Implanted Microelectrode Arrays Research Articles

High-Performance Bidirectional Microelectrode Array for Assessing Sevoflurane Anesthesia Effects and In Situ Electrical Stimulation in Deep Brain Regions.

Precise assessment of wakefulness states during sevoflurane anesthesia and timely arousal are of paramount importance to refine the control of anesthesia. To tackle this issue, a bidirectional implantable microelectrode array (MEA) is designed with the capability to detect electrophysiological signal and perform in situ deep brain stimulation (DBS) within the dorsomedial hypothalamus (DMH) of mice. The MEA, modified with platinum nanoparticles/IrOx nanocomposites, exhibits exceptional characteristics, featuring low impedance, minimal phase delay, substantial charge storage capacity, high double-layer capacitance, and longer in vivo lifetime, thereby enhancing the sensitivity of spike firing detection and electrical stimulation (ES) effectiveness. Using this MEA, sevoflurane-inhibited neurons and sevoflurane-excited neurons, together with changes in the oscillation characteristics of the local field potential within the DMH, are revealed as indicative markers of arousal states. During the arousal period, varying-frequency ESs are applied to the DMH, eliciting distinct arousal effects. Through in situ detection and stimulation, the disparity between these outcomes can be attributed to the influence of DBS on different neurons. These advancements may further our understanding of neural circuits and their potential applications in clinical contexts.

Representation of internal speech by single neurons in human supramarginal gyrus.

Speech brain-machine interfaces (BMIs) translate brain signals into words or audio outputs, enabling communication for people having lost their speech abilities due to diseases or injury. While important advances in vocalized, attempted and mimed speech decoding have been achieved, results for internal speech decoding are sparse and have yet to achieve high functionality. Notably, it is still unclear from which brain areas internal speech can be decoded. Here two participants with tetraplegia with implanted microelectrode arrays located in the supramarginal gyrus (SMG) and primary somatosensory cortex (S1) performed internal and vocalized speech of six words and two pseudowords. In both participants, we found significant neural representation of internal and vocalized speech, at the single neuron and population level in the SMG. From recorded population activity in the SMG, the internally spoken and vocalized words were significantly decodable. In an offline analysis, we achieved average decoding accuracies of 55% and 24% for each participant, respectively (chance level 12.5%), and during an online internal speech BMI task, we averaged 79% and 23% accuracy, respectively. Evidence of shared neural representations between internal speech, word reading and vocalized speech processes was found in participant 1. SMG represented words as well as pseudowords, providing evidence for phonetic encoding. Furthermore, our decoder achieved high classification with multiple internal speech strategies (auditory imagination/visual imagination). Activity in S1 was modulated by vocalized but not internal speech in both participants, suggesting no articulator movements of the vocal tract occurred during internal speech production. This work represents a proof-of-concept for a high-performance internal speech BMI.

Wireless closed-loop deep brain stimulation using microelectrode array probes.

Deep brain stimulation (DBS), including optical stimulation and electrical stimulation, has been demonstrated considerable value in exploring pathological brain activity and developing treatments for neural disorders. Advances in DBS microsystems based on implantable microelectrode array (MEA) probes have opened up new opportunities for closed-loop DBS (CL-DBS) in situ. This technology can be used to detect damaged brain circuits and test the therapeutic potential for modulating the output of these circuits in a variety of diseases simultaneously. Despite the success and rapid utilization of MEA probe-based CL-DBS microsystems, key challenges, including excessive wired communication, need to be urgently resolved. In this review, we considered recent advances in MEA probe-based wireless CL-DBS microsystems and outlined the major issues and promising prospects in this field. This technology has the potential to offer novel therapeutic options for psychiatric disorders in the future.

Real-Time Compact Digital Processing Chain for the Detection and Sorting of Neural Spikes from Implanted Microelectrode Arrays

