@article {4561, title = {Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods}, journal = {Journal of Neuroscience Methods}, volume = {404}, year = {2024}, pages = {110056}, author = {Blenkmann, Alejandro Omar and Leske, Sabine Liliana and Llorens, Ana{\"\i}s and Lin, Jack J and Chang, Edward F and Brunner, Peter and Schalk, Gerwin and Ivanovic, Jugoslav and Larsson, P{\r a}l Gunnar and Knight, Robert Thomas and others} } @article {4584, title = {Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods.}, journal = {J Neurosci Methods}, volume = {404}, year = {2024}, month = {2024 Apr}, pages = {110056}, abstract = {

BACKGROUND: Intracranial electrodes are typically localized from post-implantation CT artifacts. Automatic~algorithms localizing low signal-to-noise ratio artifacts and high-density electrode arrays are missing. Additionally, implantation of grids/strips introduces brain deformations, resulting in registration errors when fusing post-implantation CT and pre-implantation MR images. Brain-shift compensation methods project electrode coordinates to cortex, but either fail to produce smooth solutions or do not account for brain deformations.

NEW METHODS: We first introduce GridFit, a model-based fitting approach that simultaneously localizes all electrodes{\textquoteright} CT artifacts in grids, strips, or depth arrays. Second, we present CEPA, a brain-shift compensation algorithm combining orthogonal-based projections, spring-mesh models, and spatial regularization constraints.

RESULTS: We tested GridFit on \~{}6000 simulated scenarios. The localization of CT artifacts showed robust performance under difficult scenarios, such as noise, overlaps, and high-density implants (<1~mm errors). Validation with data from 20 challenging patients showed 99\% accurate localization of the electrodes (3160/3192). We tested CEPA brain-shift compensation with data from 15 patients. Projections accounted for simple mechanical deformation principles with <~0.4~mm errors. The inter-electrode distances smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance.

COMPARISON WITH EXISTING METHODS: GridFit succeeded in difficult scenarios that challenged available methods and outperformed visual localization by preserving the inter-electrode distance. CEPA registration errors were smaller than those obtained for well-established alternatives. Additionally, modeling resting-state high-frequency activity in five patients further supported CEPA.

CONCLUSION: GridFit and CEPA are versatile tools for registering intracranial electrode coordinates, providing highly accurate results even in the most challenging implantation scenarios. The methods are implemented in the iElectrodes open-source toolbox.

}, keywords = {Brain, Cerebral Cortex, Electrodes, Electrodes, Implanted, Electroencephalography, Humans, Magnetic Resonance Imaging}, issn = {1872-678X}, doi = {10.1016/j.jneumeth.2024.110056}, author = {Blenkmann, Alejandro Omar and Leske, Sabine Liliana and Llorens, Ana{\"\i}s and Lin, Jack J and Chang, Edward F and Brunner, Peter and Schalk, Gerwin and Ivanovic, Jugoslav and Larsson, P{\r a}l Gunnar and Knight, Robert Thomas and Endestad, Tor and Solbakk, Anne-Kristin} } @article {4554, title = {Non-invasive Brain-Computer Interfaces: State of the Art and Trends}, journal = {IEEE Reviews in Biomedical Engineering}, year = {2024}, author = {Edelman, Bradley J and Zhang, Shuailei and Schalk, Gerwin and Brunner, Peter and M{\"u}ller-Putz, Gernot and Guan, Cuntai and He, Bin} } @article {4462, title = {A motor association area in the depths of the central sulcus.}, journal = {Nat Neurosci}, volume = {26}, year = {2023}, month = {07/2023}, pages = {1165-1169}, abstract = {

Cells in the precentral gyrus directly send signals to the periphery to generate movement and are principally organized as a topological map of the body. We find that movement-induced electrophysiological responses from depth electrodes extend this map three-dimensionally throughout the gyrus. Unexpectedly, this organization is interrupted by a previously undescribed motor association area in the depths of the midlateral aspect of the central sulcus. This {\textquoteright}Rolandic motor association{\textquoteright} (RMA) area is active during movements of different body parts from both sides of the body and may be important for coordinating complex behaviors.

