-back task to induce five different working memory load levels (from 0-back up to 4-back) forcing the participants to continuously update, memorize, and recall the previous 'n' speed sequences and adjust their speed accordingly while they drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. We measured brain activation using multichannel whole head, high density functional near-infrared spectroscopy (fNIRS) and predicted working memory load level from the fNIRS data by combining multivariate lasso regression and cross-validation. This allowed us to predict variations in working memory load in a continuous time-resolved manner with mean Pearson correlations between induced and predicted working memory load over 15 participants of 0.61 [standard error (SE) 0.04] and a maximum of 0.8. Restricting the analysis to prefrontal sensors placed over the forehead reduced the mean correlation to 0.38 (SE 0.04), indicating additional information gained through whole head coverage. Moreover, working memory load predictions derived from peripheral heart rate parameters achieved much lower correlations (mean 0.21, SE 0.1). Importantly, whole head fNIRS sampling revealed increasing brain activation in bilateral inferior frontal and bilateral temporo-occipital brain areas with increasing working memory load levels suggesting that these areas are specifically involved in workload-related processing.

Automatic emotion processing as a function of trait emotional awareness: an fMRI study.

It is unclear whether reflective awareness of emotions is related to extent and intensity of implicit affective reactions. This study is the first to investigate automatic brain reactivity to emotional stimuli as a function of trait emotional awareness. To assess emotional awareness the Levels of Emotional Awareness Scale (LEAS) was administered. During scanning, masked happy, angry, fearful and neutral facial expressions were presented to 46 healthy subjects, who had to rate the fit between artificial and emotional words. The rating procedure allowed assessment of shifts in implicit affectivity due to emotion faces. Trait emotional awareness was associated with increased activation in the primary somatosensory cortex, inferior parietal lobule, anterior cingulate gyrus, middle frontal and cerebellar areas, thalamus, putamen and amygdala in response to masked happy faces. LEAS correlated positively with shifts in implicit affect caused by masked happy faces. According to our findings, people with high emotional awareness show stronger affective reactivity and more activation in brain areas involved in emotion processing and simulation during the perception of masked happy facial expression than people with low emotional awareness. High emotional awareness appears to be characterized by an enhanced positive affective resonance to others at an automatic processing level.

Enhanced response to music in pregnancy.

Given a possible effect of estrogen on the pleasure-mediating dopaminergic system, musical appreciation in participants whose estrogen levels are naturally elevated during the oral contraceptive cycle and pregnancy has been investigated (n = 32, 15 pregnant, 17 nonpregnant; mean age 27.2). Results show more pronounced blood pressure responses to music in pregnant women. However, estrogen level differences during different phases of oral contraceptive intake did not have any effect, indicating that the observed changes were not related to estrogen. Effects of music on blood pressure were independent of valence, and dissonance elicited the greatest drop in blood pressure. Thus, the enhanced physiological response in pregnant women probably does not reflect a protective mechanism to avoid unpleasantness. Instead, this enhanced response is discussed in terms of a facilitation of prenatal conditioning to acoustical (musical) stimuli.

Alexithymic features and the labeling of brief emotional facial expressions - An fMRI study.

The ability to recognize subtle facial expressions can be valuable in social interaction to infer emotions and intentions of others. Research has shown that the personality trait of alexithymia is linked to difficulties labeling facial expressions especially when these are presented with temporal constraints. The present study investigates the neural mechanisms underlying this deficit. 50 young healthy volunteers had to label briefly presented (≤100ms) emotional (happy, angry, fearful) facial expressions masked by a neutral expression while undergoing functional magnetic resonance imaging (fMRI). A multi-method approach (20-Item Toronto Alexithymia Scale and Toronto Structured Interview for Alexithymia) was administered to assess alexithymic tendencies. Behavioral results point to a global deficit of alexithymic individuals in labeling brief facial expressions. Alexithymia was related to decreased response of the ventral striatum to negative facial expressions. Moreover, alexithymia was associated with lowered activation in frontal, temporal and occipital cortices. Our data suggest that alexithymic individuals have difficulties in creating appropriate representations of the emotional state of other persons under temporal constraints. These deficiencies could lead to problems in labeling other people׳s facial emotions.

Alexithymia is related to differences in gray matter volume: a voxel-based morphometry study.

