Wireless Brain-machine Interface Research Articles

A wireless neural recording microsystem with operator-based spike detection

We introduce an innovative approach that incorporates operator-based spike detection in wireless microsystems for neural signal processing. Through comparative analyses between simple thresholding and operator-based detection conducted on pre-recorded spike detection experiments, our research emphasizes the superiority of the operator-based spike detection approach. The operator-based spike detection emerges as a promising technique for miniaturized wireless neural signal devices, primarily due to its proficient noise-handling capabilities paired with reduced power consumption. Furthermore, its adaptability across various experimental conditions amplifies its versatility. Empirical tests underscored its low power requisites and compactness, emphasizing practical utility of the detection scheme in the neural microsystems. Collectively, our results mark a significant progression in wireless cerebral signal recording methodologies, paving the way for optimized wireless brain-machine interface (BMI) systems.

A 0.75 V Sub-mW CMOS LNA employing transmitted signal suppression technique in a full-duplex wireless brain-machine interface transceiver

This paper presents a sub-milliwatt (sub-mW) low-voltage CMOS low-noise amplifier (LNA) that can be utilized in a full-duplex transceiver without using duplexer. The topology of the proposed LNA includes a wideband RLC network to suppress and isolate the receiver from a high-power ultra-wideband transmitted signal. This feature is comparable to other reported LNAs, which are only applicable to a narrowband signal instead of a wideband signal. In addition, the combination of a source-degenerated cascode LNA with an inverter-based LNA in the proposed topology achieves an ultra-low-voltage low-power operation with a 0.75 V bias voltage. Analytical results and the circuit implementation in a 180 nm CMOS technology verify that the proposed LNA presents an noise figure (NF) of less than 3.45 dB over a 200 MHz bandwidth at a carrier frequency of 2.4 GHz. Simulation results also demonstrate a 14 dB gain (S21), less than −11 dB input return loss (S11) and 50 dB transmitted signal attenuation rather than the desired signal. The power consumption is only 980 μW at a 0.75 V supply. The input-referred third-order intercept point (IIP3) is about −8 dBm, and the chip occupies an area of 0.37 mm2.

Minimal Tissue Reaction after Chronic Subdural Electrode Implantation for Fully Implantable Brain-Machine Interfaces.

There is a growing interest in the use of electrocorticographic (ECoG) signals in brain–machine interfaces (BMIs). However, there is still a lack of studies involving the long-term evaluation of the tissue response related to electrode implantation. Here, we investigated biocompatibility, including chronic tissue response to subdural electrodes and a fully implantable wireless BMI device. We implanted a half-sized fully implantable device with subdural electrodes in six beagles for 6 months. Histological analysis of the surrounding tissues, including the dural membrane and cortices, was performed to evaluate the effects of chronic implantation. Our results showed no adverse events, including infectious signs, throughout the 6-month implantation period. Thick connective tissue proliferation was found in the surrounding tissues in the epidural space and subcutaneous space. Quantitative measures of subdural reactive tissues showed minimal encapsulation between the electrodes and the underlying cortex. Immunohistochemical evaluation showed no significant difference in the cell densities of neurons, astrocytes, and microglia between the implanted sites and contralateral sites. In conclusion, we established a beagle model to evaluate cortical implantable devices. We confirmed that a fully implantable wireless device and subdural electrodes could be stably maintained with sufficient biocompatibility in vivo.

An exoskeleton controlled by an epidural wireless brain–machine interface in a tetraplegic patient: a proof-of-concept demonstration

