ECoG Signals Research Articles

Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics.

Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/f noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation's fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.

Brain to 3D Model (Enabling tech of 3D Printers)

This study was planned to conduct and to bring the novel approach of thoughts into 3D into reality by using AI techniques & Methods. The study was conducted on user EEG signals and processed into pre-trained tokens of NLP and EEG signals to identify the probability of the EEG signal approach. The translation of EEG and ECoG signals into NLP level and generate a better prompting to Solve the issue hidden in Tokens of Prompting. These Prompt Techniques produce Generative Art by using Stable diffusion to create an image. That 2 Dimension image has Raw data at the background which is removed by using u2net & ModNet to introduce a more refined format as a preprocess for 3-D artwork Generation. The study focused on using different model like shape-e by OpenAI to create a more general approach but our code used it to produce for specifically 3-D avatar character for Metaverse. Using PifuHD estimation model by Facebook and lightweight human pose estimation.3-D art produced a more refined artwork as 3-D object file that can later be used in terms of producing into 3-D printed stuff and enable 3-D Printers closer to User Access as an efficient way of Technology.

The neurophysiological effect of mild hypothermia in gyrencephalic brains submitted to ischemic stroke and spreading depolarizations.

Characterize the neurophysiological effects of mild hypothermia on stroke and spreading depolarizations (SDs) in gyrencephalic brains. Left middle cerebral arteries (MCAs) of six hypothermic and six normothermic pigs were permanently occluded (MCAo). Hypothermia began 1 h after MCAo and continued throughout the experiment. ECoG signals from both frontoparietal cortices were recorded. Five-minute ECoG epochs were collected 5 min before, at 5 min, 4, 8, 12, and 16 h after MCAo, and before, during, and after SDs. Power spectra were decomposed into fast (alpha, beta, and gamma) and slow (delta and theta) frequency bands. In the vascular insulted hemisphere under normothermia, electrodes near the ischemic core exhibited power decay across all frequency bands at 5 min and the 4th hour after MCAo. The same pattern was registered in the two furthest electrodes at the 12th and 16th hour. When mild hypothermia was applied in the vascular insulted hemispheres, the power decay was generalized and seen even in electrodes with uncompromised blood flow. During SD analysis, hypothermia maintained increased delta and beta power during the three phases of SDs in the furthest electrode from the ischemic core, followed by the second furthest and third electrode in the beta band during preSD and postSD segments. However, in hypothermic conditions, the third electrode showed lower delta, theta, and alpha power. Mild hypothermia attenuates all frequency bands in the vascularly compromised hemisphere, irrespective of the cortical location. During SD formation, it preserves power spectra more significantly in electrodes further from the ischemic core.

ECoG spiking activity and signal dimension are early predictive measures of epileptogenesis in a translational mouse model of traumatic brain injury

The latency between traumatic brain injury (TBI) and the onset of epilepsy (PTE) represents an opportunity for counteracting epileptogenesis. Antiepileptogenesis trials are hampered by the lack of sensitive biomarkers that allow to enrich patient's population at-risk for PTE. We aimed to assess whether specific ECoG signals predict PTE in a clinically relevant mouse model with ∼60% epilepsy incidence. TBI was provoked in adult CD1 male mice by controlled cortical impact on the left parieto-temporal cortex, then mice were implanted with two perilesional cortical screw electrodes and two similar electrodes in the hemisphere contralateral to the lesion site. Acute seizures and spikes/sharp waves were ECoG-recorded during 1 week post-TBI. These early ECoG events were analyzed according to PTE incidence as assessed by measuring spontaneous recurrent seizures (SRS) at 5 months post-TBI. We found that incidence, number and duration of acute seizures during 3 days post-TBI were similar in PTE mice and mice not developing epilepsy (No SRS mice). Control mice with cortical electrodes (naïve, n = 5) or with electrodes and craniotomy (sham, n = 5) exhibited acute seizures but did not develop epilepsy. The daily number of spikes/sharp waves at the perilesional electrodes was increased similarly in PTE (n = 15) and No SRS (n = 8) mice vs controls (p < 0.05, n = 10) from day 2 post-injury. Differently, the daily number of spikes/sharp waves at both contralateral electrodes showed a progressive increase in PTE mice vs No SRS and control mice. In particular, spikes number was higher in PTE vs No SRS mice (p < 0.05) at 6 and 7 days post-TBI, and this measure predicted epilepsy development with high accuracy (AUC = 0.77, p = 0.03; CI 0.5830–0.9670). The cut-off value was validated in an independent cohort of TBI mice (n = 12). The daily spike number at the contralateral electrodes showed a circadian distribution in PTE mice which was not observed in No SRS mice. Analysis of non-linear dynamics at each electrode site showed changes in dimensionality during 4 days post-TBI. This measure yielded the best discrimination between PTE and No SRS mice (p < 0.01) at the cortical electrodes contralateral to injury. Data show that epileptiform activity contralateral to the lesion site has the the highest predictive value for PTE in this model reinforcing the hypothesis that the hemisphere contralateral to the lesion core may drive epileptogenic networks after TBI.