Implantable microelectrodes arrays are used to record electrical signals from surrounding neurons and have led to incredible improvements in modern neuroscience research. Digital signals resulting from conditioning and the analog-to-digital conversion of neural spikes captured by microelectrodes arrays have to be elaborated in a dedicated DSP core devoted to a real-time spike-sorting process for the classification phase based on the source neurons from which they were emitted. On-chip spike-sorting is also essential to achieve enough data reduction to allow for wireless transmission within the power constraints imposed on implantable devices. The design of such integrated circuits must meet stringent constraints related to ultra-low power density and the minimum silicon area, as well as several application requirements. The aim of this work is to present real-time hardware architecture able to perform all the spike-sorting tasks on chip while satisfying the aforementioned stringent requirements related to this type of application. The proposed solution has been coded in VHDL language and simulated in the Cadence Xcelium tool to verify the functional behavior of the digital processing chain. Then, a synthesis and place and route flow has been carried out to implement the proposed architecture in both a 130 nm and a FD-SOI 28 nm CMOS process, with a 200 MHz clock frequency target. Post-layout simulations in the Cadence Xcelium tool confirmed the proper operation up to a 200 MHz clock frequency. The area occupation and power consumption of the proposed detection and clustering module are 0.2659 mm2/ch, 7.16 μW/ch, 0.0168 mm2/ch, and 0.47 μW/ch for the 130 nm and 28 nm implementation, respectively.

Neural Oscillation Disorder in the Hippocampal CA1 Region of Different Alzheimer's Disease Mice.

Alzheimer's disease (AD) is a well-known neurodegenerative disease that gradually induces neural network dysfunction and progressive memory deficits. Neural network activity is represented by rhythmic oscillations that influence local field potentials (LFPs). However, changes in hippocampal neural rhythmic oscillations in the early stage of AD remain largely unexplored. This study investigated neural rhythmic oscillations in 3-month-old APP/PS1 and 5x- FAD mice to assess early neural connectivity in AD. LFPs were recorded from the hippocampal CA1 region with implanted microelectrode arrays while the mice were in the awake resting stage. Welch fast Fourier transforms, continuous wavelet transforms, and phase-amplitude coupling analyses were used to compute the power density of different frequency bands and phase-amplitude modulation indices in the LFPs. Our results showed impaired theta, low gamma, and high gamma frequency band power in APP/PS1 and 5xFAD mice during the awake resting stage. AD mice also showed decreased delta, alpha, and beta frequency band power. Impaired theta-low gamma and theta-high gamma phaseamplitude coupling were observed in 5xFAD mice. This study revealed neural network activity differences in oscillation power and cross-frequency coupling in the early stage of AD, providing a new perspective for developing biomarkers for early AD diagnosis.

Differential expression of genes involved in the chronic response to intracortical microelectrodes.

Brain-Machine Interface systems (BMIs) are clinically valuable devices that can provide functional restoration for patients with spinal cord injury or improved integration for patients requiring prostheses. Intracortical microelectrodes can record neuronal action potentials at a resolution necessary for precisely controlling BMIs. However, intracortical microelectrodes have a demonstrated history of progressive decline in the recording performance with time, inhibiting their usefulness. One major contributor to decreased performance is the neuroinflammatory response to the implanted microelectrodes. The neuroinflammatory response can lead to neurodegeneration and the formation of a glial scar at the implant site. Historically, histological imaging of relatively few known cellular and protein markers has characterized the neuroinflammatory response to implanted microelectrode arrays. However, neuroinflammation requires many molecular players to coordinate the response - meaning traditional methods could result in an incomplete understanding. Taking advantage of recent advancements in tools to characterize the relative or absolute DNA/RNA expression levels, a few groups have begun to explore gene expression at the microelectrode-tissue interface. We have utilized a custom panel of ∼813 neuroinflammatory-specific genes developed with NanoString for bulk tissue analysis at the microelectrode-tissue interface. Our previous studies characterized the acute innate immune response to intracortical microelectrodes. Here we investigated the gene expression at the microelectrode-tissue interface in wild-type (WT) mice chronically implanted with nonfunctioning probes. We found 28 differentially expressed genes at chronic time points (4WK, 8WK, and 16WK), many in the complement and extracellular matrix system. Further, the expression levels were relatively stable over time. Genes identified here represent chronic molecular players at the microelectrode implant sites and potential therapeutic targets for the long-term integration of microelectrodes. STATEMENT OF SIGNIFICANCE: Intracortical microelectrodes can record neuronal action potentials at a resolution necessary for the precise control of Brain-Machine Interface systems (BMIs). However, intracortical microelectrodes have a demonstrated history of progressive declines in the recording performance with time, inhibiting their usefulness. One major contributor to the decline in these devices is the neuroinflammatory response against the implanted microelectrodes. Historically, neuroinflammation to implanted microelectrode arrays has been characterized by histological imaging of relatively few known cellular and protein markers. Few studies have begun to develop a more in-depth understanding of the molecular pathways facilitating device-mediated neuroinflammation. Here, we are among the first to identify genetic pathways that could represent targets to improve the host response to intracortical microelectrodes, and ultimately device performance.

