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Blinks and saccades, both ubiquitous in natural viewing conditions, cause rapid changes of visual inputs that are hardly consciously perceived. The neural dynamics in early visual areas of the human brain underlying this remarkable visual stability are still incompletely understood. We used electrocorticography (ECoG) from electrodes directly implanted on the human early visual areas V1, V2, V3d/v, V4d/v and the fusiform gyrus to investigate blink- and saccade-related neuronal suppression effects during non-experimental, free viewing conditions. We found a characteristic, biphasic, broadband gamma power decrease-increase pattern in all investigated visual areas. During saccades, a decrease in gamma power clearly preceded eye movement onset, at least in V1. This may indicate that cortical information processing is actively suppressed in human early visual areas before and during saccades, which then possibly mediates perceptual visual suppression. The following eye movement offset-related increase in gamma power may indicate the recovery of visual perception and the resumption of visual processing., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2021. Published by Elsevier Inc.)

Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding have typically analyzed a limited number of features, decoders, or both. For a I) more elaborate feature-based EEG analysis, and II) in-depth comparisons of both approaches, here we first develop a comprehensive feature-based framework, and then compare this framework to state-of-the-art end-to-end methods. To this aim, we apply the proposed feature-based framework and deep neural networks including an EEG-optimized temporal convolutional network (TCN) to the task of pathological versus non-pathological EEG classification. For a robust comparison, we chose the Temple University Hospital (TUH) Abnormal EEG Corpus (v2.0.0), which contains approximately 3000 EEG recordings. The results demonstrate that the proposed feature-based decoding framework can achieve accuracies on the same level as state-of-the-art deep neural networks. We find accuracies across both approaches in an astonishingly narrow range from 81 to 86%. Moreover, visualizations and analyses indicated that both approaches used similar aspects of the data, e.g., delta and theta band power at temporal electrode locations. We argue that the accuracies of current binary EEG pathology decoders could saturate near 90% due to the imperfect inter-rater agreement of the clinical labels, and that such decoders are already clinically useful, such as in areas where clinical EEG experts are rare. We make the proposed feature-based framework available open source and thus offer a new tool for EEG machine learning research., Competing Interests: Declaration of competing interest The authors declare no competing financial interests., (Copyright © 2020. Published by Elsevier Inc.)

Background: Intracranial electroencephalography (iEEG) is increasingly used in neuroscientific research. However, the position of the implanted electrodes varies greatly between patients, which makes group analyses particularly difficult. Therefore, an assignment procedure is needed that enables the neuroanatomical information to be obtained for each individual electrode contact., New Method: Here, we present a MATLAB-based electrode assignment approach for iEEG electrode contacts, implemented in the open-source toolbox ELAS, that allows a hierarchical probabilistic assignment of individual electrode contacts to cytoarchitectonically-defined brain areas. The here presented ELAS consists of two major steps: (I) a pre-assignment to the cerebral lobes and (II) a following probabilistic assignment based on lobe-specific probability maps of the SPM Anatomy Toolbox., Results: We analyzed iEEG data obtained in 14 epilepsy patients with a total of 783 intracranial electrode contacts. The neuroanatomical assignment to cortical brain areas was possible in 72.5% of the electrode contacts that were located on the lateral cortical convexity., Comparison With Existing Methods: This assignment procedure is to our knowledge the first approach that combines both individual macro-anatomical and cytoarchitectonic probabilistic information. Due to the integration of information about individual anatomical landmarks, incorrect assignments could be avoided in approx. 7% of electrode contacts., Conclusion: The present study demonstrates how probabilistic assignment procedures developed for the analysis of neuroimaging data can be adapted to iEEG, which is especially helpful for group analyses. The presented assignment approach is freely available via the open-source toolbox ELAS, including a 3D visualization and a file export for virtual reality setups., (Copyright © 2019. Published by Elsevier B.V.)

