Electrocorticogram Signals Research Articles

Electrocorticogram (ECoG): Engineering Approaches and Clinical Challenges for Translational Medicine

AbstractElectrocorticogram (ECoG) is an electrophysiological signal that results from the summation of neuronal activity near the cortical surface. To record ECoG signals, the scalp and skull are surgically opened and electrodes are placed on the cortical surface, either epidurally or subdurally. Owing to its improved spatiotemporal resolution and signal quality compared with electroencephalography, it is widely used to diagnose and treat neurological disorders in clinical settings for several decades, despite the invasiveness of ECoG. Recently, ECoG is applied in research to explore brain functions and connectivity, brain‐computer interfaces, and brain‐machine interfaces. In addition to the need for ECoG in neuroscience research, ECoG devices have advanced in terms of materials, fabrication, and function to overcome the limitations of commercially available ECoG arrays. Here, the conventional use of ECoG in clinical medicine, the new applications of ECoG in basic neuroscience research, and the future challenges in translating recent developments in ECoG devices for clinical use are described.

Investigating cortical complexity and connectivity in rats with schizophrenia.

The above studies indicate that the SCZ animal model has abnormal gamma oscillations and abnormal functional coupling ability of brain regions at the cortical level. However, few researchers have focused on the correlation between brain complexity and connectivity at the cortical level. In order to provide a more accurate representation of brain activity, we studied the complexity of electrocorticogram (ECoG) signals and the information interaction between brain regions in schizophrenic rats, and explored the correlation between brain complexity and connectivity. We collected ECoG signal from SCZ rats. The frequency domain and time domain functional connectivity of SCZ rats were evaluated by magnitude square coherence and mutual information (MI). Permutation entropy (PE) and permutation Lempel-Ziv complexity (PLZC) were used to analyze the complexity of ECoG, and the relationship between them was evaluated. In addition, in order to further understand the causal structure of directional information flow among brain regions, we used phase transfer entropy (PTE) to analyze the effective connectivity of the brain. Firstly, in the high gamma band, the complexity of brain regions in SCZ rats is higher than that in normal rats, and the neuronal activity is irregularity. Secondly, the information integration ability of SCZ rats decreased and the communication of brain network information was hindered at the cortical level. Finally, compared with normal rats, the causal relationship between brain regions of SCZ rats was closer, but the information interaction center was not clear. The above findings suggest that at the cortical level, complexity and connectivity are valid biomarkers for identifying SCZ. This bridges the gap between peak potentials and EEG. This may help to understand the pathophysiological mechanisms at the cortical level in schizophrenics.

Suppression of cortical electrostimulation artifacts using pre-whitening and null projection

Objective. Invasive brain–computer interfaces (BCIs) have shown promise in restoring motor function to those paralyzed by neurological injuries. These systems also have the ability to restore sensation via cortical electrostimulation. Cortical stimulation produces strong artifacts that can obscure neural signals or saturate recording amplifiers. While front-end hardware techniques can alleviate this problem, residual artifacts generally persist and must be suppressed by back-end methods. Approach. We have developed a technique based on pre-whitening and null projection (PWNP) and tested its ability to suppress stimulation artifacts in electroencephalogram (EEG), electrocorticogram (ECoG) and microelectrode array (MEA) signals from five human subjects. Main results. In EEG signals contaminated by narrow-band stimulation artifacts, the PWNP method achieved average artifact suppression between 32 and 34 dB, as measured by an increase in signal-to-interference ratio. In ECoG and MEA signals contaminated by broadband stimulation artifacts, our method suppressed artifacts by 78%–80% and 85%, respectively, as measured by a reduction in interference index. When compared to independent component analysis, which is considered the state-of-the-art technique for artifact suppression, our method achieved superior results, while being significantly easier to implement. Significance. PWNP can potentially act as an efficient method of artifact suppression to enable simultaneous stimulation and recording in bi-directional BCIs to biomimetically restore motor function.