}, keywords = {Brain Mapping, Motor Cortex, Movement}, issn = {1546-1726}, doi = {10.1038/s41593-023-01346-z}, author = {Jensen, Michael A and Huang, Harvey and Valencia, Gabriela Ojeda and Klassen, Bryan T and van den Boom, Max A and Kaufmann, Timothy J and Schalk, Gerwin and Brunner, Peter and Worrell, Gregory A and Hermes, Dora and Miller, Kai J} } @article {4448, title = {Passive functional mapping of receptive language cortex during general anesthesia using electrocorticography.}, journal = {Clin Neurophysiol}, volume = {147}, year = {2023}, month = {03/2023}, pages = {31-44}, abstract = {

OBJECTIVE: To investigate the feasibility of passive functional mapping in the receptive language cortex during general anesthesia using electrocorticographic (ECoG) signals.

METHODS: We used subdurally placed ECoG grids to record cortical responses to speech stimuli during awake and anesthesia conditions. We identified the cortical areas with significant responses to the stimuli using the spectro-temporal consistency of the brain signal in the broadband gamma (BBG) frequency band (70-170~Hz).

RESULTS: We found that ECoG BBG responses during general anesthesia effectively identify cortical regions associated with receptive language function. Our analyses demonstrated that the ability to identify receptive language cortex varies across different states and depths of anesthesia. We confirmed these results by comparing them to receptive language areas identified during the awake condition. Quantification of these results demonstrated an average sensitivity and specificity of passive language mapping during general anesthesia to be 49{\textpm}7.7\% and 100\%, respectively.

CONCLUSION: Our results demonstrate that mapping receptive language cortex in patients during general anesthesia is feasible.

SIGNIFICANCE: Our proposed protocol could greatly expand the population of patients that can benefit from passive language mapping techniques, and could eliminate the risks associated with electrocortical stimulation during an awake craniotomy.

}, keywords = {Anesthesia, General, Brain, Brain Mapping, Cerebral Cortex, Electrocorticography, Humans, Language}, issn = {1872-8952}, doi = {10.1016/j.clinph.2022.11.021}, author = {Nourmohammadi, Amin and Swift, James R and de Pesters, Adriana and Guay, Christian S and Adamo, Matthew A and Dalfino, John C and Ritaccio, Anthony L and Schalk, Gerwin and Brunner, Peter} } @article {4341, title = {Automated intraoperative central sulcus localization and somatotopic mapping using median nerve stimulation.}, journal = {J Neural Eng}, year = {2022}, month = {07/2022}, abstract = {

OBJECTIVE: Accurate identification of functional cortical regions is essential in neurological resection. The central sulcus (CS) is an important landmark that delineates functional cortical regions. Median nerve stimulation (MNS) is a standard procedure to identify the position of the CS intraoperatively. In this paper, we introduce an automated procedure that uses MNS to rapidly localize the CS and create functional somatotopic maps.

APPROACH: We recorded electrocorticographic signals from 13 patients who underwent MNS in the course of an awake craniotomy. We analyzed these signals to develop an automated procedure that determines the location of the CS and that also produces functional somatotopic maps.

MAIN RESULTS: The comparison between our automated method and visual inspection performed by the neurosurgeon shows that our procedure has a high sensitivity (89\%) in identifying the CS. Further, we found substantial concordance between the functional somatotopic maps generated by our method and passive functional mapping (92\% sensitivity).

SIGNIFICANCE: Our automated MNS-based method can rapidly localize the CS and create functional somatotopic maps without imposing additional burden on the clinical procedure. With additional development and validation, our method may lead to a diagnostic tool that guides neurosurgeon and reduces postoperative morbidity in patients undergoing resective brain surgery.

}, issn = {1741-2552}, doi = {10.1088/1741-2552/ac7dfd}, author = {Xie, Tao and Wu, Zehan and Schalk, Gerwin and Tong, Yusheng and Vato, Alessandro and Raviv, Nataly and Guo, Qinglong and Ye, Huanpeng and Sheng, Xinjun and Zhu, Xiangyang and Peter Brunner and Chen, Liang} } @article {4340, title = {Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals.}, journal = {Front Hum Neurosci}, volume = {15}, year = {2022}, month = {02/2022}, pages = {794654}, abstract = {