Phase-amplitude coupling-based adaptive filters for neural signal decoding.

Bandpass filters play a core role in ECoG signal processing. Commonly used frequency bands such as alpha, beta, and gamma bands can reflect the normal rhythm of the brain. However, the universally predefined bands might not be optimal for a specific task. Especially the gamma band usually covers a wide frequency span (i.e., 30-200 Hz) which can be too coarse to capture features that appear in narrow bands. An ideal option is to find the optimal frequency bands for specific tasks in real-time and dynamically. To tackle this problem, we propose an adaptive band filter that selects the useful frequency band in a data-driven way. Specifically, we leverage the phase-amplitude coupling (PAC) of the coupled working mechanism of synchronizing neuron and pyramidal neurons in neuronal oscillations, in which the phase of slower oscillations modulates the amplitude of faster ones, to help locate the fine frequency bands from the gamma range, in a task-specific and individual-specific way. Thus, the information can be more precisely extracted from ECoG signals to improve neural decoding performance. Based on this, an end-to-end decoder (PACNet) is proposed to construct a neural decoding application with adaptive filter banks in a uniform framework. Experiments show that PACNet can improve neural decoding performance universally with different tasks.

Regulation of arousal and performance of a healthy non-human primate using closed-loop central thalamic deep brain stimulation.

Application of closed-loop approaches in systems neuroscience and brain-computer interfaces holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation strategies to restore lost function. The anterior forebrain mesocircuit (AFM) of the mammalian brain is hypothesized to underlie arousal regulation of the cortex and striatum, and support cognitive functions during wakefulness. Dysfunction of arousal regulation is hypothesized to contribute to cognitive dysfunctions in various neurological disorders, and most prominently in patients following traumatic brain injury (TBI). Several clinical studies have explored the use of daily central thalamic deep brain stimulation (CT-DBS) within the AFM to restore consciousness and executive attention in TBI patients. In this study, we explored the use of closed-loop CT-DBS in order to episodically regulate arousal of the AFM of a healthy non-human primate (NHP) with the goal of restoring behavioral performance. We used pupillometry and near real-time analysis of ECoG signals to episodically initiate closed-loop CT-DBS and here we report on our ability to enhance arousal and restore the animal's performance. The initial computer based approach was then experimentally validated using a customized clinical-grade DBS device, the DyNeuMo-X, a bi-directional research platform used for rapidly testing closed-loop DBS. The successful implementation of the DyNeuMo-X in a healthy NHP supports ongoing clinical trials employing the internal DyNeuMo system (NCT05437393, NCT05197816) and our goal of developing and accelerating the deployment of novel neuromodulation approaches to treat cognitive dysfunction in patients with structural brain injuries and other etiologies.

Movement decoding using spatio-spectral features of cortical and subcortical local field potentials

The first commercially sensing enabled deep brain stimulation (DBS) devices for the treatment of movement disorders have recently become available. In the future, such devices could leverage machine learning based brain signal decoding strategies to individualize and adapt therapy in real-time. As multi-channel recordings become available, spatial information may provide an additional advantage for informing machine learning models. To investigate this concept, we compared decoding performances from single channels vs. spatial filtering techniques using intracerebral multitarget electrophysiology in Parkinson's disease patients undergoing DBS implantation. We investigated the feasibility of spatial filtering in invasive neurophysiology and the putative utility of combined cortical ECoG and subthalamic local field potential signals for decoding grip-force, a well-defined and continuous motor readout. We found that adding spatial information to the model can improve decoding (6% gain in decoding), but the spatial patterns and additional benefit was highly individual. Beyond decoding performance results, spatial filters and patterns can be used to obtain meaningful neurophysiological information about the brain networks involved in target behavior. Our results highlight the importance of individualized approaches for brain signal decoding, for which multielectrode recordings and spatial filtering can improve precision medicine approaches for clinical brain computer interfaces.

