Abstract Recent developments in EEG hardware and analyses approaches allow for recordings both stationary mobile settings. Irrespective of the experimental setting, are contaminated with noise that has to be removed before data can functionally interpreted. Independent component analysis (ICA) is a commonly used tool remove artifacts such as eye movement, muscle activity, external from analyze activity on level effective brain sources. The effectiveness filtering one key preprocessing step...

Abstract Removing power line noise and other frequency‐specific artifacts from electrophysiological data without affecting neural signals remains a challenging task. Recently, an approach was introduced that combines spectral spatial filtering to effectively remove noise: Zapline. This algorithm, however, requires manual selection of the frequency number components during filtering. Moreover, it assumes topography are stable over time, which is often not warranted. To overcome these issues,...

Abstract Advancements in hardware technology and analysis methods allow more mobility electroencephalography (EEG) experiments. Mobile Brain/Body Imaging (MoBI) studies may record various types of data such as motion or eye tracking addition to neural activity. Although there are options available analyze EEG a standardized way, they do not fully cover complex multimodal from mobile We thus propose the BeMoBIL Pipeline, an easy-to-use pipeline MATLAB that supports time-synchronized handling...

The retrosplenial complex (RSC) plays a crucial role in spatial orientation by computing heading direction and translating between distinct reference frames based on multi-sensory information. While invasive studies allow investigating computation moving animals, established non-invasive analyses of human brain dynamics are restricted to stationary setups. To investigate the RSC actively humans, we used Mobile Brain/Body Imaging approach synchronizing electroencephalography with motion...

Coupling behavioral measures and brain imaging in naturalistic, ecological conditions is key to comprehend the neural bases of spatial navigation. This highly integrative function encompasses sensorimotor, cognitive, executive processes that jointly mediate active exploration learning. However, most neuroimaging approaches humans are based on static, motion-constrained paradigms they do not account for all these processes, particular multisensory integration. Following Mobile Brain/Body...

Abstract Electroencephalography (EEG) studies increasingly utilize more mobile experimental protocols, leading to and stronger artifacts in the recorded data. Independent Component Analysis (ICA) is commonly used remove these artifacts. It standard practice artifactual samples before ICA improve decomposition, for example using automatic tools such as sample rejection option of AMICA algorithm. However, effects movement intensity strength on decomposition have not been systematically...

Objective. Neural interfaces hold significant promise to implicitly track user experience. Their application in virtual and augmented reality (VR/AR) simulations is especially favorable as it allows assessment without breaking the immersive In VR, designing immersion one key challenge. Subjective questionnaires are established metrics assess effectiveness of VR simulations. However, administering such requires experience they supposed assess.Approach. We present a complimentary metric based...

Abstract Passive brain-computer interfaces (passive BCIs, pBCIs) enable computers to unobtrusively decipher aspects of a user's mental state in real time from recordings brain activity, e.g. electroencephalography (EEG). When used during human-computer interaction (HCI), this allows computer dynamically adapt for enhancing the subjective user experience. For transitioning controlled laboratory environments practical applications, understanding BCI performance contexts is utmost importance....

Recent developments in EEG hardware and analyses approaches allow for recordings both stationary mobile settings. Irrespective of the experimental setting, are contaminated with noise that has to be removed before data can functionally interpreted. Independent component analysis (ICA) is a commonly used tool remove artifacts such as eye movement, muscle activity, external from analyze activity on level effective brain sources. While effectiveness filtering one key preprocessing step improve...

Abstract Objective. Magneto- and electroencephalography (M/EEG) measurements record a mix of signals from the brain, eyes, muscles. These can be disentangled for artifact cleaning e.g. using spatial filtering techniques. However, correctly localizing identifying these components relies on head models that so far only take brain sources into account. Approach. We thus developed Head Artifact Model Tripoles (HArtMuT). This volume conduction model extends to neck includes as well representing...

This work documents the implementation of a dynamic personality as Human-Robot-Interface well test its effect. It creates an emergent structure for robot based upon psychological theory. The resulting emotions lead to different reactions and speech output robot. personalities had great impact on user, appeared more human-like emotional.

Abstract Electroencephalographic (EEG) source imaging depends upon sophisticated signal processing algorithms for data cleaning, separation, and localization. Typically, these problems are addressed separately using a variety of heuristics, making it difficult to systematize methodology extracting robust EEG estimates on wide range experimental paradigms. In this paper, we propose unifying Bayesian framework in which apparently dissimilar can be understood solved principled manner single...

Abstract Removing power line noise and other frequency-specific artifacts from electrophysiological data without affecting neural signals remains a challenging task. Recently, an approach was introduced that combines spectral spatial filtering to effectively remove noise: Zapline (de Cheveigné, 2020). This algorithm, however, requires manual selection of the frequency number components during filtering. Moreover, it assumes topography are stable over time, which is often not warranted. To...

Abstract The retrosplenial complex (RSC) plays a crucial role in spatial orientation by computing heading direction and translating between distinct reference frames based on multi-sensory information. While invasive studies allow investigating computation moving animals, established non-invasive analyses of human brain dynamics are restricted to stationary setups. To investigate the RSC actively humans, we used Mobile Brain/Body Imaging approach synchronizing electroencephalography with...

Abstract Magneto- and electroencephalography (M/EEG) measurements record a mix of signals from the brain, eyes, muscles. These can be disentangled for artifact cleaning e.g. using spatial filtering techniques. However, correctly localizing identifying these components relies on head models that so far only take brain sources into account. We thus developed Head Artefact Model Tripoles (HArtMuT). This volume conduction model extends to neck includes as well representing eyes muscles modeled...

Abstract Objective Electroencephalography (EEG) studies increasingly make use of more ecologically valid experimental protocols involving mobile participants who actively engage with their environment (MoBI; Gramann et al., 2011). These paradigms lead to increased artifacts in the recorded data that are often treated using Independent Component Analysis (ICA). When analyzing EEG data, especially a context, removing samples regarded as artifactual is common approach before computing ICA....

The emerging integration of Brain-Computer Interfaces (BCIs) in human-robot collaboration holds promise for dynamic adaptive interaction. use electroencephalogram (EEG)-measured error-related potentials (ErrPs) online error detection assistive devices offers a practical method improving the reliability such devices. However, continuous faces challenges as developing efficient and lightweight classification techniques quick predictions, reducing false alarms from artifacts, dealing with...

Abstract Coupling behavioral measures and brain imaging in naturalistic, ecological conditions is key to comprehend the neural bases of spatial navigation. This highly-integrative function encompasses sensorimotor, cognitive, executive processes that jointly mediate active exploration learning. However, most neuroimaging approaches humans are based on static, motion constrained paradigms they do not account for all these processes, particular multisensory integration. Following Mobile...