Electrochemical Properties of Sputtered Ruthenium Oxide Neural Stimulation and Recording Electrodes

A chronically stable electrode material with a low impedance for recording neural activity, and a high charge-injection capacity for functional electro-stimulation is desirable for the fabrication of implantable microelectrode arrays that aim to restore impaired or lost neurological functions in humans. For this purpose, we have investigated the electrochemical properties of sputtered ruthenium oxide (RuOx) electrode coatings deposited on planar microelectrode arrays, using an inorganic model of interstitial fluid (model-ISF) at 37 °C as the electrolyte. Through a combination of cyclic voltammetry (CV) and an electrochemical impedance spectroscopy (EIS) modelling study, we have established the contribution of the faradaic reaction as the major charge-injection contributor within the safe neural stimulation potential window of ±0.6 V vs. Ag|AgCl. We have also established the reversibility of the charge-injection process for sputtered RuOx film, by applying constant charge-per-phase current stimulations at different pulse widths, and by comparing the magnitudes of the leading and trailing access voltages during voltage transient measurements. Finally, the impedance of the sputtered RuOx film was found to be reasonably comparable in both its oxidized and reduced states, although the electronic contribution from the capacitive double-layer was found to be slightly higher for the completely oxidized film around 0.6 V than for its reduced counterpart around −0.6 V.

Multi-channel microelectrode arrays for detection of single-cell level neural information in the hippocampus CA1 under general anesthesia induced by low-dose isoflurane

Timely monitoring of anesthesia status during surgery is important to prevent an overdose of isoflurane anesthesia. Therefore, in-depth studies of the neural mechanisms of anesthetics are warranted. Hippocampal CA1 plays an important role during anesthesia. Currently, a high spatiotemporal resolution microdevice technology for the accurate detection of deep brain nuclei is lacking. In this research, four-shank 32-channel implantable microelectrode arrays (MEAs) were developed for the real-time recording of single-cell level neural information in rat hippocampal CA1. Platinum nanoparticles were modified onto the microelectrodes to substantially enhance the electrical properties of the microelectrode arrays. The modified MEAs exhibited low impedance (11.5 ± 1 kΩ) and small phase delay (-18.5 ± 2.54°), which enabled the MEAs to record single-cell level neural information with a high signal-to-noise ratio. The MEAs were implanted into the CA1 nuclei of the anesthetized rats, and the electrophysiological signals were recorded under different degrees of anesthesia mediated by low-dose concentrations of isoflurane. The recorded signals were analyzed in depth. Isoflurane caused an inhibition of spike firing rate in hippocampal CA1 neurons, while inducing low-frequency oscillations in CA1, thus enhancing the low-frequency power of local field potentials. In this manner, the spike firing rate and the power of local field potentials in CA1 could characterize the degree of isoflurane anesthesia. The present study provides a technical tool to study the neural mechanisms of isoflurane anesthesia and a research method for monitoring the depth of isoflurane anesthesia in clinical practice.

Highly Activated Neuronal Firings Monitored by Implantable Microelectrode Array in the Paraventricular Thalamus of Insomnia Rats

Insomnia is a common sleep disorder around the world, which is harmful to people’s health, daily life, and work. The paraventricular thalamus (PVT) plays an essential role in the sleep–wake transition. However, high temporal-spatial resolution microdevice technology is lacking for accurate detection and regulation of deep brain nuclei. The means for analyzing sleep–wake mechanisms and treating sleep disorders are limited. To detect the relationship between the PVT and insomnia, we designed and fabricated a special microelectrode array (MEA) to record electrophysiological signals of the PVT for insomnia and control rats. Platinum nanoparticles (PtNPs) were modified onto an MEA, which caused the impedance to decrease and improved the signal-to-noise ratio. We established the model of insomnia in rats and analyzed and compared the neural signals in detail before and after insomnia. In insomnia, the spike firing rate was increased from 5.48 ± 0.28 spike/s to 7.39 ± 0.65 spike/s, and the power of local field potential (LFP) decreased in the delta frequency band and increased in the beta frequency band. Furthermore, the synchronicity between PVT neurons declined, and burst-like firing was observed. Our study found neurons of the PVT were more activated in the insomnia state than in the control state. It also provided an effective MEA to detect the deep brain signals at the cellular level, which conformed with macroscopical LFP and insomnia symptoms. These results laid the foundation for studying PVT and the sleep–wake mechanism and were also helpful for treating sleep disorders.