REM Sleep Rebound Following Acute Intermittent Hypoxia in Freely Behaving Rats

Core components of the circadian clock and molecules involved in neuroplasticity elicited by acute intermittent hypoxia (AIH) exhibit 24‐hour rhythms in gene expression in the phrenic/diaphragm motor system. Ongoing investigations of time‐of‐day effects on AIH‐induced motor plasticity must account for changes in time spent across vigilance states since sleep architecture profoundly influences other biological rhythms across the rest‐active cycle. Previous investigations from our group reported a marginal increase in wakefulness during the AIH exposure in unanesthetized rats (10, 5‐min hypoxic episodes; 5 min intervals). Following AIH, we also observed a prolonged increase in slow‐wave sleep lasting hours, but changes in REM were not assessed (Terada et al., J. Appl. Physiol., 2011). Given the significant effects disruptions of the sleep‐wake cycle can have on daily biological rhythms, further characterization of sleep architecture following AIH is needed. In this preliminary analysis, from an extensive study of diaphragm long‐term facilitation (LTF) in rats at opposite stages of the diurnal cycle (mid‐active vs. mid‐rest), we added second set of intracortical EEG leads (ECoG) not used by Terada et al. (2011) to enhance detection of hippocampal theta bursts and sleep spindling. In 8 unanesthetized, instrumented rats exposed to AIH, ECoG, and nuchal EMG signals acquired via radiotelemetry were analyzed in 10‐second epochs using Fast Fourier transformation spectral analysis. Power spectra were averaged in frequency bands: delta (1.5‐4.0Hz), theta (4.0‐10.0Hz), beta (10.0‐30.0 Hz), and sigma (10.0‐14.0Hz). We assessed vigilance state at baseline, during AIH, and 90 minutes post‐AIH; there were significant effects of AIH protocol period (p &lt; 0.05) and sleep state (p&lt;0.001). There were marginal, non‐significant trends towards increased active wakefulness during AIH, with decreased quiet wakefulness, slow‐wave sleep, and REM. Time spent in REM sleep rebounded to levels nearly two‐fold above baseline at 60 and 90 min post‐AIH; there were non‐significant trends towards reduced quiet wakefulness without apparent change in NREM sleep post‐AIH. In this preliminary analysis, we report an acute AIH protocol (3, 5 min hypoxic episodes, 13% O2; 5‐min normoxic intervals) has minimal impact on NREM sleep during or following AIH but increases REM post‐AIH. Additional analyses will be performed during and following AIH during the mid‐active and mid‐rest phases, using the 5‐min AIH protocol and a modified protocol with similar cumulative duration of hypoxia (15, 1 min hypoxic episodes; 1 min intervals) often used in human studies of repetitive AIH as a neurotherapeutic modality to improve breathing and non‐respiratory motor function in humans living with chronic spinal cord injury and ALS. REM sleep is essential for mechanisms of neuroplasticity in neural networks controlling diverse physiological systems. These results warrant future research to delineate the influence REM may play in initiating and maintaining memory in the respiratory system.

Multimodal pathophysiological dataset of gradual cerebral ischemia in a cohort of juvenile pigs

Ischemic brain injuries are frequent and difficult to detect reliably or early. We present the multi-modal data set containing cardiovascular (blood pressure, blood flow, electrocardiogram) and brain electrical activities to derive electroencephalogram (EEG) biomarkers of corticothalamic communication under normal, sedation, and hypoxic/ischemic conditions with ensuing recovery. We provide technical validation using EEGLAB. We also delineate the corresponding changes in the electrocardiogram (ECG)-derived heart rate variability (HRV) with the potential for future in-depth analyses of joint EEG-ECG dynamics. We review an open-source methodology to derive signatures of coupling between the ECoG and electrothalamogram (EThG) signals contained in the presented data set to better characterize the dynamics of thalamocortical communication during these clinically relevant states. The data set is presented in full band sampled at 2000 Hz, so the additional potential exists for insights from the full-band EEG and high-frequency oscillations under the bespoke experimental conditions. Future studies on the dataset may contribute to the development of new brain monitoring technologies, which will facilitate the prevention of neurological injuries.