Recent computational models of perception conceptualize auditory oddball responses as signatures of a (Bayesian) learning process, in line with the influential view of the mismatch negativity (MMN) as a prediction error signal. Novel MMN experimental paradigms have put an emphasis on neurophysiological effects of manipulating regularity and predictability in sound sequences. This raises the question of the contextual adaptation of the learning process itself, which on the computational side speaks to the mechanisms of gain-modulated (or precision-weighted) prediction error. In this study using electrocorticographic (ECoG) signals, we manipulated the predictability of oddball sound sequences with two objectives: (i) Uncovering the computational process underlying trial-by-trial variations of the cortical responses. The fluctuations between trials, generally ignored by approaches based on averaged evoked responses, should reflect the learning involved. We used a general linear model (GLM) and Bayesian Model Reduction (BMR) to assess the respective contributions of experimental manipulations and learning mechanisms under probabilistic assumptions. (ii) To validate and expand on previous findings regarding the effect of changes in predictability using simultaneous EEG-MEG recordings. Our trial-by-trial analysis revealed only a few stimulus-responsive sensors but the measured effects appear to be consistent over subjects in both time and space. In time, they occur at the typical latency of the MMN (between 100 and 250 ms post-stimulus). In space, we found a dissociation between time-independent effects in more anterior temporal locations and time-dependent (learning) effects in more posterior locations. However, we could not observe any clear and reliable effect of our manipulation of predictability modulation onto the above learning process. Overall, these findings clearly demonstrate the potential of trial-to-trial modeling to unravel perceptual learning processes and their neurophysiological counterparts.

}, issn = {1662-5161}, doi = {10.3389/fnhum.2021.794654}, author = {Lecaignard, Fran{\c c}oise and Bertrand, Rapha{\"e}lle and Peter Brunner and Caclin, Anne and Schalk, Gerwin and Mattout, J{\'e}r{\'e}mie} } @article {4447, title = {Toward a fully implantable ecosystem for adaptive neuromodulation in humans: Preliminary experience with the CorTec BrainInterchange device in a canine model.}, journal = {Front Neurosci}, volume = {16}, year = {2022}, month = {12/2022}, pages = {932782}, abstract = {

This article describes initial work toward an ecosystem for adaptive neuromodulation in humans by documenting the experience of implanting CorTec{\textquoteright}s BrainInterchange (BIC) device in a beagle canine and using the BCI2000 environment to interact with the BIC device. It begins with laying out the substantial opportunity presented by a useful, easy-to-use, and widely available hardware/software ecosystem in the current landscape of the field of adaptive neuromodulation, and then describes experience with implantation, software integration, and post-surgical validation of recording of brain signals and implant parameters. Initial experience suggests that the hardware capabilities of the BIC device are fully supported by BCI2000, and that the BIC/BCI2000 device can record and process brain signals during free behavior. With further development and validation, the BIC/BCI2000 ecosystem could become an important tool for research into new adaptive neuromodulation protocols in humans.

}, issn = {1662-4548}, doi = {10.3389/fnins.2022.932782}, author = {Schalk, Gerwin and Worrell, Samuel and Mivalt, Filip and Belsten, Alexander and Kim, Inyong and Morris, Jonathan M and Hermes, Dora and Klassen, Bryan T and Staff, Nathan P and Messina, Steven and Kaufmann, Timothy and Rickert, J{\"o}rn and Brunner, Peter and Worrell, Gregory A and Miller, Kai J} } @article {4336, title = {Modulation in cortical excitability disrupts information transfer in perceptual-level stimulus processing.}, journal = {Neuroimage}, volume = {243}, year = {2021}, month = {11/2021}, pages = {118498}, abstract = {

Despite significant interest in the neural underpinnings of behavioral variability, little light has been shed on the cortical mechanism underlying the failure to respond to perceptual-level stimuli. We hypothesized that cortical activity resulting from perceptual-level stimuli is sensitive to the moment-to-moment fluctuations in cortical excitability, and thus may not suffice to produce a behavioral response. We tested this hypothesis using electrocorticographic recordings to follow the propagation of cortical activity in six human subjects that responded to perceptual-level auditory stimuli. Here we show that for presentations that did not result in a behavioral response, the likelihood of cortical activity decreased from auditory cortex to motor cortex, and was related to reduced local cortical excitability. Cortical excitability was quantified using instantaneous voltage during a short window prior to cortical activity onset. Therefore, when humans are presented with an auditory stimulus close to perceptual-level threshold, moment-by-moment fluctuations in cortical excitability determine whether cortical responses to sensory stimulation successfully connect auditory input to a resultant behavioral response.

