Biology subjects

Schalk, G.

Publications and source records attributed to Schalk, G..

10 recordsLinked to original sources

bioRxiv2026

A Translational Platform for Brain-Computer Interfaces and Adaptive Neuromodulation: Technical Characterization, Long-Term Validation, and Implementation of the CorTec Brain Interchange--BCI2000 Ecosystem

Objective: Adaptive neuromodulation systems and implantable brain-computer interfaces (BCIs) are promising therapies for neurological and psychiatric disorders. However, their broader translation into research and clinical practice remains limited by technological complexity, restricted access to implantable research platforms, and the lack of standardized, reproducible experimental workflows. We therefore aimed to develop and validate an open, general-purpose translational ecosystem that enables rapid development, evaluation, and dissemination of novel neuromodulation and implantable BCI paradigms. Approach: The CorTec Brain Interchange (BIC) implantable neural sensing and stimulation device was integrated with the open-source BCI2000 platform to create a modular, extensible neuromodulation ecosystem. We established a standardized battery of quantitative assessments to characterize implantable neuromodulation systems to comprehensively evaluate the CorTec BIC device through benchtop characterization, long-term preclinical in vitro and in vivo validation, and a human proof-of-concept demonstration. Results: Benchtop and saline testing provided a comprehensive technical ex vivo characterization of the BIC device, independently validating previously reported performance while extending its characterization through quantification of the recording noise floor, stimulation and acquisition latencies and impedance measurement accuracy. Long-term in vivo validation in five canines, with the longest implantation exceeding three years, demonstrated stable chronic recordings while capturing progressive channel deterioration and its underlying mechanical causes. The ecosystem enabled active functional decoding more than two years after implantation, implementation of closed-loop stimulation using arbitrary spectral biomarkers, detection and modulation of epilepsy-associated biomarkers, and brain stimulation evoked potential recordings. In addition, we translated an established one-dimensional BCI cursor control paradigm to the BIC benchtop evaluation kit and demonstrated its feasibility in a human participant. Finally, we openly provide standardized surgical, imaging, and analysis pipelines together with datasets and software to facilitate reproducible neuromodulation research. Significance: We present a versatile, open-source translational ecosystem that supports a wide range of neuromodulation and implantable BCI applications with minimal modification. This battery of quantitative assessments can be applied generally as a blueprint for systematic characterization of implantable neuromodulation systems. By combining comprehensive hardware characterization with standardized software tools and experimental workflows, this work provides both an essential reference for researchers adopting the Brain Interchange platform. The ecosystem lowers technical barriers to implantable neurotechnology research, promotes reproducibility, and provides a foundation for accelerating the development and clinical translation of next-generation adaptive neuromodulation and implantable BCI therapies for patients with neurological and psychiatric disorders.

bioengineering↗

bioRxiv2024

Feasibility of Stereo EEG Based Brain Computer Interfacing in An Adult and Pediatric Cohort

IntroductionStereoelectroencephalography (sEEG) is a mesoscale intracranial monitoring method which records from the brain volumetrically with depth electrodes. Implementation of sEEG in BCI has not been well-described across a diverse patient cohort. MethodsAcross eighteen subjects, channels with high frequency broadband (HFB, 65-115Hz) power increases during hand, tongue, or foot movements during a motor screening task were provided real-time feedback based on these HFB power changes to control a cursor on a screen. ResultsSeventeen subjects established successful control of the overt motor BCI, but only nine were able to control imagery BCI with[≥] 80% accuracy. In successful imagery BCI, HFB power in the two target conditions separated into distinct subpopulations, which appear to engage unique subnetworks of the motor cortex compared to cued movement or imagery alone. ConclusionsEEG-based motor BCI utilizing overt movement and kinesthetic imagery is robust across patient ages and cortical regions with substantial differences in learning proficiency between real or imagined movement.

bioengineering↗

bioRxiv2024

Bred for affection: The canine anterior ectosylvian gyrus responds selectively to social reinforcement