Enhancing DNN Models for EEG/ECoG BCI With a Novel Data-Driven Offline Optimization Method

A better understanding of ElectroEncephaloGraphy (EEG) and ElectroCorticoGram (ECoG) signals would get us closer to comprehending brain functionality, creating new avenues for treating brain abnormalities and developing novel Brain-Computer Interface (BCI)-related applications. Deep Neural Networks (DNN)s have lately been employed with remarkable success to decode EEG/ECoG signals for BCI. However, the optimal architectural/training parameter values in these DNN architectures have yet to receive much attention. In addition, new data-driven optimization methodologies that leverage significant advancements in Machine Learning, such as the Transformer model, have recently been proposed. Because an exhaustive search on all possible architectural/training parameter values of the state-of-the-art DNN model (our baseline model) decoding the motor imagery EEG and finger tension ECoG signals comprising the BCI IV 2a and 4 datasets, respectively, would require prohibitively much time, this paper proposes an offline model-based optimization technique based on the Transformer model for the discovery of the optimal architectural/training parameter values for that model. Our findings indicate that we could pick better values for the baseline model’s architectural/training parameters, enhancing the baseline model’s performance by up to 14.7% in the BCI IV 2a dataset and by up to 61.0% in the BCI IV 4 dataset.

Injectable and tissue-conformable conductive hydrogel for MRI-compatible brain-interfacing electrodes

The development of flexible and stretchable materials has led to advances in implantable bio-integrated electronic devices that can sense physiological signals or deliver electrical stimulation to various organs in the human body. Such devices are particularly useful for neural interfacing systems that monitor neurodegenerative diseases such as Parkinson’s disease or epilepsy in real time. However, coupling current brain-interfacing devices with magnetic resonance imaging (MRI) remains a practical challenge due to resonance frequency variations from inorganic metal-based devices. Thus, organic conductive materials, such as poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS), have recently been considered as promising candidates. Nonetheless, their conformability on curvilinear tissues remains questionable. In this study, we developed an injectable conductive hydrogel (ICH) composed of tyramine-conjugated hyaluronic acid (HATYR) and PEDOT:PSS for MRI-compatible brain-interfacing electrodes. Our ICH produced low impedance around 5 kΩ even under 10 Hz, demonstrating high confidence volumetric capacitance. Due to HATYR’s biocompatibility, histological and cytotoxicity assays showed almost no inflammation and toxicity, respectively; in addition, ICH was able to degrade into 40% of its original volume within four weeks in vivo. An electrocorticogram (ECoG) array was also patternable by syringe injections of ICH on a stretchable and flexible elastomeric substrate layer that conformed to curvy brain tissues and successfully recorded ECoG signals under light stimulation. Furthermore, MRI imaging of implanted devices did not show any artifacts, indicating the potential of the MRI-compatible hydrogel electrodes for advanced ECoG arrays. This study provides a promising solution for MRI-compatible neural electrodes, enabling the advancement of chronic neural interfacing systems for monitoring neurodegenerative diseases.

A multivariate comparison of electroencephalogram and functional magnetic resonance imaging to electrocorticogram using visual object representations in humans.

Today, most neurocognitive studies in humans employ the non-invasive neuroimaging techniques functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG). However, how the data provided by fMRI and EEG relate exactly to the underlying neural activity remains incompletely understood. Here, we aimed to understand the relation between EEG and fMRI data at the level of neural population codes using multivariate pattern analysis. In particular, we assessed whether this relation is affected when we change stimuli or introduce identity-preserving variations to them. For this, we recorded EEG and fMRI data separately from 21 healthy participants while participants viewed everyday objects in different viewing conditions, and then related the data to electrocorticogram (ECoG) data recorded for the same stimulus set from epileptic patients. The comparison of EEG and ECoG data showed that object category signals emerge swiftly in the visual system and can be detected by both EEG and ECoG at similar temporal delays after stimulus onset. The correlation between EEG and ECoG was reduced when object representations tolerant to changes in scale and orientation were considered. The comparison of fMRI and ECoG overall revealed a tighter relationship in occipital than in temporal regions, related to differences in fMRI signal-to-noise ratio. Together, our results reveal a complex relationship between fMRI, EEG, and ECoG signals at the level of population codes that critically depends on the time point after stimulus onset, the region investigated, and the visual contents used.