OBJECTIVE: Alexithymia has been characterized as the inability to identify and describe feelings. Functional imaging studies have revealed that alexithymia is linked to reactivity changes in emotion- and face-processing-relevant brain areas. In this respect, anterior cingulate cortex (ACC), amygdala, anterior insula and fusiform gyrus (FFG) have been consistently reported. However, it remains to be clarified whether alexithymia is also associated with structural differences. METHODS: Voxel-based morphometry on T1-weighted magnetic resonance images was used to investigate gray matter volume in 17 high alexithymics (HA) and 17 gender-matched low alexithymics (LA), which were selected from a sample of 161 healthy volunteers on basis of the 20-item Toronto Alexithymia Scale. Data were analyzed as statistic parametric maps for the comparisons LA>HA and HA>LA in a priori determined regions of interests (ROIs), i.e., ACC, amygdala, anterior insula and FFG. Moreover, an exploratory whole brain analysis was accomplished. RESULTS: For the contrast LA>HA, significant clusters were detected in the ACC, left amygdala and left anterior insula. Additionally, the whole brain analysis revealed volume differences in the left middle temporal gyrus. No significant differences were found for the comparison HA>LA. CONCLUSION: Our findings suggest that high compared to low alexithymics show less gray matter volume in several emotion-relevant brain areas. These structural differences might contribute to the functional alterations found in previous imaging studies in alexithymia.

Adult attachment anxiety is associated with enhanced automatic neural response to positive facial expression.

According to social psychology models of adult attachment, a fundamental dimension of attachment is anxiety. Individuals who are high in attachment anxiety are motivated to achieve intimacy in relationships, but are mistrustful of others and their availability. Behavioral research has shown that anxiously attached persons are vigilant for emotional facial expression, but the neural substrates underlying this perceptual sensitivity remain largely unknown. In the present study functional magnetic resonance imaging was used to examine automatic brain reactivity to approach-related facial emotions as a function of attachment anxiety in a sample of 109 healthy adults. Pictures of sad and happy faces were presented masked by neutral faces. The Relationship Scales Questionnaire (RSQ) was used to assess attachment style. Attachment anxiety was correlated with depressivity, trait anxiety, and attachment avoidance. Controlling for these variables, attachment-related anxiety was positively related to responses in left inferior, middle, and medial prefrontal areas, globus pallidus, claustrum, and right cerebellum to masked happy facial expression. Attachment anxiety was not found to be associated with brain activation due to masked sad faces. Our findings suggest that anxiously attached adults are automatically more responsive to positive approach-related facial expression in brain areas that are involved in the perception of facial emotion, facial mimicry, or the assessment of affective value and social distance.

A public data hub for benchmarking common brain-computer interface algorithms.

Methods of statistical machine learning have recently proven to be very useful in contemporary brain-computer interface (BCI) research based on the discrimination of electroencephalogram (EEG) patterns. Because of this, many research groups develop new algorithms for both feature extraction and classification. However, until now, no large-scale comparison of these algorithms has been accomplished due to the fact that little EEG data is publicly available. Therefore, we at Team PhyPA recorded 32-channel EEGs, electromyograms and electrooculograms of 36 participants during a simple finger movement task. The data are published on our website www.phypa.org and are freely available for downloading. We encourage BCI researchers to test their algorithms on these data and share their results. This work also presents exemplary benchmarking procedures of common feature extraction methods for slow cortical potentials and event-related desynchronization as well as for classification algorithms based on these features.

A Dry EEG-System for Scientific Research and Brain-Computer Interfaces.

Although it ranks among the oldest tools in neuroscientific research, electroencephalography (EEG) still forms the method of choice in a wide variety of clinical and research applications. In the context of brain-computer interfacing (BCI), EEG recently has become a tool to enhance human-machine interaction. EEG could be employed in a wider range of environments, especially for the use of BCI systems in a clinical context or at the homes of patients. However, the application of EEG in these contexts is impeded by the cumbersome preparation of the electrodes with conductive gel that is necessary to lower the impedance between electrodes and scalp. Dry electrodes could provide a solution to this barrier and allow for EEG applications outside the laboratory. In addition, dry electrodes may reduce the time needed for neurological exams in clinical practice. This study evaluates a prototype of a three-channel dry electrode EEG system, comparing it to state-of-the-art conventional EEG electrodes. Two experimental paradigms were used: first, event-related potentials (ERP) were investigated with a variant of the oddball paradigm. Second, features of the frequency domain were compared by a paradigm inducing occipital alpha. Furthermore, both paradigms were used to evaluate BCI classification accuracies of both EEG systems. Amplitude and temporal structure of ERPs as well as features in the frequency domain did not differ significantly between the EEG systems. BCI classification accuracies were equally high in both systems when the frequency domain was considered. With respect to the oddball classification accuracy, there were slight differences between the wet and dry electrode systems. We conclude that the tested dry electrodes were capable to detect EEG signals with good quality and that these signals can be used for research or BCI applications. Easy to handle electrodes may help to foster the use of EEG among a wider range of potential users.