Studying mammalian brain function aids our understanding of human brain evolution. We implanted a beagle with a prototype human neuromodulation platform that measures activity from the brain surface. One year later, a set of simple sensory tasks was performed, finding visual and somatosensory representation in the canine homologs of the expected areas in humans. Surprisingly, the canine anterior ectosylvian gyrus, which is anatomically homologous to human receptive speech areas, was selectively active during independent social reinforcement tasks. This suggests that human speech understanding may have evolved from more general mammalian brain structures that are specialized for social reinforcement.

animal behavior and cognition↗

bioRxiv2024

Polaris Neurosense: A General-Purpose Non-invasive Neurotechnology Research Platform

This article describes our initial work toward a general-purpose platform for non-invasive neurotechnology research. This platform consists of a multi-modal wireless recording device, an associated software API, and full integration into BCI2000 software. The device is placed on the forehead and features two electroencephalographic (EEG) sensors, an inertial movement sensor (IMU), a photoplethysmogram (PPG) sensor, a microphone, and vibration-based feedback. Herein, we demonstrate different technical characteristics of our platform and its use in the context of sleep monitoring/modulation, simultaneous and synchronized recordings from different hardware, and evoked potentials. With further development and widespread dissemination, our platform could become an important tool for research into new non-invasive neurotechnology protocols in humans.

bioengineering↗

bioRxiv2023

Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods

Precise electrode localization is important for maximizing the utility of intracranial EEG data. Electrodes are typically localized from post-implantation CT artifacts, but algorithms can fail due to low signal-to-noise ratio, unrelated artifacts, or high-density electrode arrays. Minimizing these errors usually requires time-consuming visual localization and can still result in inaccurate localizations. In addition, surgical implantation of grids and strips typically introduces non-linear brain deformations, which result in anatomical registration errors when post-implantation CT images are fused with the pre-implantation MRI images. Several projection methods are currently available, but they either fail to produce smooth solutions or do not account for brain deformations. To address these shortcomings, we propose two novel algorithms for the anatomical registration of intracranial electrodes that are almost fully automatic and provide highly accurate results. We first present GridFit, an algorithm that simultaneously localizes all contacts in grids, strips, or depth arrays by fitting flexible models to the electrodes CT artifacts. We observed localization errors of less than one millimeter (below 8% relative to the inter-electrode distance) and robust performance under the presence of noise, unrelated artifacts, and high-density implants when we ran [~]6000 simulated scenarios. Furthermore, we validated the method with real data from 20 intracranial patients. As a second registration step, we introduce CEPA, a brain-shift compensation algorithm that combines orthogonal-based projections, spring-mesh models, and spatial regularization constraints. When tested with real data from 15 patients, anatomical registration errors were smaller than those obtained for well-established alternatives. Additionally, CEPA accounted simultaneously for simple mechanical deformation principles, which is not possible with other available methods. Inter-electrode distances of projected coordinates smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance. Moreover, in an additional validation procedure, we found that modeling resting-state high-frequency activity (75-145 Hz) in five patients further supported our new algorithm. Together, GridFit and CEPA constitute a versatile set of tools for the registration of subdural grid, strip, and depth electrode coordinates that provide highly accurate results even in the most challenging implantation scenarios. The methods presented here are implemented in the iElectrodes open-source toolbox, making their use simple, accessible, and straightforward to integrate with other popular toolboxes used for analyzing electrophysiological data.

bioinformatics↗

bioRxiv2022

Homunculus Interruptus: A motor association area in the depth of the central sulcus

Cells in the precentral gyrus of the human brain directly send signals to the periphery to generate movement and are topologically organized as a map of the body. We find that movement induced electrophysiological changes from implanted depth electrodes extend this map 3-dimensionally throughout the volume of the gyrus. Unexpectedly, this organization is interrupted by a motor association area in the depths of the central sulcus at its mid-lateral aspect that is active during many different types of movements from both sides of the body.