An electrocorticographic decoder for arm movement for brain–machine interface using an echo state network and Gaussian readout

Brain–machine interface (BMI) studies typically use an electrocorticogram (ECoG) to record neural signals from the surface of the cortex because of the high spatial and temporal resolution and high signal-to-noise levels of the data obtained. However, in certain medical conditions, it may not be possible to place the ECoG electrodes at the target brain regions for BMI. Consequently, developing an ECoG decoder with suitable feature extraction and selection processes is challenging. This study investigated the possibility of a novel ECoG decoder for arm movement BMI. The ECoG signals were recorded from four individuals with intractable epilepsy while imaging and performing a reach-and-grasp movement. We examined the performance of the ECoG decoder using an echo state network and 24 Gaussian readouts within the classification problem paradigm of the arm movement directions. A genetic algorithm was used to optimize the hyperparameters of the ECoG decoder. The ECoG decoder successfully classified 24 arm movement directions in the x–y, x–z, and y–z planes in execution and imagination tasks. The best hit rates were 90.9 ± 5.3 %, 92.6 ± 3.9, and 92.6 ± 4.2 for the x–y, x–z, and y–z planes, respectively. A robot arm control simulation indicated that a real-time movement BMI system could use the novel ECoG decoder. Thus, the echo state network with Gaussian readouts for classification can be a successful ECoG decoder model for motor BMIs.

Long-term stability of the chronic epidural wireless recorder WIMAGINE in tetraplegic patients

Objective. Theevaluation of the long-term stability of ElectroCorticoGram (ECoG) signals isan important scientific question as new implantable recording devices can beused for medical purposes such as Brain-Computer Interface (BCI) or brainmonitoring. Approach. The long-term clinical validation of wireless implantable multi-channel acquisition system for generic interface with neurons (WIMAGINE), a wireless 64-channel epidural ECoG recorder was investigated. The WIMAGINE device was implanted in two quadriplegic patients within the context of a BCI protocol. This study focused on the ECoG signal stability in two patients bilaterally implanted in June 2017 (P1) and in November 2019 (P2). Methods. The ECoG signal was recorded at rest prior to each BCI session resulting in a 32 month and in a 14 month follow-up for P1 and P2 respectively. State-of-the-art signal evaluation metrics such as root mean square (RMS), the band power (BP), the signal to noise ratio (SNR), the effective bandwidth (EBW) and the spectral edge frequency (SEF) were used to evaluate stability of signal over the implantation time course. The time-frequency maps obtained from task-related motor activations were also studied to investigate the long-term selectivity of the electrodes. Main results. Based on temporal linear regressions, we report a limited decrease of the signal average level (RMS), spectral distribution (BP) and SNR, and a remarkable steadiness of the EBW and SEF. Time-frequency maps obtained during motor imagery, showed a high level of discrimination 1 month after surgery and also after 2 years. Conclusions. The WIMAGINE epidural device showed high stability over time. The signal evaluation metrics of two quadriplegic patients during 32 months and 14 months respectively provide strong evidence that this wireless implant is well-suited for long-term ECoG recording. Significance. These findings are relevant for the future of implantable BCIs, and could benefit other patients with spinal cord injury, amyotrophic lateral sclerosis, neuromuscular diseases or drug-resistant epilepsy.

Optimizing channel selection using multi-objective FODPSO for BCI applications

ABSTRACT Brain-computer interfacing (BCI) requires multichannel electroencephalogram (EEG) or electrocorticogram (ECoG) signal acquisition for better performance. The selection of optimum channels that provides the best accuracy is itself a problem to solve because noisy/irrelevant channels can complexify the system and degrade its performance. This paper presents a novel automated model for optimum channel selection for BCI applications using the Fractional Order Darwinian Particle Swarm Optimization (FODPSO). To assess the information content of selected channels, a Support Vector Machine (SVM) classifier is used. The weighted sum of the number of channels and the classification accuracy on validation samples is taken as the fitness value for the FODPSO based binary optimization method (where all the variables are binary). The FODPSO and SVM based algorithm is evaluated on the electrocorticography (ECoG) recordings that have been used in BCI competition III. Dual tree complex wavelet transform (DTCWT) is used for preprocessing of the data and sample entropy is calculated for obtaining the most informative features from the preprocessed data. Eight channels are selected using this algorithm, yielding a classification accuracy of 0.81 for the testing dataset (classifying the ECoG signals of imagined movement of little finger and tongue) that compares favorably with the already reported methods. Experimental results successfully demonstrate that this channel selection algorithm works better both in terms of classification accuracy and in the reduction of the number of required channels.

Linear and non-linear feature extraction from rat electrocorticograms for seizure detection by support vector machine.