Resting-state fMRI was first described by Biswal et al in 1995 and has since then been widely used in both healthy subjects and patients with various neurologic, neurosurgical, and psychiatric disorders. As opposed to paradigm- or task-based functional MR imaging, resting-state fMRI does not require subjects to perform any specific task. The low-frequency oscillations of the resting-state fMRI signal have been shown to relate to the spontaneous neural activity. There are many ways to analyze resting-state fMRI data. In this review article, we will briefly describe a few of these and highlight the advantages and limitations of each. This description is to facilitate the adoption and use of resting-state fMRI in the clinical setting, helping neuroradiologists become familiar with these techniques and applying them for the care of patients with neurologic and psychiatric diseases., (© 2018 by American Journal of Neuroradiology.)

The number of implantable bidirectional neural interfaces available for neuroscientific research applications is still limited, despite the rapidly increasing number of customized components. We previously reported on how to translate available components into "ready-to-use" wireless implantable systems utilizing components off-the-shelf (COTS). The aim of the present study was to verify the viability of a micro-electrocorticographic ($\mu $ECoG) device built by this approach. Functionality for both neural recording and stimulation was evaluated in an ovine animal model using acoustic stimuli and cortical electrical stimulation, respectively. We show that auditory evoked responses were reliably recorded in both time and frequency domain and present data that demonstrates the cortical electrical stimulation functionality. The successful recording of neuronal activity suggests that the device can compete with existing implantable systems as a neurotechnological research tool.

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., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

The foremost aim of presurgical epilepsy evaluation is the delineation of the seizure onset zone (SOZ). There is increasing evidence that fast epileptic activity (FEA, 14-250 Hz) occurring interictally, i.e. between seizures, is predominantly localized within the SOZ. Currently it is unknown, which frequency band of FEA performs best in identifying the SOZ, although prior studies suggest highest concordance of spectral changes with the SOZ for high frequency changes. We suspected that FEA reflects dampened oscillations in local cortical excitatory-inhibitory neural networks, and that interictal FEA in the SOZ is a consequence of reduced oscillatory damping. We therefore predict a narrowing of the spectral bandwidth alongside increased amplitudes of spectral peaks during interictal FEA events. To test this hypothesis, we evaluated spectral changes during interictal FEA in invasive EEG (iEEG) recordings of 13 patients with focal epilepsy. In relative spectra of beta and gamma band changes (14-250 Hz) during FEA, we found that spectral peaks within the SOZ indeed were significantly more narrow-banded and their power changes were significantly higher than outside the SOZ. In contrast, the peak frequency did not differ within and outside the SOZ. Our results show that bandwidth and power changes of spectral modulations during FEA both help localizing the SOZ. We propose the spectral bandwidth as new source of information for the evaluation of EEG data.

Introduction: Resting-state connectivity patterns have been observed in humans and other mammal species, and can be recorded using a variety of different technologies. Functional connectivity has been previously compared between species using resting-state fMRI, but not in electrophysiological studies., Methods: We compared connectivity with implanted electrodes in humans (electrocorticography) to macaques and sheep (microelectrocorticography), which are capable of recording neural data at high frequencies with spatial precision. We specifically examined synchrony, implicated in functional integration between regions., Results: We found that connectivity strength was overwhelmingly similar in humans and monkeys for pairs of two different brain regions (prefrontal, motor, premotor, parietal), but differed more often within single brain regions. The two connectivity measures, correlation and phase locking value, were similar in most comparisons. Connectivity strength agreed more often between the species at higher frequencies. Where the species differed, monkey synchrony was stronger than human in all but one case. In contrast, human and sheep connectivity within somatosensory cortex diverged in almost all frequencies, with human connectivity stronger than sheep., Discussion: Our findings imply greater heterogeneity within regions in humans than in monkeys, but comparable functional interactions between regions in the two species. This suggests that monkeys may be effectively used to probe resting-state connectivity in humans, and that such findings can then be validated in humans. Although the discrepancy between humans and sheep is larger, we suggest that findings from sheep in highly invasive studies may be used to provide guidance for studies in other species.

Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping. Hum Brain Mapp 38:5391-5420, 2017. © 2017 Wiley Periodicals, Inc., (© 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.)