neuroscience↗

bioRxiv2022

Encoding and decoding analysis of music perception using intracranial EEG

Music perception engages multiple brain regions, however the neural dynamics of this core human experience remains elusive. We applied predictive models to intracranial EEG data from 29 patients listening to a Pink Floyd song. We investigated the relationship between the song spectrogram and the elicited high-frequency activity (70-150Hz), a marker of local neural activity. Encoding models characterized the spectrotemporal receptive fields (STRFs) of each electrode and decoding models estimated the population-level song representation. Both methods confirmed a crucial role of the right superior temporal gyri (STG) in music perception. A component analysis on STRF coefficients highlighted overlapping neural populations tuned to specific musical elements (vocals, lead guitar, rhythm). An ablation analysis on decoding models revealed the presence of unique musical information concentrated in the right STG and more spatially distributed in the left hemisphere. Lastly, we provided the first song reconstruction decoded from human neural activity.

neuroscience↗

bioRxiv2021

Revealing the Physiological Origin of Event-Related Potentials using Electrocorticography in Humans

The scientific and clinical value of event-related potentials (ERPs) depends on understanding the contributions to them of three possible mechanisms: (1) additivity of time-locked voltage changes; (2) phase resetting of ongoing oscillations; (3) asymmetrical oscillatory activity. Their relative contributions are currently uncertain. This study uses analysis of human electrocorticographic activity to quantify the origins of movement-related potentials (MRPs) and auditory evoked potentials (AEPs). The results show that MRPs are generated primarily by endogenous additivity (88%). In contrast, P1 and N1 components of AEPs are generated almost entirely by exogenous phase reset (93%). Oscillatory asymmetry contributes very little. By clarifying ERP mechanisms, these results enable creation of ERP models; and they enhance the value of ERPs for understanding the genesis of normal and abnormal auditory or sensorimotor behaviors.

neuroscience↗

bioRxiv2019

Unexpected sound omissions are signaled in human posterior superior temporal gyrus: an intracranial study

Context modulates sensory neural activations enhancing perceptual and behavioral performance and reducing prediction errors. However, the mechanism of when and where these high-level expectations act on sensory processing is unclear. Here, we isolate the effect of expectation absent any auditory evoked activity by assessing the response to omitted expected sounds. Electrophysiological signals were recorded directly from the superior temporal gyrus (STG) and superior temporal sulcus (STS) in patients with medically refractory epilepsy. Subjects listened to a predictable sequence of syllables, with some infrequently omitted. We found a high frequency band (HFB, 70-150Hz) response to omissions, which overlap with a posterior subset of auditory active electrodes. This response is distinct from omission activations observed in non-auditory selective sites in STG. Heard syllables could be classified reliably from STG, but not the identity of the omitted stimulus. Both omission- and target detection activations were also observed in prefrontal cortex.\n\nWe propose that the posterior STG and STS are central for implementing predictions in the auditory environment. HFB omission activations in this region appear to index mismatch-signaling or salience detection processes.

neuroscience↗

bioRxiv2019

Intracranial recordings from human auditory cortex reveal a neural population selective for musical song

How are neural representations of music organized in the human brain? While neuroimaging has suggested some segregation between responses to music and other sounds, it remains unclear whether finer-grained organization exists within the domain of music. To address this question, we measured cortical responses to natural sounds using intracranial recordings from human patients and inferred canonical response components using a data-driven decomposition algorithm. The inferred components replicated many prior findings including distinct neural selectivity for speech and music. Our key novel finding is that one component responded nearly exclusively to music with singing. Song selectivity was not explainable by standard acoustic features and was co-located with speech- and music-selective responses in the middle and anterior superior temporal gyrus. These results suggest that neural representations of music are fractionated into subpopulations selective for different types of music, at least one of which is specialized for the analysis of song.

neuroscience↗