Seizures, the main symptom of epilepsy, are provoked due to a neurological disorder that underlies the disease. The accurate detection of seizures is a crucial step in any procedure of treatment. In the present study, electrocorticogram (ECoG) signals were recorded from awake and freely moving animals implanted with cortical electrodes before and after pentylenetetrazol, the chemo-convulsant injection. ECoG signals were segmented into 4-s epochs and labeled. Twenty-four linear and non-linear features were extracted from the time and frequency domains of the ECoG signals. The extracted features either individually or in combinations were fed to an automatic support vector machine (SVM) classification system. SVM classifier was trained with 5min of ictal and non-ictal labeled ECoG signals to build the hyperplane that separates two sets of training signals. Sensitivity, specificity, and accuracy were determined for the testing dataset using the different feature combinations. It has been found that some linear features either individually or in combinations outperform non-linear features in terms of the accuracy for seizure detection. The maximum accuracy achieved by the system was 95.3% and has been obtained only after linear and non-linear features were combined. ECoG signals were classified without pre-processing or removal of artifacts to reduce the required computational time to be suitable for online implementation purposes. This may prove the detection system's robustness and supports its use in online seizure detection protocols.

Printed Stretchable Liquid Metal Electrode Arrays for In Vivo Neural Recording.

The adoption of neural interfacing into neurological diagnosis is severely hampered by the complex, costly, and error-prone manufacturing methods, requiring new fabrication processes and materials for flexible neural interfacing. Here a strategy for fabricating highly stretchable neural electrode arrays based on screen printing of liquid metal conductors onto polydimethylsiloxane substrates is presented. The screen-printed electrode arrays show a resolution of 50µm, which is ideally applicable to neural interfaces. The integration of liquid metal-polymer conductor enables the neural electrode arrays to retain stable electrical properties and compliant mechanical performance under a significant (≈108%) strain. Taking advantage of its high biocompatibility, liquid metal electrode arrays exhibit excellent performance for neurite growth and long-term implantation. The stretchable electrode arrays can spontaneously conformally come in touch with the brain surface, and high-throughput electrocorticogram signals are recorded. Based on stretchable electrode arrays, real-time monitoring of epileptiform activities can be provided at different states of seizure. The method reported here offers a new fabrication strategy to manufacture stretchable neural electrodes, with additional potential utility in diagnostic brain-machine interfaces.

Changes in beta and high-gamma power in resting-state electrocorticogram induced by repetitive transcranial magnetic stimulation of primary motor cortex in unanesthetized macaque monkeys

Repetitive transcranial magnetic stimulation (rTMS) is now widely used as a means of neuromodulation, but the details of the mechanisms by which rTMS works remain unclarified. As a step forward to unveiling the neural phenomena occurring underneath the TMS coil, we conducted an electrophysiological study using awake and unanesthetized monkeys with subdural electrocorticogram (ECoG) electrodes implanted over the primary motor cortex (MI). We evaluated the effects of low-frequency (1 Hz) and high-frequency (10 Hz) rTMS on the resting-state ECoG signals in the stimulated MI, as well as the motor evoked potentials (MEPs) in the contralateral hand. Following the 1-Hz rTMS application, the ECoG beta band power and the MEP amplitude were significantly decreased. Following the 10-Hz rTMS application, the ECoG high-gamma power and the MEP amplitude significantly increased. Given that beta and high-gamma activities in the ECoG reflect the synchronous firing and the firing frequency of cell assemblies, respectively, in local neural circuits, these results suggest that low-frequency rTMS inhibits neural activity by desynchronizing the firing activity of local circuits, whereas high-frequency rTMS facilitates neural activity by increasing the firing rate of cell assemblies in the local circuits.

Electrophysiological and neurochemical evaluation of the adverse effects of REM sleep deprivation and epileptic seizures on rat's brain

AimThe current study aims to investigate the impact of paradoxical (REM) sleep deprivation and/or epileptic seizures on rat's cortical brain tissues. Main methodsAnimals were divided into four groups; control, epileptic, REM sleep deprived and epileptic subjected to REM sleep deprivation. Electrocorticogram (ECoG) signals were recorded and quantitatively analyzed for each group. Concentrations of amino acid neurotransmitters; proinflammatory cytokines; and oxidative stress parameters; and acetylcholinesterase activity were determined in the cortex of the animals in different groups. Key findingsResults showed significant variations in the spectral distribution of ECoG waves in the epilepsy model, 24- and 48-hours of REM sleep deprivation and their combined effects indicating a state of cortical hyperexcitability. Significant increases in NO and taurine and significant decrement in glutamine, GABA and glycine were determined. In REM sleep deprived rats significant elevation in glutamate, aspartate, glycine and taurine and a significant lowering in GABA were obtained. This was accompanied by significant reduction in AchE and IL-β. In the cortical tissue of epileptic rats deprived from REM sleep significant increases in lipid peroxidation, TNF-α, IL-1β, IL-6 and aspartate and a significant reduction in AchE were observed. SignificanceThe present data indicate that REM sleep deprivation induces an increase in lipid peroxidation and storming in proinflammatory cytokines in the cortex of rat model of epilepsy during SRS. These changes are associated with a decreased seizure threshold as inferred from the increase in alpha and Beta waves and a decrease in Delta waves of ECoG.