PtNPs/rGO-GluOx/mPD Directionally Electroplated Dual-Mode Microelectrode Arrays for Detecting the Synergistic Relationship between the Cortex and Hippocampus of Epileptic Rats.

Precise and directional couplings of functional nanomaterials with implantable microelectrode arrays (IMEAs) are critical for the manufacture of sensitive enzyme-based electrochemical neural sensors. However, there is a gap between the microscale of IMEA and conventional bioconjugation techniques for enzyme immobilization, which leads to a series of challenges such as limited sensitivity, signal crosstalk, and high detection voltage. Here, we developed a novel method using carboxylated graphene oxide (cGO) to directionally couple the glutamate oxidase (GluOx) biomolecules onto the neural microelectrode to monitor glutamate concentration and electrophysiology in the cortex and hippocampus of epileptic rats under RuBi-GABA modulation. The resulting glutamate IMEA exhibited good performance involving less signal crosstalk between microelectrodes, lower reaction potential (0.1 V), and higher linear sensitivity (141.00 ± 5.66 nA μM-1 mm-2). The excellent linearity ranged from 0.3 to 68 μM (R = 0.992), and the limit of detection was 0.3 μM. For epileptic rats, the proposed IMEA sensitively obtained synergetic variations in the action potential (Spike), local field potentials (LFPs), and glutamate of the cortex and hippocampus during seizure and RuBi-GABA inhibition. We found that the increase in glutamate preceded the burst of electrophysiological signals. At the same time, both changes in the hippocampus preceded the cortex. This reminded us that glutamate changes in the hippocampus could serve as important indicators for early warning of epilepsy. Our findings provided a new technical strategy for directionally stabilizing enzymes onto the IMEA with versatile implications for various biomolecules' modification and facilitated the development of detecting tools for understanding the neural mechanism.

Chronic Stability of Local Field Potentials Using Amorphous Silicon Carbide Microelectrode Arrays Implanted in the Rat Motor Cortex.

Implantable microelectrode arrays (MEAs) enable the recording of electrical activity of cortical neurons, allowing the development of brain-machine interfaces. However, MEAs show reduced recording capabilities under chronic conditions, prompting the development of novel MEAs that can improve long-term performance. Conventional planar, silicon-based devices and ultra-thin amorphous silicon carbide (a-SiC) MEAs were implanted in the motor cortex of female Sprague-Dawley rats, and weekly anesthetized recordings were made for 16 weeks after implantation. The spectral density and bandpower between 1 and 500 Hz of recordings were compared over the implantation period for both device types. Initially, the bandpower of the a-SiC devices and standard MEAs was comparable. However, the standard MEAs showed a consistent decline in both bandpower and power spectral density throughout the 16 weeks post-implantation, whereas the a-SiC MEAs showed substantially more stable performance. These differences in bandpower and spectral density between standard and a-SiC MEAs were statistically significant from week 6 post-implantation until the end of the study at 16 weeks. These results support the use of ultra-thin a-SiC MEAs to develop chronic, reliable brain-machine interfaces.

Soft Neural Interfacing based on Implantable Graphene Fiber Microelectrode Arrays.

Microelectrode Arrays (MEAs) neural interfaces are considered implantable devices that interact with the nervous system to monitor and/or modulate brain activity. Graphene-based materials are utilized to address some of the current challenges in neural interface design due to their desirable features, such as high conductance, large surface-to-volume ratio, suitable electrochemical properties, biocompatibility, flexibility, and ease of production. In the current study, we fabricated and characterized a type of flexible, ultrasmall, and implantable neurostimulator based on graphene fibers. In this procedure, wet-spinning was employed to create graphene fibers with diameters of 10 to 50 µm. A 10-channel polyimide Printed Circuit Board (PCB) was then custom-designed and manufactured. The fibers were attached to each channel by conductive glue and also insulated by soaking them in a polyurethane solution. The tips were subsequently exposed using a blowtorch. Microstructural information on the fibers was obtained using Scanning Electron Microscopy (SEM), and the measurements of Electrochemical Impedance Spectroscopy (EIS) were conducted for each electrode. Flexible MEAs were created using graphene fibers with diameters ranging from 10 to 50 microns with a spacing of 150 microns. This method leads to producing electrode arrays with any size of fibers and a variety of channel numbers. The flexible neural prostheses can replace conventional electrodes in both neuroscience and biomedical research.

Theta and gamma oscillations in the rat hippocampus support the discrimination of object displacement in a recognition memory task.