Electrophysiological Mapping During Brain Tumor Surgery: Recording Cortical Potentials Evoked Locally, Subcortically and Remotely by Electrical Stimulation to Assess the Brain Connectivity On-line

Direct electrical stimulation (DES) is used to perform functional brain mapping during awake surgery and in epileptic patients. DES may be coupled with the measurement of Evoked Potentials (EP) to study the conductive and integrative properties of activated neural ensembles and probe the spatiotemporal dynamics of short- and long-range networks. However, its electrophysiological effects remain by far unknown. We recorded ECoG signals on two patients undergoing awake brain surgery and measured EP on functional sites after cortical stimulations and were the firsts to record three different types of EP on the same patients. Using low-intensity (1-3mA) to evoke electrogenesis we observed that: (i) "true" remote EPs are attenuated in amplitude and delayed in time due to the divergence of white matter pathways; (ii) "false" remote EPs are attenuated but not delayed: as they originate from the same electrical source; (iii) Singular but reproducible positive components in the EP can be generated when the DES is applied in the temporal lobe or the premotor cortex; and (iv) rare EP can be triggered when the DES is applied subcortically: these can be either negative, or surprisingly, positive. We proposed different activation and electrophysiological propagation mechanisms following DES, based on the nature of activated neural elements and discussed important methodological pitfalls when measuring EP in the brain. Altogether, these results pave the way to map the connectivity in real-time between the DES and the recording sites; to characterize the local electrophysiological states and to link electrophysiology and function. In the future, and in practice, this technique could be used to perform electrophysiological mapping in order to link (non)-functional to electrophysiological responses with DES and could be used to guide the surgical act itself.

Efficient MAC Protocols for Brain Computer Interface Applications

Brain computer interface (BCI) systems permit individuals with motor disorders to utilize their thoughts as a mean to control external devices. BCI is a promising interdisciplinary field that gained the attention of many researchers. Yet, the development of BCI systems is facing several challenges, such as network lifetime. The Medium Access Control (MAC) Protocol is the bottle- neck of network reliability. There are many MAC protocols that can be utilized for dependable transmission in BCI applications by altering their control parameters. However, modifying these parameters is another source of concern due to the scarcity in knowledge about the effect of modification. Also, there is still no instrument that can receive and actualize these parameters on transmitters embedded inside the cerebrum. In this paper, we propose two novel MAC protocols using passive UHF-RFID, the proposed protocols provide efficient and reliable communication between the transmitters and the receiver. The UHF-RFID transmitters were used because they are energy efficient which makes them compatible with BCI application. The first protocol is designed for the EEG signals. While the second protocol was designed for the ECoG signals. The evaluation results showed the validity of the proposed protocols in terms of network performance. The results also proved that the protocols are suitable and reliable for designing efficient BCI applications.

A Novel Idea of Imagine &amp; Real Speech Decoding Model Using Electrocardiogram to Enhance BCI

Many researchers have tried to decode talked and guessed speech instantly from brain signals towards the growth of a raw-speech BCI. This paper intends to feature extraction and decoding, using the electrocorticoscope (ECoG), the auditory and articulatory features of the motor cortex. Consonants were selected as auditory depictions, and both positions of articulation and manners of articulation were selected as articulatory depictions. The auditory and articulatory representations were decoded at different time lags concerning the speech onset to find optimal temporal decoding parameters. Moreover, this work explores the role of the temporal lobe during speech production directly from ECoG signals. Also, their temporal propagation before and after the speech onset was performed using classification and statistical tests. A novel decoding model using temporal lobe activity was developed to predict a spectral representation of the speech envelope during speech production. Deep learning was utilized in our analysis. This new knowledge may be used to enhance existing speech-based BCI systems, which will offer a more natural communication modality. Also, the work contributes to the field of speech neurophysiology by providing a better understanding of speech processes in the brain.

