A Low-Power Integrated Circuit for a Wireless 100-Electrode Neural Recording System
26 Dec 2006-Vol. 42, Iss: 1, pp 123-133
TL;DR: A prototype integrated circuit for wireless neural recording from a 100-channel microelectrode array was developed and a two-chip system was used to record neural signals from a Utah Electrode Array in cat cortex and transmit the digitized signals wirelessly to a receiver.
Abstract: Recent work in field of neuroprosthetics has demonstrated that by observing the simultaneous activity of many neurons in specific regions of the brain, it is possible to produce control signals that allow animals or humans to drive cursors or prosthetic limbs directly through thoughts. As neuroprosthetic devices transition from experimental to clinical use, there is a need for fully-implantable amplification and telemetry electronics in close proximity to the recording sites. To address these needs, we developed a prototype integrated circuit for wireless neural recording from a 100-channel microelectrode array. The design of both the system-level architecture and the individual circuits were driven by severe power constraints for small implantable devices; chronically heating tissue by only a few degrees Celsius leads to cell death. Due to the high data rate produced by 100 neural signals, the system must perform data reduction as well. We use a combination of a low-power ADC and an array of "spike detectors" to reduce the transmitted data rate while preserving critical information. The complete system receives power and commands (at 6.5 kb/s) wirelessly over a 2.64-MHz inductive link and transmits neural data back at a data rate of 330 kb/s using a fully-integrated 433-MHz FSK transmitter. The 4.7times5.9 mm2 chip was fabricated in a 0.5-mum 3M2P CMOS process and consumes 13.5 mW of power. While cross-chip interference limits performance in single-chip operation, a two-chip system was used to record neural signals from a Utah Electrode Array in cat cortex and transmit the digitized signals wirelessly to a receiver
Citations
More filters
TL;DR: This work outlined the theoretical foundation of optimal power transmission efficiency in an inductive link, and combined it with semi-empirical models to predict parasitic components in PSCs to devise an iterative PSC design methodology that starts with a set of realistic design constraints and ends with the optimal PSC pair geometries.
Abstract: The next generation of implantable high-power neuroprosthetic devices such as visual prostheses and brain computer interfaces are going to be powered by transcutaneous inductive power links formed between a pair of printed spiral coils (PSC) that are batch-fabricated using micromachining technology. Optimizing the power efficiency of the wireless link is imperative to minimize the size of the external energy source, heating dissipation in the tissue, and interference with other devices. Previous design methodologies for coils made of 1-D filaments are not comprehensive and accurate enough to consider all geometrical aspects of PSCs with planar 3-D conductors as well as design constraints imposed by implantable device application and fabrication technology. We have outlined the theoretical foundation of optimal power transmission efficiency in an inductive link, and combined it with semi-empirical models to predict parasitic components in PSCs. We have used this foundation to devise an iterative PSC design methodology that starts with a set of realistic design constraints and ends with the optimal PSC pair geometries. We have executed this procedure on two design examples at 1 and 5 MHz achieving power transmission efficiencies of 41.2% and 85.8%, respectively, at 10-mm spacing. All results are verified with simulations using a commercial field solver (HFSS) as well as measurements using PSCs fabricated on printed circuit boards.
616 citations
Cites methods from "A Low-Power Integrated Circuit for ..."
...Digital Object Identifier 10.1109/TBCAS.2007.913130 inside the eyeball through a 5-mm incision [2], and invasive brain–machine interfaces are being developed for the 1 3 mm epidural spacing between the outer surface of the brain and the skull [3]....
[...]
TL;DR: It is shown that despite achieving high PTE at larger coil separations, the 4-coil inductive links fail to achieve a high PDL, and an iterative design methodology is devised that provides the optimal coil geometries in a 3-coils inductive power transfer link.
Abstract: Inductive power transmission is widely used to energize implantable microelectronic devices (IMDs), recharge batteries, and energy harvesters. Power transfer efficiency (PTE) and power delivered to the load (PDL) are two key parameters in wireless links, which affect the energy source specifications, heat dissipation, power transmission range, and interference with other devices. To improve the PTE, a 4-coil inductive link has been recently proposed. Through a comprehensive circuit-based analysis that can guide a design and optimization scheme, we have shown that despite achieving high PTE at larger coil separations, the 4-coil inductive links fail to achieve a high PDL. Instead, we have proposed a 3-coil inductive power transfer link with comparable PTE over its 4-coil counterpart at large coupling distances, which can also achieve high PDL. We have also devised an iterative design methodology that provides the optimal coil geometries in a 3-coil inductive power transfer link. Design examples of 2-, 3-, and 4-coil inductive links have been presented, and optimized for a 13.56-MHz carrier frequency and 12-cm coupling distance, showing PTEs of 15%, 37%, and 35%, respectively. At this distance, the PDL of the proposed 3-coil inductive link is 1.5 and 59 times higher than its equivalent 2- and 4-coil links, respectively. For short coupling distances, however, 2-coil links remain the optimal choice when a high PDL is required, while 4-coil links are preferred when the driver has large output resistance or small power is needed. These results have been verified through simulations and measurements.
537 citations
TL;DR: The amplifier appears to be the lowest power and most energy-efficient neural recording amplifier reported to date and the low-noise design techniques that help the neural amplifier achieve input-referred noise that is near the theoretical limit of any amplifier using a differential pair as an input stage.