Approximately 20% of traumatic cervical spinal cord injuries result in tetraplegia. Neuroprosthetics are being developed to manage this condition and thus improve the lives of patients. We aimed to test the feasibility of a semi-invasive technique that uses brain signals to drive an exoskeleton. We recruited two participants at Clinatec research centre, associated with Grenoble University Hospital, Grenoble, France, into our ongoing clinical trial. Inclusion criteria were age 18-45 years, stability of neurological deficits, a need for additional mobility expressed by the patient, ambulatory or hospitalised monitoring, registration in the French social security system, and signed informed consent. The exclusion criteria were previous brain surgery, anticoagulant treatments, neuropsychological sequelae, depression, substance dependence or misuse, and contraindications to magnetoencephalography (MEG), EEG, or MRI. One participant was excluded because of a technical problem with the implants. The remaining participant was a 28-year-old man, who had tetraplegia following a C4-C5 spinal cord injury. Two bilateral wireless epidural recorders, each with 64 electrodes, were implanted over the upper limb sensorimotor areas of the brain. Epidural electrocorticographic (ECoG) signals were processed online by an adaptive decoding algorithm to send commands to effectors (virtual avatar or exoskeleton). Throughout the 24 months of the study, the patient did various mental tasks to progressively increase the number of degrees of freedom. Between June 12, 2017, and July 21, 2019, the patient cortically controlled a programme that simulated walking and made bimanual, multi-joint, upper-limb movements with eight degrees of freedom during various reach-and-touch tasks and wrist rotations, using a virtual avatar at home (64·0% [SD 5·1] success) or an exoskeleton in the laboratory (70·9% [11·6] success). Compared with microelectrodes, epidural ECoG is semi-invasive and has similar efficiency. The decoding models were reusable for up to approximately 7 weeks without recalibration. These results showed long-term (24-month) activation of a four-limb neuroprosthetic exoskeleton by a complete brain-machine interface system using continuous, online epidural ECoG to decode brain activity in a tetraplegic patient. Up to eight degrees of freedom could be simultaneously controlled using a unique model, which was reusable without recalibration for up to about 7 weeks. French Atomic Energy Commission, French Ministry of Health, Edmond J Safra Philanthropic Foundation, Fondation Motrice, Fondation Nanosciences, Institut Carnot, Fonds de Dotation Clinatec.

Design and testing of a 96-channel neural interface module for the Networked Neuroprosthesis system

BackgroundThe loss of motor functions resulting from spinal cord injury can have devastating implications on the quality of one’s life. Functional electrical stimulation has been used to help restore mobility, however, current functional electrical stimulation (FES) systems require residual movements to control stimulation patterns, which may be unintuitive and not useful for individuals with higher level cervical injuries. Brain machine interfaces (BMI) offer a promising approach for controlling such systems; however, they currently still require transcutaneous leads connecting indwelling electrodes to external recording devices. While several wireless BMI systems have been designed, high signal bandwidth requirements limit clinical translation. Case Western Reserve University has developed an implantable, modular FES system, the Networked Neuroprosthesis (NNP), to perform combinations of myoelectric recording and neural stimulation for controlling motor functions. However, currently the existing module capabilities are not sufficient for intracortical recordings.MethodsHere we designed and tested a 1 × 4 cm, 96-channel neural recording module prototype to fit within the specifications to mate with the NNP. The neural recording module extracts power between 0.3–1 kHz, instead of transmitting the raw, high bandwidth neural data to decrease power requirements.ResultsThe module consumed 33.6 mW while sampling 96 channels at approximately 2 kSps. We also investigated the relationship between average spiking band power and neural spike rate, which produced a maximum correlation of R = 0.8656 (Monkey N) and R = 0.8023 (Monkey W).ConclusionOur experimental results show that we can record and transmit 96 channels at 2ksps within the power restrictions of the NNP system and successfully communicate over the NNP network. We believe this device can be used as an extension to the NNP to produce a clinically viable, fully implantable, intracortically-controlled FES system and advance the field of bioelectronic medicine.

An End-to-End Implanted Brain–Machine Interface Antenna System Performance Characterizations and Development

Brain-machine interface (BMI) is a multidisciplinary field that has been recently developed in an attempt to help restore functionalities for paralyzed individuals. One of the key components for the implementation of a wireless BMI necessitates unique designs for both the internal brain and external head antennas. In this paper, we initially revisited the design of an optimized 1-mm3 implantable antenna transferring power and data with a reduced size low profile external reader antenna by utilizing radio-frequency identification (RFID)-inspired backscattering. Detailed computational assessments and specific absorption rate evaluations are performed. Prototypes were characterized in terms of link efficiency through a realized RFID link with up to −25 dB link efficiency. The noise analysis for antennas in biological systems was performed using two novel absorption-noise models. And finally a channel capacity estimation was performed, proving that the BMI antenna link could support up to 100 recording channels. An end-to-end BMI antenna system characterization is detailed in this paper for multichannel implanted neural recording applications.

Independent Mobility Achieved through a Wireless Brain-Machine Interface.

Individuals with tetraplegia lack independent mobility, making them highly dependent on others to move from one place to another. Here, we describe how two macaques were able to use a wireless integrated system to control a robotic platform, over which they were sitting, to achieve independent mobility using the neuronal activity in their motor cortices. The activity of populations of single neurons was recorded using multiple electrode arrays implanted in the arm region of primary motor cortex, and decoded to achieve brain control of the platform. We found that free-running brain control of the platform (which was not equipped with any machine intelligence) was fast and accurate, resembling the performance achieved using joystick control. The decoding algorithms can be trained in the absence of joystick movements, as would be required for use by tetraplegic individuals, demonstrating that the non-human primate model is a good pre-clinical model for developing such a cortically-controlled movement prosthetic. Interestingly, we found that the response properties of some neurons differed greatly depending on the mode of control (joystick or brain control), suggesting different roles for these neurons in encoding movement intention and movement execution. These results demonstrate that independent mobility can be achieved without first training on prescribed motor movements, opening the door for the implementation of this technology in persons with tetraplegia.

Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates

Several groups have developed brain-machine-interfaces (BMIs) that allow primates to use cortical activity to control artificial limbs. Yet, it remains unknown whether cortical ensembles could represent the kinematics of whole-body navigation and be used to operate a BMI that moves a wheelchair continuously in space. Here we show that rhesus monkeys can learn to navigate a robotic wheelchair, using their cortical activity as the main control signal. Two monkeys were chronically implanted with multichannel microelectrode arrays that allowed wireless recordings from ensembles of premotor and sensorimotor cortical neurons. Initially, while monkeys remained seated in the robotic wheelchair, passive navigation was employed to train a linear decoder to extract 2D wheelchair kinematics from cortical activity. Next, monkeys employed the wireless BMI to translate their cortical activity into the robotic wheelchair’s translational and rotational velocities. Over time, monkeys improved their ability to navigate the wheelchair toward the location of a grape reward. The navigation was enacted by populations of cortical neurons tuned to whole-body displacement. During practice with the apparatus, we also noticed the presence of a cortical representation of the distance to reward location. These results demonstrate that intracranial BMIs could restore whole-body mobility to severely paralyzed patients in the future.

Multichannel neural recording with a 128 Mbps UWB wireless transmitter for implantable brain-machine interfaces.

To realize a low-invasive and high accuracy BMI (Brain-machine interface) system, we have already developed a fully-implantable wireless BMI system which consists of ECoG neural electrode arrays, neural recording ASICs, a Wi-Fi based wireless data transmitter and a wireless power receiver with a rechargeable battery. For accurate estimation of movement intentions, it is important for a BMI system to have a large number of recording channels. In this paper, we report a new multi-channel BMI system which is able to record up to 4096-ch ECoG data by multiple connections of 64-ch ASICs and time division multiplexing of recorded data. This system has an ultra-wide-band (UWB) wireless unit for transmitting the recorded neural signals to outside the body. By preliminary experiments with a human body equivalent liquid phantom, we confirmed 4096-ch UWB wireless data transmission at 128 Mbps mode below 20 mm distance.

Backscattering Neural Tags for Wireless Brain-Machine Interface Systems

Brain-machine interface (BMI) technology has tremendous potential to revolutionize healthcare by greatly improving the quality of life of millions of people suffering from a wide variety of neurological conditions. Radio-frequency identification (RFID)-inspired backscattering is a promising approach for wireless powering of miniature neural sensors required in BMI interfaces. We analyze the functionality of millimeter-size loop antennas in the wireless powering of miniature cortical implants through measurements in a human head equivalent liquid phantom and in the head of a postmortem pig. For the first time, we present the design and measurement of a miniature 1×1×1 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> backscattering device based on a cubic loop connected with an RFID integrated circuit (IC). Our measurement results show that this very small loop receives sufficient electromagnetic power to activate the IC when the device is implanted in a pig's head. This demonstrates the feasibility of extremely small implant antennas in challenging wireless biomedical systems.