Although advances in neuroimaging have yielded insights into the intrinsic organization of human brain networks and their relevance to psychiatric and neurological disorders, there has been no translation of these insights into clinical practice. One necessary step toward clinical translation is identifying a summary metric of network function that is reproducible, reliable, and has known normative data, analogous to normed neuropsychological tests. Our aim was therefore to establish the proof of principle for such a metric, focusing on the default mode network (DMN). We compared three candidate summary metrics: global clustering coefficient, characteristic path length, and average connectivity. Across three samples totaling 322 healthy, mostly Caucasian adults, average connectivity performed best, with good internal consistency (Cronbach's α=0.69-0.70) and adequate eight-week test-retest reliability (intra-class coefficient=0.62 in a subsample N=65). We therefore present normative data for average connectivity of the DMN and its sub-networks. These proof of principle results are an important first step for the translation of neuroimaging to clinical practice. Ultimately, a normed summary metric will allow a single patient's DMN function to be quantified and interpreted relative to normative peers.

Goal: To identify and overcome barriers to creating new neurotechnologies capable of restoring both motor and sensory function in individuals with neurological conditions., Methods: This report builds upon the outcomes of a joint workshop between the US National Science Foundation and the German Research Foundation on New Perspectives in Neuroengineering and Neurotechnology convened in Arlington, VA, USA, November 13-14, 2014., Results: The participants identified key technological challenges for recording and manipulating neural activity, decoding, and interpreting brain data in the presence of plasticity, and early considerations of ethical and social issues pertinent to the adoption of neurotechnologies., Conclusions: The envisaged progress in neuroengineering requires tightly integrated hardware and signal processing efforts, advances in understanding of physiological adaptations to closed-loop interactions with neural devices, and an open dialog with stakeholders and potential end-users of neurotechnology., Significance: The development of new neurotechnologies (e.g., bidirectional brain-computer interfaces) could significantly improve the quality of life of people living with the effects of brain or spinal cord injury, or other neurodegenerative diseases. Focused efforts aimed at overcoming the remaining barriers at the electrode tissue interface, developing implantable hardware with on-board computation, and refining stimulation methods to precisely activate neural tissue will advance both our understanding of brain function and our ability to treat currently intractable disorders of the nervous system.

