The dynamics of error processing in the human brain as reflected by high-gamma activity in noninvasive and intracranial EEG.

Error detection in motor behavior is a fundamental cognitive function heavily relying on local cortical information processing. Neural activity in the high-gamma frequency band (HGB) closely reflects such local cortical processing, but little is known about its role in error processing, particularly in the healthy human brain. Here we characterize the error-related response of the human brain based on data obtained with noninvasive EEG optimized for HGB mapping in 31 healthy subjects (15 females, 16 males), and additional intracranial EEG data from 9 epilepsy patients (4 females, 5 males). Our findings reveal a multiscale picture of the global and local dynamics of error-related HGB activity in the human brain. On the global level as reflected in the noninvasive EEG, the error-related response started with an early component dominated by anterior brain regions, followed by a shift to parietal regions, and a subsequent phase characterized by sustained parietal HGB activity. This phase lasted for more than 1 s after the error onset. On the local level reflected in the intracranial EEG, a cascade of both transient and sustained error-related responses involved an even more extended network, spanning beyond frontal and parietal regions to the insula and the hippocampus. HGB mapping appeared especially well suited to investigate late, sustained components of the error response, possibly linked to downstream functional stages such as error-related learning and behavioral adaptation. Our findings establish the basic spatio-temporal properties of HGB activity as a neural correlate of error processing, complementing traditional error-related potential studies.

A Structured Approach to Test the Signal Quality of Electroencephalography Measurements During Use of Head-Mounted Displays for Virtual Reality Applications.

Joint applications of virtual reality (VR) systems and electroencephalography (EEG) offer numerous new possibilities ranging from behavioral science to therapy. VR systems allow for highly controlled experimental environments, while EEG offers a non-invasive window to brain activity with a millisecond-ranged temporal resolution. However, EEG measurements are highly susceptible to electromagnetic (EM) noise and the influence of EM noise of head-mounted-displays (HMDs) on EEG signal quality has not been conclusively investigated. In this paper, we propose a structured approach to test HMDs for EM noise potentially harmful to EEG measures. The approach verifies the impact of HMDs on the frequency- and time-domain of the EEG signal recorded in healthy subjects. The verification task includes a comparison of conditions with and without an HMD during (i) an eyes-open vs. eyes-closed task, and (ii) with respect to the sensory- evoked brain activity. The approach is developed and tested to derive potential effects of two commercial HMDs, the Oculus Rift and the HTC Vive Pro, on the quality of 64-channel EEG measurements. The results show that the HMDs consistently introduce artifacts, especially at the line hum of 50 Hz and the HMD refresh rate of 90 Hz, respectively, and their harmonics. The frequency range that is typically most important in non-invasive EEG research and applications (<50 Hz) however, remained largely unaffected. Hence, our findings demonstrate that high-quality EEG recordings, at least in the frequency range up to 50 Hz, can be obtained with the two tested HMDs. However, the number of commercially available HMDs is constantly rising. We strongly suggest to thoroughly test such devices upfront since each HMD will most likely have its own EM footprint and this article provides a structured approach to implement such tests with arbitrary devices.

Hybrid Brain-Computer-Interfacing for Human-Compliant Robots: Inferring Continuous Subjective Ratings With Deep Regression.

Appropriate robot behavior during human-robot interaction is a key part in the development of human-compliant assistive robotic systems. This study poses the question of how to continuously evaluate the quality of robotic behavior in a hybrid brain-computer interfacing (BCI) task, combining brain and non-brain signals, and how to use the collected information to adapt the robot's behavior accordingly. To this aim, we developed a rating system compatible with EEG recordings, requiring the users to execute only small movements with their thumb on a wireless controller to rate the robot's behavior on a continuous scale. The ratings were recorded together with dry EEG, respiration, ECG, and robotic joint angles in ROS. Pilot experiments were conducted with three users that had different levels of previous experience with robots. The results demonstrate the feasibility to obtain continuous rating data that give insight into the subjective user perception during direct human-robot interaction. The rating data suggests differences in subjective perception for users with no, moderate, or substantial previous robot experience. Furthermore, a variety of regression techniques, including deep CNNs, allowed us to predict the subjective ratings. Performance was better when using the position of the robotic hand than when using EEG, ECG, or respiration. A consistent advantage of features expected to be related to a motor bias could not be found. Across-user predictions showed that the models most likely learned a combination of general and individual features across-users. A transfer of pre-trained regressor to a new user was especially accurate in users with more experience. For future research, studies with more participants will be needed to evaluate the methodology for its use in practice. Data and code to reproduce this study are available at https://github.com/TNTLFreiburg/NiceBot.

Mapping the fine structure of cortical activity with different micro-ECoG electrode array geometries.

OBJECTIVE: Innovations in micro-electrocorticography (µECoG) electrode array manufacturing now allow for intricate designs with smaller contact diameters and/or pitch (i.e. inter-contact distance) down to the sub-mm range. The aims of the present study were: (i) to investigate whether frequency ranges up to 400 Hz can be reproducibly observed in µECoG recordings and (ii) to examine how differences in topographical substructure between these frequency bands and electrode array geometries can be quantified. We also investigated, for the first time, the influence of blood vessels on signal properties and assessed the influence of cortical vasculature on topographic mapping. APPROACH: The present study employed two µECoG electrode arrays with different contact diameters and inter-contact distances, which were used to characterize neural activity from the somatosensory cortex of minipigs in a broad frequency range up to 400 Hz. The analysed neural data were recorded in acute experiments under anaesthesia during peripheral electrical stimulation. MAIN RESULTS: We observed that µECoG recordings reliably revealed multi-focal cortical somatosensory response patterns, in which response peaks were often less than 1 cm apart and would thus not have been resolvable with conventional ECoG. The response patterns differed by stimulation site and intensity, they were distinct for different frequency bands, and the results of functional mapping proved independent of cortical vascular. Our analysis of different frequency bands exhibited differences in the number of activation peaks in topographical substructures. Notably, signal strength and signal-to-noise ratios differed between the two electrode arrays, possibly due to their different sensitivity for variations in spatial patterns and signal strengths. SIGNIFICANCE: Our findings that the geometry of µECoG electrode arrays can strongly influence their recording performance can help to make informed decisions that maybe important in number of clinical contexts, including high-resolution brain mapping, advanced epilepsy diagnostics or brain-machine interfacing.