Noninvasive detection of brain order-disorder transitions using functional EEG

Systems capable of real-time monitoring have several potential health-related applications, particularly for individuals who desire to age in place as well as those living with acute health conditions. An electrical response exists near the scalp from ionic transmission within neurons (brain cells) and may be measured noninvasively by electroencephalography (EEG). Use of EEG for detecting convulsive1, 2 and nonconvulsive3 epilepsy is well developed and documented in the literature with sensitivities between 80 and 90%. Real-time EEG monitoring during sleep may provide valuable information on alterations in sleep patterns, which has been shown to be effective in detecting and predicting anxiety, depression, and early dementia in the elderly.4, 5 Anxiety and depression in turn have been directly correlated to numerous deleterious chronic health conditions such as cardiovascular disease, stroke, sleep disorders, and diabetes. Having a mechanism to detect and predict onset of anxiety and depression would be a powerful out-of-clinic tool. A potential application for at-home EEGmonitoring is assessment of head injuries and post-concussion syndrome. As these conditions affect the alertness of individuals, there is evidence that head injuries affect the alpha wave patterns of the EEG in the range of 8–12Hz.6 While many previous studies have shown the potential for EEG to detect and assess cognitive impairments, most have been conducted under controlled research conditions in the clinic. Extension of these findings to the home requires further investigation as environmental conditions are likely to affect the EEG data collected. Wang and colleagues7 showed the efficacy of an untethered, wireless brain-machine interface system using 20 dry electrodes embedded into a head cap. This wireless ‘functional’ EEG Figure 1. (a) User-friendly smartphone interface to the functional electroencephalography (f-EEG) head mount. (b) 1D Peano-Hilberttype space-filling curve replaces the 3D layout of the electrodes. (c) High-density wired conventional EEG is replaced by a wireless hat. (d) Right-hemisphere brain f-EEG response stimulated by the visual input for a left eye dominator: before (left) and after compressive sensing (middle), as well as the difference errors (right). (e) Singlet electrode-averaged EEG (left) vs. pair-electrode fluctuation f-EEG (right) reflecting the subject’s intention to point a computer screen cursor to the left as opposed to the right: 121 electrodes (1 node to 82 points), 250Hz (averaged over 1/16 second).

Strategies for High-Performance Resource-Efficient Compression of Neural Spike Recordings

Brain-machine interfaces (BMIs) based on extracellular recordings with microelectrodes provide means of observing the activities of neurons that orchestrate fundamental brain function, and are therefore powerful tools for exploring the function of the brain. Due to physical restrictions and risks for post-surgical complications, wired BMIs are not suitable for long-term studies in freely behaving animals. Wireless BMIs ideally solve these problems, but they call for low-complexity techniques for data compression that ensure maximum utilization of the wireless link and energy resources, as well as minimum heat dissipation in the surrounding tissues. In this paper, we analyze the performances of various system architectures that involve spike detection, spike alignment and spike compression. Performance is analyzed in terms of spike reconstruction and spike sorting performance after wireless transmission of the compressed spike waveforms. Compression is performed with transform coding, using five different compression bases, one of which we pay special attention to. That basis is a fixed basis derived, by singular value decomposition, from a large assembly of experimentally obtained spike waveforms, and therefore represents a generic basis specially suitable for compressing spike waveforms. Our results show that a compression factor of 99.8%, compared to transmitting the raw acquired data, can be achieved using the fixed generic compression basis without compromising performance in spike reconstruction and spike sorting. Besides illustrating the relative performances of various system architectures and compression bases, our findings show that compression of spikes with a fixed generic compression basis derived from spike data provides better performance than compression with downsampling or the Haar basis, given that no optimization procedures are implemented for compression coefficients, and the performance is similar to that obtained when the optimal SVD based basis is used.

Miniature implantable and wearable on-body antennas: towards the new era of wireless body-centric systems [antenna applications corner

Wireless body-centric sensing systems have an important role in the fields of biomedicine, personal healthcare, safety, and security. Body-centric radio-frequency identification (RFID) technology provides a wireless and maintenance-free communication link between the human body and the surroundings through wearable and implanted antennas. This enables real-time monitoring of human vital signs everywhere. Seamlessly integrated wearable and implanted miniaturized antennas thus have the potential to revolutionize the everyday life of people, and to contribute to independent living. Low-cost and low-power system solutions will make widespread use of such technology become reality. The primary target applications for this research are body-centric sensing systems and the relatively new interdisciplinary field of wireless brain-machine interface (BMI) systems. Providing a direct wireless pathway between the brain and an external device, a wireless brain-machine interface holds an enormous potential for helping people suffering from severely disabling neurological conditions to communicate and manage their everyday life more independently. In this paper, we discuss RFID-inspired wireless brain-machine interface systems. We demonstrate that mm-size loop implanted antennas are capable of efficiently coupling to an external transmitting loop antenna through an inductive link. In addition, we focus on wearable antennas based on electrically conductive textiles and threads, and present design guidelines for their use as wearable-antenna conductive elements. Overall, our results constitute an important milestone in the development of wireless brain-machine interface systems, and a new era of wireless body-centric systems.

Measurement of Wireless Link for Brain–Machine Interface Systems Using Human-Head Equivalent Liquid

In this letter, we analyze the wireless power transfer from an on-body transmit antenna to a millimeter-size antenna in a cortical implant. The studied wireless link provides power and data telemetry for a battery-free wireless brain-machine interface microelectronic system. We present a cubic 2 × 2 × 2 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> implant loop and analyze the effect of a magneto-dielectric core material on the performance of the antenna. We compare the wireless link performances to two different transmit antennas: solid and 2-segmented loops. Our simulation results show that the 2-segmented loop provides reduced near electric field, but maintains the link power efficiency. Finally, we validate the simulated link power efficiencies through measurements in air and in human-head equivalent liquid at 300 MHz.