Consciousness Detection in a Complete Locked-in Syndrome Patient through Multiscale Approach Analysis.

Completely locked-in state (CLIS) patients are unable to speak and have lost all muscle movement. From the external view, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to still be conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is important to find alternative ways to re-establish communication with these patients during periods of awareness, and one such alternative is through a brain–computer interface (BCI). In this study, multiscale-based methods (multiscale sample entropy, multiscale permutation entropy and multiscale Poincaré plots) were applied to analyze electrocorticogram signals from a CLIS patient to detect the underlying consciousness level. Results from these different methods converge to a specific period of awareness of the CLIS patient in question, coinciding with the period during which the CLIS patient is recorded to have communicated with an experimenter. The aim of the investigation is to propose a methodology that could be used to create reliable communication with CLIS patients.

The ferroelectric ceramic/elastomer composite as the dielectric coating of soft capacitive neural interface: The competitive effects of ceramic particles

Capacitive neural interface provides a safer way to record the neural activity with reduced risk of current leakage and improved biocompatibility. The emerging of soft neural interfaces makes it necessary to propose dielectric coating with excellent flexibility and dielectric property for the soft capacitive neural interface. In this study, the feasibility and strategy of using the ferroelectric ceramic/elastomer composite as the dielectric coating in soft capacitive neural interface are discussed. With excellent electrical isolation performance, the device experimentally demonstrates the abilities to record in vivo electrocorticogram signal with comparable quality to the commercial electrode and steady-state visual evoked potential signal with improved signal-to-noise ratio. The roles of the ceramic particles in the performance of soft capacitive neural interface are discussed by evaluating the influences of composite content on not only the permittivity but the wettability and stiffness of the dielectric coating, providing the understanding for using the elastomer-based composite as the dielectric coating of the soft capacitive neural interface in a broader view.

A Swallowing Decoder Based on Deep Transfer Learning: AlexNet Classification of the Intracranial Electrocorticogram.

To realize a brain-machine interface to assist swallowing, neural signal decoding is indispensable. Eight participants with temporal-lobe intracranial electrode implants for epilepsy were asked to swallow during electrocorticogram (ECoG) recording. Raw ECoG signals or certain frequency bands of the ECoG power were converted into images whose vertical axis was electrode number and whose horizontal axis was time in milliseconds, which were used as training data. These data were classified with four labels (Rest, Mouth open, Water injection, and Swallowing). Deep transfer learning was carried out using AlexNet, and power in the high-[Formula: see text] band (75-150[Formula: see text]Hz) was the training set. Accuracy reached 74.01%, sensitivity reached 82.51%, and specificity reached 95.38%. However, using the raw ECoG signals, the accuracy obtained was 76.95%, comparable to that of the high-[Formula: see text] power. We demonstrated that a version of AlexNet pre-trained with visually meaningful images can be used for transfer learning of visually meaningless images made up of ECoG signals. Moreover, we could achieve high decoding accuracy using the raw ECoG signals, allowing us to dispense with the conventional extraction of high-[Formula: see text] power. Thus, the images derived from the raw ECoG signals were equivalent to those derived from the high-[Formula: see text] band for transfer deep learning.

Thermal Analysis of a Skull Implant in Brain-Computer Interfaces.

The goal of this study is to estimate the thermal impact of a titanium skull unit (SU) implanted on the exterior aspect of the human skull. We envision this unit to house the front-end of a fully implantable electrocorticogram (ECoG)-based bi-directional (BD) brain-computer interface (BCI). Starting from the bio-heat transfer equation with physiologically and anatomically constrained tissue parameters, we used the finite element method (FEM) implemented in COMSOL to build a computational model of the SU's thermal impact. Based on our simulations, we predicted that the SU could consume up to 75 mW of power without raising the temperature of surrounding tissues above the safe limits (increase in temperature of 1°C). This power budget by far exceeds the power consumption of our front-end prototypes, suggesting that this design can sustain the SU's ability to record ECoG signals and deliver cortical stimulation. These predictions will be used to further refine the existing SU design and inform the design of future SU prototypes.