}, keywords = {Acoustic Stimulation, Adult, Aged, Alpha Rhythm, Auditory Cortex, Brain Mapping, Cortical Excitability, Electrocorticography, Female, Humans, Male, Middle Aged}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2021.118498}, author = {Moheimanian, Ladan and Paraskevopoulou, Sivylla E and Adamek, Markus and Schalk, Gerwin and Peter Brunner} } @article {4335, title = {Within-subject reaction time variability: Role of cortical networks and underlying neurophysiological mechanisms.}, journal = {Neuroimage}, volume = {237}, year = {2021}, month = {08/2021}, pages = {118127}, abstract = {

Variations in reaction time are a ubiquitous characteristic of human behavior. Extensively documented, they have been successfully modeled using parameters of the subject or the task, but the neural basis of behavioral reaction time that varies within the same subject and the same task has been minimally studied. In this paper, we investigate behavioral reaction time variance using 28 datasets of direct cortical recordings in humans who engaged in four different types of simple sensory-motor reaction time tasks. Using a previously described technique that can identify the onset of population-level cortical activity and a novel functional connectivity algorithm described herein, we show that the cumulative latency difference of population-level neural activity across the task-related cortical network can explain up to 41\% of the trial-by-trial variance in reaction time. Furthermore, we show that reaction time variance may primarily be due to the latencies in specific brain regions and demonstrate that behavioral latency variance is accumulated across the whole task-related cortical network. Our results suggest that population-level neural activity monotonically increases prior to movement execution, and that trial-by-trial changes in that increase are, in part, accounted for by inhibitory activity indexed by low-frequency oscillations. This pre-movement neural activity explains 19\% of the measured variance in neural latencies in our data. Thus, our study provides a mechanistic explanation for a sizable fraction of behavioral reaction time when the subject{\textquoteright}s task is the same from trial to trial.

}, keywords = {Adult, Algorithms, Alpha Rhythm, Cerebral Cortex, Connectome, Electrocorticography, Female, Gamma Rhythm, Humans, Male, Middle Aged, Nerve Net, Psychomotor Performance, Reaction Time, Young Adult}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2021.118127}, author = {Paraskevopoulou, Sivylla E and Coon, William G and Peter Brunner and Miller, Kai J and Schalk, Gerwin} } @article {4334, title = {iEEGview: an open-source multifunction GUI-based Matlab toolbox for localization and visualization of human intracranial electrodes.}, journal = {J Neural Eng}, volume = {17}, year = {2019}, month = {12/2019}, pages = {016016}, abstract = {

OBJECTIVE: The precise localization of intracranial electrodes is a fundamental step relevant to the analysis of intracranial electroencephalography (iEEG) recordings in various fields. With the increasing development of iEEG studies in human neuroscience, higher requirements have been posed on the localization process, resulting in urgent demand for more integrated, easy-operation and versatile tools for electrode localization and visualization. With the aim of addressing this need, we develop an easy-to-use and multifunction toolbox called iEEGview, which can be used for the localization and visualization of human intracranial electrodes.

APPROACH: iEEGview is written in Matlab scripts and implemented with a GUI. From the GUI, by taking only pre-implant MRI and post-implant CT images as input, users can directly run the full localization pipeline including brain segmentation, image co-registration, electrode reconstruction, anatomical information identification, activation map generation and electrode projection from native brain space into common brain space for group analysis. Additionally, iEEGview implements methods for brain shift correction, visual location inspection on MRI slices and computation of certainty index in anatomical label assignment.

MAIN RESULTS: All the introduced functions of iEEGview work reliably and successfully, and are tested by images from 28 human subjects implanted with depth and/or subdural electrodes.

SIGNIFICANCE: iEEGview is the first public Matlab GUI-based software for intracranial electrode localization and visualization that holds integrated capabilities together within one pipeline. iEEGview promotes convenience and efficiency for the localization process, provides rich localization information for further analysis and offers solutions for addressing raised technical challenges. Therefore, it can serve as a useful tool in facilitating iEEG studies.