Episodic memory depends on the recollection of spatial and temporal aspects of past experiences in which the hippocampus plays a critical role. Studies on hippocampal lesions in rodents have shown that dentate gyrus (DG) and CA3 are necessary to detect object displacement in memory tasks. However, the understanding of real-time oscillatory activity underlying memory discrimination of subtle and pronounced displacements remains elusive. Here, we chronically implanted microelectrode arrays in adult male Wistar rats to record network oscillations from DG, CA3, and CA1 of the dorsal hippocampus while animals executed an object recognition task of high and low spatial displacement tests (HD: 108 cm, and LD: 54 cm, respectively). Behavioral analysis showed that the animals discriminate between stationary and displaced objects in the HD but not LD conditions. To investigate the hypothesis that theta and gamma oscillations in different areas of the hippocampus support discrimination processes in a recognition memory task, we compared epochs of object exploration between HD and LD conditions as well as displaced and stationary objects. We observed that object exploration epochs were accompanied by strong rhythmic activity in the theta frequency (6-12 Hz) band in the three hippocampal areas. Comparison between test conditions revealed higher theta band power and higher theta-gamma phase-amplitude coupling in the DG during HD than LD conditions. Similarly, direct comparison between displaced and stationary objects within the HD test showed higher theta band power in CA3 during exploration of displaced objects. Moreover, the discrimination index between displaced and stationary objects directly correlated with CA1 gamma band power in epochs of object exploration. We thus conclude that theta and gamma oscillations in the dorsal hippocampus support the successful discrimination of object displacement in a recognition memory task.

Manufacturing Processes of Implantable Microelectrode Array for In Vivo Neural Electrophysiological Recordings and Stimulation: A State-Of-the-Art Review.

Electrophysiological recording and stimulation of neuron activities are important for us to understand the function and dysfunction of the nervous system. To record/stimulate neuron activities as voltage fluctuation extracellularly, microelectrode array (MEA) implants are a promising tool to provide high temporal and spatial resolution for neuroscience studies and medical treatments. The design configuration and recording capabilities of the MEAs have evolved dramatically since their invention and manufacturing process development has been a key driving force for such advancement. Over the past decade, since the White House Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative launched in 2013, advanced manufacturing processes have enabled advanced MEAs with increased channel count and density, access to more brain areas, more reliable chronic performance, as well as minimal invasiveness and tissue reaction. In this state-of-the-art review paper, three major types of electrophysiological recording MEAs widely used nowadays, namely, microwire-based, silicon-based, and flexible MEAs are introduced and discussed. Conventional design and manufacturing processes and materials used for each type are elaborated, followed by a review of further development and recent advances in manufacturing technologies and the enabling new designs and capabilities. The review concludes with a discussion on potential future directions of manufacturing process development to enable the long-term goal of large-scale high-density brain-wide chronic recordings in freely moving animals.

A flexible implantable microelectrode array for recording electrocorticography signals from rodents.

Electrocorticography signals, the intracranial recording of electrical signatures of the brain, are recorded by non-penetrating planar electrode arrays placed on the cortical surface. Flexible electrode arrays minimize the tissue damage upon implantation. This work shows the design and development of a 32-channel flexible microelectrode array to record electrocorticography signals from the rat's brain. The array was fabricated on a biocompatible flexible polyimide substrate. A titanium/gold layer was patterned as electrodes, and a thin polyimide layer was used for insulation. The fabricated microelectrode array was mounted on the exposed somatosensory cortex of the right hemisphere of a rat after craniotomy and incision of the dura. The signals were recorded using OpenBCI Cyton Daisy Biosensing Boards. The array faithfully recorded the baseline electrocorticography signals, the induced epileptic activities after applying a convulsant, and the recovered baseline signals after applying an antiepileptic drug. The signals recorded by such fabricated microelectrode array from anesthetized rats demonstrate its potential to monitor electrical signatures corresponding to epilepsy. Finally, the time-frequency analyses highlight the difference in spatiotemporal features of baseline and evoked epileptic discharges.

Grid cell remapping under three-dimensional object and social landmarks detected by implantable microelectrode arrays for the medial entorhinal cortex

Grid cells with stable hexagonal firing patterns in the medial entorhinal cortex (MEC) carry the vital function of serving as a metric for the surrounding environment. Whether this mechanism processes only spatial information or involves nonspatial information remains elusive. Here, we fabricated an MEC-shaped microelectrode array (MEA) to detect the variation in neural spikes and local field potentials of the MEC when rats forage in a square enclosure with a planar, three-dimensional object and social landmarks in sequence. The results showed that grid cells exhibited rate remapping under social conditions in which spike firing fields closer to the social landmark had a higher firing rate. Furthermore, global remapping showed that hexagonal firing patterns were rotated and scaled when the planar landmark was replaced with object and social landmarks. In addition, when grid cells were activated, the local field potentials were dominated by the theta band (5–8 Hz), and spike phase locking was observed at troughs of theta oscillations. Our results suggest the pattern separation mechanism of grid cells in which the spatial firing structure and firing rate respond to spatial and social information, respectively, which may provide new insights into how the brain creates a cognitive map.