Reconstruction of the electrical sources of human EEG activity at high spatio-temporal accuracy is an important aim in neuroscience and neurological diagnostics. Over the last decades, numerous studies have demonstrated that realistic modeling of head anatomy improves the accuracy of source reconstruction of EEG signals. For example, including a cerebro-spinal fluid compartment and the anisotropy of white matter electrical conductivity were both shown to significantly reduce modeling errors. Here, we for the first time quantify the role of detailed reconstructions of the cerebral blood vessels in volume conductor head modeling for EEG. To study the role of the highly arborized cerebral blood vessels, we created a submillimeter head model based on ultra-high-field-strength (7T) structural MRI datasets. Blood vessels (arteries and emissary/intraosseous veins) were segmented using Frangi multi-scale vesselness filtering. The final head model consisted of a geometry-adapted cubic mesh with over 17×10(6) nodes. We solved the forward model using a finite-element-method (FEM) transfer matrix approach, which allowed reducing computation times substantially and quantified the importance of the blood vessel compartment by computing forward and inverse errors resulting from ignoring the blood vessels. Our results show that ignoring emissary veins piercing the skull leads to focal localization errors of approx. 5 to 15mm. Large errors (>2cm) were observed due to the carotid arteries and the dense arterial vasculature in areas such as in the insula or in the medial temporal lobe. Thus, in such predisposed areas, errors caused by neglecting blood vessels can reach similar magnitudes as those previously reported for neglecting white matter anisotropy, the CSF or the dura - structures which are generally considered important components of realistic EEG head models. Our findings thus imply that including a realistic blood vessel compartment in EEG head models will be helpful to improve the accuracy of EEG source analyses particularly when high accuracies in brain areas with dense vasculature are required., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Analyzing single trial brain activity remains a challenging problem in the neurosciences. We gain purchase on this problem by focusing on globally synchronous fields in within-trial evoked brain activity, rather than on localized peaks in the trial-averaged evoked response (ER). We analyzed data from three measurement modalities, each with different spatial resolutions: magnetoencephalogram (MEG), electroencephalogram (EEG) and electrocorticogram (ECoG). We first characterized the ER in terms of summation of phase and amplitude components over trials. Both contributed to the ER, as expected, but the ER topography was dominated by the phase component. This means the observed topography of cross-trial phase will not necessarily reflect the phase topography within trials. To assess the organization of within-trial phase, traveling wave (TW) components were quantified by computing the phase gradient. TWs were intermittent but ubiquitous in the within-trial evoked brain activity. At most task-relevant times and frequencies, the within-trial phase topography was described better by a TW than by the trial-average of phase. The trial-average of the TW components also reproduced the topography of the ER; we suggest that the ER topography arises, in large part, as an average over TW behaviors. These findings were consistent across the three measurement modalities. We conclude that, while phase is critical to understanding the topography of event-related activity, the preliminary step of collating cortical signals across trials can obscure the TW components in brain activity and lead to an underestimation of the coherent motion of cortical fields., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Functional organization units of the cerebral cortex exist over a wide range of spatial scales, from local circuits to entire cortical areas. In the last decades, scale-space representations of neuroimaging data suited to probe the multi-scale nature of cortical functional organization have been introduced and methodologically elaborated. For this purpose, responses are statistically detected over a range of spatial scales using a family of Gaussian filters, with small filters being related to fine and large filters-to coarse spatial scales. The goal of the present study was to investigate the degree of variability of fMRI-response patterns over a broad range of observation scales. To this aim, the same fMRI data set obtained from 18 subjects during a visuomotor task was analyzed with a range of filters from 4- to 16-mm full width at half-maximum (FWHM). We found substantial observation-scale-related variability. For example, using filter widths of 6- to 8-mm FWHM, in the group-level results, significant responses in the right secondary visual but not in the primary visual cortex were detected. However, when larger filters were used, the responses in the right primary visual cortex reached significance. Often, responses in probabilistically defined areas were significant when both small and large filters, but not intermediate filter widths were applied. This suggests that brain responses can be organized in local clusters of multiple distinct activation foci. Our findings illustrate the potential of multi-scale fMRI analysis to reveal novel features in the spatial organization of human brain responses., (Copyright © 2011 Wiley-Liss, Inc.)

Brain-machine interface (BMI) devices make errors in decoding. Detecting these errors online from neuronal activity can improve BMI performance by modifying the decoding algorithm and by correcting the errors made. Here, we study the neuronal correlates of two different types of errors which can both be employed in BMI: (i) the execution error, due to inaccurate decoding of the subjects' movement intention; (ii) the outcome error, due to not achieving the goal of the movement. We demonstrate that, in electrocorticographic (ECoG) recordings from the surface of the human brain, strong error-related neural responses (ERNRs) for both types of errors can be observed. ERNRs were present in the low and high frequency components of the ECoG signals, with both signal components carrying partially independent information. Moreover, the observed ERNRs can be used to discriminate between error types, with high accuracy (≥83%) obtained already from single electrode signals. We found ERNRs in multiple cortical areas, including motor and somatosensory cortex. As the motor cortex is the primary target area for recording control signals for a BMI, an adaptive motor BMI utilizing these error signals may not require additional electrode implants in other brain areas.