Patterns of cortical interactivity supporting speech production and lexical retrieval: A graph signal processing approach at the individual level

Abstract Although seemingly easy, speech production is a complex action involving several processes and brain regions and is often studied through the use of picture naming paradigms. Several brain regions have been identified as being involved in speech production and lexical retrieval, including several areas of the left lateral, ventral, and medial temporal cortex, and of the left lateral and medial prefrontal cortex (PFC). However, little is known about how these regions interact to allow us to produce and retrieve words so efficiently as we speak. In this study, we used graph signal processing, and in particular graph learning, to analyze intracranial electroencephalography data recorded directly at the cortical surface (i.e., electrocorticography, ECoG) in the blocked cyclic picture naming task (recently reported in Ries et al., 2017). In this task, pictures are named several times in semantically homogeneous (HOM) versus heterogeneous (HET) blocks. Lexical retrieval, and in particular our ability to select words from competing alternatives, is hampered in HOM vs. HET blocks with increasing repetitions of the pictures, an effect referred to as the semantic interference effect. The method we used can infer the connectivity between different brain regions from the recorded ECoG signals through an optimization process that considers the entire connectivity map instead of pair-wise methods such as more commonly used correlation measures. Our results show that distant left frontal and temporal brain regions as well as PFC regions are functionally connected in picture naming, and that connection strength is variable between pairs of brain regions and between participants. Semantic interference on naming latencies was not present in all participants and varied in terms of when naming latencies became slower in HOM versus HET blocks. Connection weights were generally sensitive to semantic context in the participants showing a semantic interference effect on behavior but in highly variable ways across participants and in different region pairs. In sum, our results are consistent with the hypotheses that left frontal and temporal regions are functionally connected during picture naming and that intra-frontal connections seem particularly important in this paradigm manipulating lexical retrieval difficulty. Further research is needed to explore the cortical interactions supporting lexical retrieval.

A state-based probabilistic method for decoding hand position during movement from ECoG signals in non-human primate

Objective. In this study, we proposed a state-based probabilistic method for decoding hand positions during unilateral and bilateral movements using the ECoG signals recorded from the brain of Rhesus monkey. Approach. A customized electrode array was implanted subdurally in the right hemisphere of the brain covering from the primary motor cortex to the frontal cortex. Three different experimental paradigms were considered: ipsilateral, contralateral, and bilateral movements. During unilateral movement, the monkey was trained to get food with one hand, while during bilateral movement, the monkey used its left and right hands alternately to get food. To estimate the hand positions, a state-based probabilistic method was introduced which was based on the conditional probability of the hand movement state (i.e. idle, right hand movement, and left hand movement) and the conditional expectation of the hand position for each state. Moreover, a hybrid feature extraction method based on linear discriminant analysis and partial least squares (PLS) was introduced. Main results. The proposed method could successfully decode the hand positions during ipsilateral, contralateral, and bilateral movements and significantly improved the decoding performance compared to the conventional Kalman and PLS regression methods . The proposed hybrid feature extraction method was found to outperform both the PLS and PCA methods . Investigating the kinematic information of each frequency band shows that more informative frequency bands were (15–30 Hz) and (50–100 Hz) for ipsilateral and and (100–200 Hz) for contralateral movements. It is observed that ipsilateral movement was decoded better than contralateral movement for (5–15 Hz) and bands, while contralateral movements was decoded better for (30–200 Hz) and hfECoG (200–400 Hz) bands. Significance. Accurate decoding the bilateral movement using the ECoG recorded from one brain hemisphere is an important issue toward real-life applications of the brain-machine interface technologies.

Кросскорреляционный и когерентный анализ электрокортикограмм крыс, перенесших черепно-мозговую травму

Traumatic brain injury (TBI) is a leading cause of disability and mortality among young population both in Russian Federation and abroad. The development of adequate and reproducible models of TBI in laboratory animals as well as precise methods for assessment of neurological disorders degree will optimize investigation of new effective neuroprotective drugs. Aim of the study: to perform the comparative crosscorrelation and coherent analysis of ECOG signal in non-injured rats and rats with TBI rats. The open penetrating brain injury was modeled following the craniotomy over the motor cortex of the left hemisphere by controlled cortical impact injury method (CCI). The recording nichrome ECoG electrodes were implanted bilaterally in the primary and secondary motor cortex areas and also in the area of primary somatosensory cortex (over the hippocampus). ECoG recordings were performed on the 3rd and 7th day after the operation in condition of the home cage at resting state. Crosscorrelation analysis comprised the calculation of crosscorrelation coefficient, mean frequency and maximum span of crosscorrelation function (CCF). Also the calculated mean coherence power of delta-, tetha-, alpha- and beta-rhythms. Unilateral traumatic damage of motor cortex and near situated brain areas lead to disturbances of inter- and intrahemispheric connections, these changes are recorded not only in the damaged area, but also in the remote areas of the cortex on the 3rd and 7th day after the injury.