}, keywords = {Brain, Brain Mapping, Electrocorticography, Electrodes, Implanted, Electroencephalography, Humans, Magnetic Resonance Imaging}, issn = {1741-2552}, doi = {10.1088/1741-2552/ab51a5}, author = {Li, Guangye and Jiang, Shize and Chen, Chen and Peter Brunner and Wu, Zehan and Schalk, Gerwin and Chen, Liang and Zhang, Dingguo} } @inbook {4145, title = {BCI Software}, booktitle = {Brain{\textendash}Computer Interfaces Handbook: Technological and Theoretical Advances}, year = {2018}, pages = {323}, author = {Peter Brunner and Schalk, Gerwin} } @inbook {4146, title = {ECoG-Based BCIs}, booktitle = {Brain{\textendash}Computer Interfaces Handbook: Technological and Theoretical Advances}, year = {2018}, pages = {297}, author = {Gunduz, Aysegul and Schalk, Gerwin} } @article {4114, title = {Optimal referencing for stereo-electroencephalographic (SEEG) recordings}, journal = {NeuroImage}, volume = {183}, year = {2018}, month = {12/2018}, pages = {327-335}, abstract = {Stereo-electroencephalography (SEEG) is an intracranial recording technique in which depth electrodes are inserted in the brain as part of presurgical assessments for invasive brain surgery. SEEG recordings can tap into neural signals across the entire brain and thereby sample both cortical and subcortical sites. However, even though signal referencing is important for proper assessment of SEEG signals, no previous study has comprehensively evaluated the optimal referencing method for SEEG. In our study, we recorded SEEG data from 15 human subjects during a motor task, referencing them against the average of two white matter contacts (monopolar reference). We then subjected these signals to 5 different re-referencing approaches: common average reference (CAR), gray-white matter reference (GWR), electrode shaft reference (ESR), bipolar reference, and Laplacian reference. The results from three different signal quality metrics suggest the use of the Laplacian re-reference for study of local population-level activity and low-frequency oscillatory activity.}, keywords = {Noise subtraction, Referencing method, SEEG, Signal quality, Stereo-electroencephalography}, issn = {1053-8119}, doi = {10.1016/j.neuroimage.2018.08.020}, url = {https://www.sciencedirect.com/science/article/pii/S1053811918307183}, author = {Li, G and Jiang, S and Paraskevopoulou, S and Wang, M and Xu, Y and Wu, Z and Chen, L and Zhang, D and Schalk, Gerwin} } @inbook {4147, title = {Perspectives on Brain{\textendash}Computer Interfaces}, booktitle = {Brain{\textendash}Computer Interfaces Handbook}, year = {2018}, pages = {721{\textendash}724}, publisher = {CRC Press}, organization = {CRC Press}, author = {Schalk, Gerwin} } @article {4095, title = {Differential roles of high gamma and local motor potentials for movement preparation and execution}, journal = {Brain-Computer Interfaces}, volume = {3}, year = {2016}, month = {May}, pages = {88-102}, abstract = {Determining a person{\textquoteright}s intent, such as the planned direction of their movement, directly from their cortical activity could support important applications such as brain-computer interfaces (BCIs). Continuing development of improved BCI systems requires a better understanding of how the brain prepares for and executes movements. To contribute to this understanding, we recorded surface cortical potentials (electrocorticographic signals; ECoG) in 11 human subjects performing a delayed center-out task to establish the differential role of high gamma activity (HGA) and the local motor potential (LMP) as a function of time and anatomical area during movement preparation and execution. High gamma modulations mostly confirm previous findings of sensorimotor cortex involvement, whereas modulations in LMPs are observed in prefrontal cortices. These modulations include directional information during movement planning as well as execution. Our results suggest that sampling signals from these widely distributed cortical areas improves decoding accuracy.}, keywords = {BCI, brain-computer interfaces, ECoG, Electrocorticography, sensorimotor systems}, doi = {https://doi.org/10.1080/2326263X.2016.1179087}, author = {Gunduz, Aysegul and Peter Brunner and Sharma, Mohit and Leuthardt, Eric C. and Ritaccio, Anthony L. and Pesaran, Bijan and Schalk, Gerwin} } @article {4096, title = {Robust Signal Identification for Dynamic Pattern Classification}, journal = {2016 23rd International Conference on Pattern Recognition}, year = {2016}, month = {Dec}, pages = {3910-3915}, abstract = {This paper addresses the problem of identifying signals of interest from discrete-time sequences contaminated by erroneous segments, which we define as the part of time series whose dynamic patterns are inconsistent with that of the signals. Assuming the signals of interest consist of consecutive samples with arbitrary starting point, duration and following a stationary dynamic pattern, we propose a robust algorithm combining Random Sample Consensus (RANSAC) and Hidden Markov Model (HMM) to automatically identify the start and end of signals of interest from time series. To evaluate the identification quality, we perform a classification task, where the identified signals are used to train a classifier. A majority vote strategy is adopted to handle error contaminated testing sequences. Compared with manual selection approach and other unsupervised learning methods, the proposed method shows improvement in classification accuracy on both synthetic and real Electrocorticographic (ECoG) data.}, keywords = {computational modeling, data models, Hidden Markov models, motion segmentation, robustness, testing, Time series analysis}, doi = {10.1109/ICPR.2016.7900245}, url = {https://ieeexplore.ieee.org/document/7900245/}, author = {Zhao, Rui and Schalk, Gerwin and Ji, Qiang} }