Biology subjects

Fiederer, L. D. J.

Publications and source records attributed to Fiederer, L. D. J..

3 recordsLinked to original sources

bioRxiv2019

Topography, Spectral Characteristics, and Extra-to-Intracranial Propagation Pathways of EMG

ObjectiveIntracranial EEG (iEEG) plays an increasingly important role in neuroscientific research and can provide informative control signal for brain-machine interfaces (BMI). While it is clear that electromyographic (EMG) activity of extracranial origin reaches intracranial recordings, the topographic and spectral characteristics of intracranial EMG have been scarcely investigated. It is currently unclear how these characteristics compare to those of physiological brain activity. Little is also known about the exact pathways of extra- to intracranial volume conduction, including the role of craniotomy defects.\n\nMethodsIn 5 epilepsy patients under invasive pre-neurosurgical EEG monitoring, we examined chewing-related effects (ChREs) as a source of intracranial EMG activity and compared those effects with physiological brain activity of 9 patients during several behavioural tasks. These included speech production, finger movements, and music perception. Further, we analyzed the association of craniotomy defects (burr-holes and saw-lines) and the intracranial EMG-effects based on the individual post-operative images.\n\nResultsChRE presented with a spatially smooth distribution across almost all intracranial electrodes with the maximum below the temporal muscle. In contrast, the responses of neural origin were spatially more focalized. ChREs were broad-banded and had a higher spectral power and affected higher frequencies than event-related neural activity. ChRE were largely independent of the individual configuration of craniotomy defects. However, we found indications that the silicone sheet, in which electrocorticography (ECoG) electrodes are embedded, attenuates EMG influences, when sufficiently large.\n\nConclusionThe present work is the first comprehensive evaluation of topographic and spectral characteristics of EMG effects in iEEG based on a large sample of subjects. It shows that chewing-related EMG can affect iEEG recordings with higher power than typical physiological brain activity, especially in higher spectral frequencies. As the topographic pattern of ChRE is largely independent of the individual position of craniotomy defects, a direct pathway of volume conduction through the intact skull plays an important role for extra- to-intracranial signal propagation. Intracranial EMG activity related to natural behavior should be accounted for in neuroscientific and BMI applications, especially when based on high-frequency iEEG components. A detailed knowledge of EMG properties may help to design both EMG-reducing algorithms and ECoG grids with a high shielding factor.\n\nHighlightsO_LIFirst comprehensive description of chewing-related EMG artifacts in iEEG recordings\nC_LIO_LIEMG artifacts and brain activity have distinct topographic and spectral iEEG characteristics\nC_LIO_LIChewing EMG reaches the brain with higher spectral power than task-related brain activity\nC_LIO_LIChewing-related EMG artifacts are largely independent of the the position of craniotomy defects\nC_LI

neuroscience↗

bioRxiv2018

Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation

As autonomous service robots become more affordable and thus available for the general public, there is a growing need for user-friendly interfaces to control these systems. Control interfaces typically get more complicated with increasing complexity of the robotic tasks and the environment. Traditional control modalities as touch, speech or gesture commands are not necessarily suited for all users. While non-expert users can make the effort to familiarize themselves with a robotic system, paralyzed users may not be capable of controlling such systems even though they need robotic assistance most. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The system is composed of several interacting components: non-invasive neuronal signal recording and co-adaptive deep learning which form the brain-computer interface (BCI), high-level task planning based on referring expressions, navigation and manipulation planning as well as environmental perception. We extensively evaluate the BCI in various tasks, determine the performance of the goal formulation user interface and investigate its intuitiveness in a user study. Furthermore, we demonstrate the applicability and robustness of the system in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results show, the system is capable of adapting to frequent changes in the environment and reliably accomplishes given tasks within a reasonable amount of time. Combined with high-level planning using referring expressions and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based human-robot interactions.

neuroscience↗

bioRxiv2017

Global and Local Dynamics of High-Gamma Activity Underlying Error Processing in the Human Brain Revealed by Noninvasive and Intracranial EEG

Error detection in motor behavior is a fundamental cognitive function heavily relying on 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 comprehensive 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.\n\nSignificance StatementThere is great interest to understand how the human brain reacts to errors in goal-directed behavior. An important index of cortical and subcortical information processing is fast oscillatory brain activity, particularly in the high-gamma band (above 50 Hz). Here we show that it is possible to detect signatures of errors in event-related high-gamma responses with noninvasive techniques, characterize these responses comprehensively, and validate the EEG procedure for the detection of such signals. In addition, we demonstrate the added value of intracranial recordings pinpointing the fine-grained spatio-temporal patterns in error-related brain networks. We anticipate that the optimized noninvasive EEG techniques as described here will be helpful in many areas of cognitive neuroscience where fast oscillatory brain activity is of interest.

neuroscience↗