Longitudinal Functional Assessment of Brain Injury Induced by High-Intensity Ultrasound Pulse Sequences

Exposure of the brain to high-intensity stress waves creates the potential for long-term functional deficits not related to thermal or cavitational damage. Possible sources of such exposure include overpressure from blast explosions or high-intensity focused ultrasound (HIFU). While current ultrasound clinical protocols do not normally produce long-term neurological deficits, the rapid expansion of potential therapeutic applications and ultrasound pulse-train protocols highlights the importance of establishing a safety envelope beyond which therapeutic ultrasound can cause neurological deficits not detectable by standard histological assessment for thermal and cavitational damage. In this study, we assessed the neuroinflammatory response, behavioral effects, and brain micro-electrocorticographic (µECoG) signals in mice following exposure to a train of transcranial pulses above normal clinical parameters. We found that the HIFU exposure induced a mild regional neuroinflammation not localized to the primary focal site, and impaired locomotor and exploratory behavior for up to 1 month post-exposure. In addition, low frequency (δ) and high frequency (β, γ) oscillations recorded by ECoG were altered at acute and chronic time points following HIFU application. ECoG signal changes on the hemisphere ipsilateral to HIFU exposure are of greater magnitude than the contralateral hemisphere, and persist for up to three months. These results are useful for describing the upper limit of transcranial ultrasound protocols, and the neurological sequelae of injury induced by high-intensity stress waves.

Attenuation and Delay of Remote Potentials Evoked by Direct Electrical Stimulation During Brain Surgery.

Direct electrical stimulation (DES) is used to perform functional brain mapping during awake surgery but its electrophysiological effects remain by far unknown. DES may be coupled with the measurement of evoked potentials (EPs) to study the conductive and integrative properties of activated neural ensembles and probe the spatiotemporal dynamics of short- and long-range networks. We recorded ECoG signals on two patients undergoing awake brain surgery and measured EPs on functional sites after cortical stimulations, using combinations of stimulation parameters. EPs were similar in shape but delayed in time and attenuated in amplitude when elicited from a different gyrus or remotely from the recording site. We were able to trigger remote EPs using low stimulation intensities. We propose different activation and electrophysiological propagation mechanisms following DES based on activated neural elements.

Long-Term Sheep Implantation of WIMAGINE®, a Wireless 64-Channel Electrocorticogram Recorder.

This article deals with the long-term preclinical validation of WIMAGINE® (Wireless Implantable Multi-channel Acquisition system for Generic Interface with Neurons), a 64-channel wireless implantable recorder that measures the electrical activity at the cortical surface (electrocorticography, ECoG). The WIMAGINE® implant was designed for chronic wireless neuronal signal acquisition, to be used e.g., as an intracranial Brain–Computer Interface (BCI) for severely motor-impaired patients. Due to the size and shape of WIMAGINE®, sheep appeared to be the best animal model on which to carry out long-term in vivo validation. The devices were implanted in two sheep for a follow-up period of 10 months, including idle state cortical recordings and Somato-Sensory Evoked Potential (SSEP) sessions. ECoG and SSEP demonstrated relatively stable behavior during the 10-month observation period. Information recorded from the SensoriMotor Cortex (SMC) showed an SSEP phase reversal, indicating the cortical site of the sensorimotor activity was retained after 10 months of contact. Based on weekly recordings of raw ECoG signals, the effective bandwidth was in the range of 230 Hz for both animals and remarkably stable over time, meaning preservation of the high frequency bands valuable for decoding of the brain activity using BCIs. The power spectral density (in dB/Hz), on a log scale, was of the order of 2.2, –4.5 and –18 for the frequency bands (10–40), (40–100), and (100–200) Hz, respectively. The outcome of this preclinical work is the first long-term in vivo validation of the WIMAGINE® implant, highlighting its ability to record the brain electrical activity through the dura mater and to send wireless digitized data to the external base station. Apart from local adhesion of the dura to the skull, the neurosurgeon did not face any difficulty in the implantation of the WIMAGINE® device and post-mortem analysis of the brain revealed no side effect related to the implantation. We also report on the reliability of the system; including the implantable device, the antennas module and the external base station.