Emerging research on brain-machine interfaces (BMIs) requires the development of animal models for testing implantable BMI electrodes. New models are necessary in order to characterize and test newly constructed electrodes in an acute environment, and their properties and performance need to be evaluated in long-term, chronic implantations. Owing to their availability, small size and neuroanatomical similarity to the human brain, minipigs are frequently used for neurological studies. Despite this fact, there are still no standardized experimental and neurosurgical procedures available for recording of cortical potentials using implantable BMI electrodes in minipigs, and, until now, it was unclear whether these animals could also be used for long-term subdural electrode implantations. We have therefore evaluated the potential use of minipigs for acute and chronic implantation of micro-electrocorticogram (μECoG) electrodes we newly developed for BMI applications and we present a standardized neurosurgical approach to the minipig's cerebral cortex. A neurophysiological setup is described which is suitable to perform recordings of somatosensory evoked potentials (SEPs) with high spatial resolution - down to approx. 1-mm inter-electrode distance. Perioperative management, anesthesia and anatomical landmarks for electrode placement are discussed and common surgical pitfalls are described. While, due to their specific cranial anatomy, minipigs appear not optimally suited for chronic subdural implantations, the findings of the present study indicate that μECoG recording from the minipig cortex is a valuable new approach for acute in vivo characterization of subdural BMI electrode function., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Brain-machine interfaces (BMIs) can be characterized by the technique used to measure brain activity and by the way different brain signals are translated into commands that control an effector. We give an overview of different approaches and focus on a particular BMI approach: the movement of an artificial effector (e.g. arm prosthesis to the right) by those motor cortical signals that control the equivalent movement of a corresponding body part (e.g. arm movement to the right). This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single-units. Here, we review recent findings showing that analog neuronal population signals, ranging from intracortical local field potentials over epicortical ECoG to non-invasive EEG and MEG, can also be used to decode movement direction and continuous movement trajectories. Therefore, these signals might provide additional or alternative control for this BMI approach, with possible advantages due to reduced invasiveness.

Both invasive and non-invasive electroencephalographic (EEG) recordings from the human brain have an increasingly important role in neuroscience research and are candidate modalities for medical brain-machine interfacing. It is often assumed that the major artifacts that compromise non-invasive EEG, such as caused by blinks and eye movement, are absent in invasive EEG recordings. Quantitative investigations on the signal quality of simultaneously recorded invasive and non-invasive EEG in terms of artifact contamination are, however, lacking. Here we compared blink related artifacts in non-invasive and invasive EEG, simultaneously recorded from prefrontal and motor cortical regions using an approach suitable for detection of small artifact contamination. As expected, we find blinks to cause pronounced artifacts in non-invasive EEG both above prefrontal and motor cortical regions. Unexpectedly, significant blink related artifacts were also found in the invasive recordings, in particular in the prefrontal region. Computing a ratio of artifact amplitude to the amplitude of ongoing brain activity, we find that the signal quality of invasive EEG is 20 to above 100 times better than that of simultaneously obtained non-invasive EEG. Thus, while our findings indicate that ocular artifacts do exist in invasive recordings, they also highlight the much better signal quality of invasive compared to non-invasive EEG data. Our findings suggest that blinks should be taken into account in the experimental design of ECoG studies, particularly when event related potentials in fronto-anterior brain regions are analyzed. Moreover, our results encourage the application of techniques for reducing ocular artifacts to further optimize the signal quality of invasive EEG.

Electrocorticographic (ECoG) recordings obtained using intracranially implanted electrodes in epilepsy patients indicate that high gamma band (HGB) activity of sensorimotor cortex is focally increased during voluntary movement. These movement related HGB modulations may play an important role in sensorimotor cortex function. It is however currently not clear to what extent this type of neural activity can be detected using non-invasive electroencephalography (EEG) and how similar HGB responses in healthy human subjects are to those observed in epilepsy patients. Using the same arm reaching task, we have investigated spectral power changes both in intracranial ECoG recordings in epilepsy patients and in non-invasive EEG recordings optimized for detecting HGB activity in healthy subjects. Our results show a common HGB response pattern both in ECoG and EEG recorded above the sensorimotor cortex contralateral to the side of arm movement. In both cases, HGB activity increased around movement onset in the 60-90 Hz range and became most pronounced at reaching movement end. Additionally, we found EEG HGB activity above the frontal midline possibly generated by the anterior supplementary motor area (SMA), a region that was however not covered by the intracranial electrodes used in the present study. In summary, our findings show that HGB activity from human sensorimotor cortex can be non-invasively detected in healthy subjects using EEG, opening a new perspective for investigating the role of high gamma range neuronal activity both in function and dysfunction of the human cortical sensorimotor network.