Minimizing data transfer with sustained performance in wireless brain–machine interfaces

Brain–machine interfaces (BMIs) may be used to investigate neural mechanisms or to treat the symptoms of neurological disease and are hence powerful tools in research and clinical practice. Wireless BMIs add flexibility to both types of applications by reducing movement restrictions and risks associated with transcutaneous leads. However, since wireless implementations are typically limited in terms of transmission capacity and energy resources, the major challenge faced by their designers is to combine high performance with adaptations to limited resources. Here, we have identified three key steps in dealing with this challenge: (1) the purpose of the BMI should be clearly specified with regard to the type of information to be processed; (2) the amount of raw input data needed to fulfill the purpose should be determined, in order to avoid over- or under-dimensioning of the design; and (3) processing tasks should be allocated among the system parts such that all of them are utilized optimally with respect to computational power, wireless link capacity and raw input data requirements. We have focused on step (2) under the assumption that the purpose of the BMI (step 1) is to assess single- or multi-unit neuronal activity in the central nervous system with single-channel extracellular recordings. The reliability of this assessment depends on performance in detection and sorting of spikes. We have therefore performed absolute threshold spike detection and spike sorting with the principal component analysis and fuzzy c-means on a set of synthetic extracellular recordings, while varying the sampling rate and resolution, noise level and number of target units, and used the known ground truth to quantitatively estimate the performance. From the calculated performance curves, we have identified the sampling rate and resolution breakpoints, beyond which performance is not expected to increase by more than 1–5%. We have then estimated the performance of alternative algorithms for spike detection and spike sorting in order to examine the generalizability of our results to other algorithms. Our findings indicate that the minimization of recording noise is the primary factor to consider in the design process. In most cases, there are breakpoints for sampling rates and resolution that provide guidelines for BMI designers in terms of minimum amount raw input data that guarantees sustained performance. Such guidelines are essential during system dimensioning. Based on these findings we conclude by presenting a quantitative task-allocation scheme that can be followed to achieve optimal utilization of available resources.

A Fully Implantable, Programmable and Multimodal Neuroprocessor for Wireless, Cortically Controlled Brain-Machine Interface Applications

Reliability, scalability and clinical viability are of utmost importance in the design of wireless Brain Machine Interface systems (BMIs). This paper reports on the design and implementation of a neuroprocessor for conditioning raw extracellular neural signals recorded through microelectrode arrays chronically implanted in the brain of awake behaving rats. The neuroprocessor design exploits a sparse representation of the neural signals to combat the limited wireless telemetry bandwidth. We demonstrate a multimodal processing capability (monitoring, compression, and spike sorting) inherent in the neuroprocessor to support a wide range of scenarios in real experimental conditions. A wireless transmission link with rate-dependent compression strategy is shown to preserve information fidelity in the neural data. At 32 channels, the neuroprocessor has been fully implemented on a 5mm×5mm nano-FPGA, and the prototyping resulted in 5.19 mW power consumption, bringing its performance within the power-size constraints for clinical use. The optimal design for compression and sorting performance was evaluated for multiple sampling frequencies, wavelet basis choice and power consumption.

Design of Wireless Links to Implanted Brain–Machine Interface Microelectronic Systems

This letter presents a monolithic integration of an antenna with an array of neural recording electrodes on a flexible thin film. The structure was designed for long-term neural recording in a wireless brain-machine interface system. The implant-on-body antenna pair is optimized for maximal link power efficiency to maximize the battery life of a portable outside-body control unit. We provide guidelines for the design of the sub-skin-depth implant antenna and validate the antenna simulation model with wireless link measurements in air. We propose a new computational analysis of both the power and voltage delivery to the battery-free implant under design variations to guarantee efficient on-chip RF-to-dc conversion.

Self-organizing maps with dynamic learning for signal reconstruction

Wireless Brain Machine Interface (BMI) communication protocols are faced with the challenge of transmitting the activity of hundreds of neurons which requires large bandwidth. Previously a data compression scheme for neural activity was introduced based on Self Organizing Maps (SOM). In this paper we propose a dynamic learning rule for improved training of the SOM on signals with sparse events which allows for more representative prototype vectors to be found, and consequently better signal reconstruction. This work was developed with BMI applications in mind and therefore our examples are geared towards this type of signals. The simulation results show that the proposed strategy outperforms conventional vector quantization methods for spike reconstruction.