Direct Electrical Stimulation in Electrocorticographic Brain-Computer Interfaces: Enabling Technologies for Input to Cortex.

Electrocorticographic brain computer interfaces (ECoG-BCIs) offer tremendous opportunities for restoring function in individuals suffering from neurological damage and for advancing basic neuroscience knowledge. ECoG electrodes are already commonly used clinically for monitoring epilepsy and have greater spatial specificity in recording neuronal activity than techniques such as electroencephalography (EEG). Much work to date in the field has focused on using ECoG signals recorded from cortex as control outputs for driving end effectors. An equally important but less explored application of an ECoG-BCI is directing input into cortex using ECoG electrodes for direct electrical stimulation (DES). Combining DES with ECoG recording enables a truly bidirectional BCI, where information is both read from and written to the brain. We discuss the advantages and opportunities, as well as the barriers and challenges presented by using DES in an ECoG-BCI. In this article, we review ECoG electrodes, the physics and physiology of DES, and the use of electrical stimulation of the brain for the clinical treatment of disorders such as epilepsy and Parkinson’s disease. We briefly discuss some of the translational, regulatory, financial, and ethical concerns regarding ECoG-BCIs. Next, we describe the use of ECoG-based DES for providing sensory feedback and for probing and modifying cortical connectivity. We explore future directions, which may draw on invasive animal studies with penetrating and surface electrodes as well as non-invasive stimulation methods such as transcranial magnetic stimulation (TMS). We conclude by describing enabling technologies, such as smaller ECoG electrodes for more precise targeting of cortical areas, signal processing strategies for simultaneous stimulation and recording, and computational modeling and algorithms for tailoring stimulation to each individual brain.

Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0.

Recordings of extracellular electrical, and later also magnetic, brain signals have been the dominant technique for measuring brain activity for decades. The interpretation of such signals is however nontrivial, as the measured signals result from both local and distant neuronal activity. In volume-conductor theory the extracellular potentials can be calculated from a distance-weighted sum of contributions from transmembrane currents of neurons. Given the same transmembrane currents, the contributions to the magnetic field recorded both inside and outside the brain can also be computed. This allows for the development of computational tools implementing forward models grounded in the biophysics underlying electrical and magnetic measurement modalities. LFPy (LFPy.readthedocs.io) incorporated a well-established scheme for predicting extracellular potentials of individual neurons with arbitrary levels of biological detail. It relies on NEURON (neuron.yale.edu) to compute transmembrane currents of multicompartment neurons which is then used in combination with an electrostatic forward model. Its functionality is now extended to allow for modeling of networks of multicompartment neurons with concurrent calculations of extracellular potentials and current dipole moments. The current dipole moments are then, in combination with suitable volume-conductor head models, used to compute non-invasive measures of neuronal activity, like scalp potentials (electroencephalographic recordings; EEG) and magnetic fields outside the head (magnetoencephalographic recordings; MEG). One such built-in head model is the four-sphere head model incorporating the different electric conductivities of brain, cerebrospinal fluid, skull and scalp. We demonstrate the new functionality of the software by constructing a network of biophysically detailed multicompartment neuron models from the Neocortical Microcircuit Collaboration (NMC) Portal (bbp.epfl.ch/nmc-portal) with corresponding statistics of connections and synapses, and compute in vivo-like extracellular potentials (local field potentials, LFP; electrocorticographical signals, ECoG) and corresponding current dipole moments. From the current dipole moments we estimate corresponding EEG and MEG signals using the four-sphere head model. We also show strong scaling performance of LFPy with different numbers of message-passing interface (MPI) processes, and for different network sizes with different density of connections. The open-source software LFPy is equally suitable for execution on laptops and in parallel on high-performance computing (HPC) facilities and is publicly available on GitHub.com.