Information sparseness in cortical microelectrode channels while decoding movement direction using an artificial neural network.

Implanted microelectrode arrays can directly pick up electrode signals from the primary motor cortex (M1) during movement, and brain-machine interfaces (BMIs) can decode these signals to predict the directions of contemporaneous movements. However, it is not well known how much each individual input is responsible for the overall performance of a BMI decoder. In this paper, we seek to quantify how much each channel contributes to an artificial neural network (ANN)-based decoder, by measuring how much the removal of each individual channel degrades the accuracy of the output. If information on movement direction was equally distributed among channels, then the removal of one would have a minimal effect on decoder accuracy. On the other hand, if that information was distributed sparsely, then the removal of specific information-rich channels would significantly lower decoder accuracy. We found that for most channels, their removal did not significantly affect decoder performance. However, for a subset of channels (16 out of 61), removing them significantly reduced the decoder accuracy. This suggests that information is not uniformly distributed among the recording channels. We propose examining these channels further to optimize BMIs more effectively, as well as understand how M1 functions at the neuronal level.

High-performance Flexible Microelectrode Array with PEDOT:PSS Coated 3D Micro-cones for Electromyographic Recording.

High signal-to-noise ratio (SNR) electromyography (EMG) recordings are essential for identifying and analyzing single motor unit activity. While high-density electrodes allow for greater spatial resolution, the smaller electrode area translates to a higher impedance and lower SNR. In this study, we developed an implantable and flexible 3D microelectrode array (MEA) with low impedance that enables high-quality EMG recording. With polyimide micro-cones realized by standard photolithography process and PEDOT:PSS coating, this design can increase effective surface area by up to 250% and significantly improve electrical performance for electrode sites with various geometric surface areas, where the electrode impedance is at most improved by 99.3%. Acute EMG activity from mice was recorded by implanting the electrodes in vivo, and we were able to detect multiple individual motor units simultaneously and with high resolution ([Formula: see text]). The charge storage capacity was measured to be 34.2 mC/cm2, indicating suitability of the electrodes for stimulation applications as well.

ID:16044 Feasibility of 2-Dimensional Multielectrode Arrays to Record Selective Cerebellar Spiking

Implantable microelectrode arrays (MEAs) have been widely used to obtain single units within the central and peripheral nervous systems due to their multi-shank design that has selective recording sites at various depths [1]. Despite their potential applications, MEAs have not been extensively used for recording inside the cerebellum. The unique MEA characteristic can be beneficial to the recording of signals within the layered cellular organization of this target (i.e., granule cells, Mossy fibers, Purkinje cells, and Golgi cells).

Implicit mechanisms of intention

High-level cortical regions encode motor decisions before or even absent awareness, suggesting that neural processes predetermine behavior before conscious choice. Such early neural encoding challenges popular conceptions of human agency. It also raises fundamental questions for brain-machine interfaces (BMIs) that traditionally assume that neural activity reflects the user's conscious intentions. Here, we study the timing of human posterior parietal cortex single-neuron activity recorded from implanted microelectrode arrays relative to the explicit urge to initiate movement. Participants were free to choose when to move, whether to move, and what to move, and they retrospectively reported the time they felt the urge to move. We replicate prior studies by showing that posterior parietal cortex (PPC) neural activity sharply rises hundreds of milliseconds before the reported urge. However, we find that this "preconscious" activity is part of a dynamic neural population response that initiates much earlier, when the participant first chooses to perform the task. Together with details of neural timing, our results suggest that PPC encodes an internal model of the motor planning network that transforms high-level task objectives into appropriate motor behavior. These new data challenge traditional interpretations of early neural activity and offer a more holistic perspective on the interplay between choice, behavior, and their neural underpinnings. Our results have important implications for translating BMIs into more complex real-world environments. We find that early neural dynamics are sufficient to drive BMI movements before the participant intends to initiate movement. Appropriate algorithms ensure that BMI movements align with the subject's awareness of choice.