An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine

In order to improve the accuracy of brain signal processing and accelerate speed meanwhile, we present an optimal and intelligent method for large dataset classification application in this paper. Optimized Extreme Learning Machine (OELM) is introduced in ElectroCorticoGram (ECoG) feature classification of motor imaginary-based brain-computer interface (BCI) system, with common spatial pattern (CSP) to extract the feature. When comparing it with other conventional classification methods like SVM and ELM, we exploit several metrics to evaluate the performance of all the adopted methods objectively. The accuracy of the proposed BCI system approaches approximately 92.31% when classifying ECoG epochs into left pinky or tongue movement, while the highest accuracy obtained by other methods is no more than 81%, which substantiates that OELM is more efficient than SVM, ELM, etc. Moreover, the simulation results also demonstrate that OELM will significantly improve the performance with p value being far less than 0.001. Hence, the proposed OELM is satisfactory in addressing ECoG signal.

Chronic neural interfacing with cerebral cortex using single-walled carbon nanotube-polymer grids

Objective. The development of electrode arrays able to reliably record brain electrical activity is a critical issue in brain machine interface (BMI) technology. In the present study we undertook a comprehensive physico-chemical, physiological, histological and immunohistochemical characterization of new single-walled carbon nanotubes (SWCNT)-based electrode arrays grafted onto medium-density polyethylene (MD-PE) films. Approach. The long-term electrical stability, flexibility, and biocompatibility of the SWCNT arrays were investigated in vivo in laboratory rats by two-months recording and analysis of subdural electrocorticogram (ECoG). Ex-vivo characterization of a thin flexible and single probe SWCNT/polymer electrode is also provided. Main results. The SWCNT arrays were able to capture high quality and very stable ECoG signals across 8 weeks. The histological and immunohistochemical analyses demonstrated that SWCNT arrays show promising biocompatibility properties and may be used in chronic conditions. The SWCNT-based arrays are flexible and stretchable, providing low electrode-tissue impedance, and, therefore, high compliance with the irregular topography of the cortical surface. Finally, reliable evoked synaptic local field potentials in rat brain slices were recorded using a special SWCNT-polymer-based flexible electrode. Significance. The results demonstrate that the SWCNT arrays grafted in MD-PE are suitable for manufacturing flexible devices for subdural ECoG recording and might represent promising candidates for long-term neural implants for epilepsy monitoring or neuroprosthetic BMI.

A benchtop system to assess the feasibility of a fully independent and implantable brain-machine interface

Objective. State-of-the-art invasive brain-machine interfaces (BMIs) have shown significant promise, but rely on external electronics and wired connections between the brain and these external components. This configuration presents health risks and limits practical use. These limitations can be addressed by designing a fully implantable BMI similar to existing FDA-approved implantable devices. Here, a prototype BMI system whose size and power consumption are comparable to those of fully implantable medical devices was designed and implemented, and its performance was tested at the benchtop and bedside. Approach. A prototype of a fully implantable BMI system was designed and implemented as a miniaturized embedded system. This benchtop analogue was tested in its ability to acquire signals, train a decoder, perform online decoding, wirelessly control external devices, and operate independently on battery. Furthermore, performance metrics such as power consumption were benchmarked. Main results. An analogue of a fully implantable BMI was fabricated with a miniaturized form factor. A patient undergoing epilepsy surgery evaluation with an electrocorticogram (ECoG) grid implanted over the primary motor cortex was recruited to operate the system. Seven online runs were performed with an average binary state decoding accuracy of 87.0% (lag optimized, or 85.0% at fixed latency). The system was powered by a wirelessly rechargeable battery, consumed ∼150 mW, and operated for >60 h on a single battery cycle. Significance. The BMI analogue achieved immediate and accurate decoding of ECoG signals underlying hand movements. A wirelessly rechargeable battery and other supporting functions allowed the system to function independently. In addition to the small footprint and acceptable power and heat dissipation, these results suggest that fully implantable BMI systems are feasible.