Abstract: This paper describes an ultralow-power neural recording amplifier. The amplifier appears to be the lowest power and most energy-efficient neural recording amplifier reported to date. We describe low-noise design techniques that help the neural amplifier achieve input-referred noise that is near the theoretical limit of any amplifier using a differential pair as an input stage. Since neural amplifiers must include differential input pairs in practice to allow robust rejection of common-mode and power supply noise, our design appears to be near the optimum allowed by theory. The bandwidth of the amplifier can be adjusted for recording either neural spikes or local field potentials (LFPs). When configured for recording neural spikes, the amplifier yielded a midband gain of 40.8 dB and a -3-dB bandwidth from 45 Hz to 5.32 kHz; the amplifier's input-referred noise was measured to be 3.06 muVrms while consuming 7.56 muW of power from a 2.8-V supply corresponding to a noise efficiency factor (NEF) of 2.67 with the theoretical limit being 2.02. When configured for recording LFPs, the amplifier achieved a midband gain of 40.9 dB and a -3-dB bandwidth from 392 mHz to 295 Hz; the input-referred noise was 1.66 muVrms while consuming 2.08 muW from a 2.8-V supply corresponding to an NEF of 3.21. The amplifier was fabricated in AMI's 0.5-mum CMOS process and occupies 0.16 mm2 of chip area. We obtained successful recordings of action potentials from the robust nucleus of the arcopallium (RA) of an anesthesized zebra finch brain with the amplifier. Our experimental measurements of the amplifier's performance including its noise were in good accord with theory and circuit simulations.
463 citations
Cites background from "A Low-Power Integrated Circuit for ..."
...Many designs of neural amplifiers have been reported in the literature [5]–[9]....
[...]
TL;DR: An overview of state-of-the-art microelectrode technologies provided, with focus on the material properties of these microdevices for the development of long-lasting functional neural interfaces is provided.
Abstract: Recent advances in nanotechnology have generated wide interest in applying nanomaterials for neural prostheses. An ideal neural interface should create seamless integration into the nervous system and performs reliably for long periods of time. As a result, many nanoscale materials not originally developed for neural interfaces become attractive candidates to detect neural signals and stimulate neurons. In this comprehensive review, an overview of state-of-the-art microelectrode technologies provided fi rst, with focus on the material properties of these microdevices. The advancements in electro active nanomaterials are then reviewed, including conducting polymers, carbon nanotubes, graphene, silicon nanowires, and hybrid organic-inorganic nanomaterials, for neural recording, stimulation, and growth. Finally, technical and scientific challenges are discussed regarding biocompatibility, mechanical mismatch, and electrical properties faced by these nanomaterials for the development of long-lasting functional neural interfaces.
461 citations
TL;DR: This work has analyzed the PTE of a pair of capacitively loaded inductors via reflected load theory (RLT) and compared it with a method known as coupled-mode theory (CMT) and proven that both methods basically result in the same set of equations in steady state and either method can be applied for short- or midrange coupling conditions.
Abstract: Inductive coupling is a viable scheme to wirelessly energize devices with a wide range of power requirements from nanowatts in radio frequency identification tags to milliwatts in implantable microelectronic devices, watts in mobile electronics, and kilowatts in electric cars. Several analytical methods for estimating the power transfer efficiency (PTE) across inductive power transmission links have been devised based on circuit and electromagnetic theories by electrical engineers and physicists, respectively. However, a direct side-by-side comparison between these two approaches is lacking. Here, we have analyzed the PTE of a pair of capacitively loaded inductors via reflected load theory (RLT) and compared it with a method known as coupled-mode theory (CMT). We have also derived PTE equations for multiple capacitively loaded inductors based on both RLT and CMT. We have proven that both methods basically result in the same set of equations in steady state and either method can be applied for short- or midrange coupling conditions. We have verified the accuracy of both methods through measurements, and also analyzed the transient response of a pair of capacitively loaded inductors. Our analysis shows that the CMT is only applicable to coils with high quality factor (Q) and large coupling distance. It simplifies the analysis by reducing the order of the differential equations by half compared to the circuit theory.
387 citations
References
More filters
TL;DR: Initial results for a tetraplegic human using a pilot NMP suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis.
Abstract: Neuromotor prostheses (NMPs) aim to replace or restore lost motor functions in paralysed humans by routeing movement-related signals from the brain, around damaged parts of the nervous system, to external effectors. To translate preclinical results from intact animals to a clinically useful NMP, movement signals must persist in cortex after spinal cord injury and be engaged by movement intent when sensory inputs and limb movement are long absent. Furthermore, NMPs would require that intention-driven neuronal activity be converted into a control signal that enables useful tasks. Here we show initial results for a tetraplegic human (MN) using a pilot NMP. Neuronal ensemble activity recorded through a 96-microelectrode array implanted in primary motor cortex demonstrated that intended hand motion modulates cortical spiking patterns three years after spinal cord injury. Decoders were created, providing a ‘neural cursor’ with which MN opened simulated e-mail and operated devices such as a television, even while conversing. Furthermore, MN used neural control to open and close a prosthetic hand, and perform rudimentary actions with a multi-jointed robotic arm. These early results suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis. The cover shows Matt Nagle, first participant in the BrainGate pilot clinical trial. He is unable to move his arms or legs following cervical spinal cord injury. Researchers at the Department of Neuroscience at Brown University, working with biotech company Cyberkinetics and 3 other institutions, demonstrate that movement-related signals can be relayed from the brain via an implanted BrainGate chip, allowing the patient to drive a computer screen cursor and activate simple robotic devices. Such neuromotor prostheses could pave the way towards systems to replace or restore lost motor function in paralysed humans. Prior to this advance, this type of work has been performed chiefly in monkeys. The latest example of such research has achieved a large increase in speed over current devices, enhancing the prospects for clinically viable brain-machine interfaces.
3,120 citations
TL;DR: In this paper, a co-adaptive algorithm uses the firing rate of the sensed neurons or neuron groupings to help develop the control signals for an object is developed from the neuron-originating electrical impulses detected by electrode arrays implanted in a subject's cerebral cortex at the pre-motor locations known to have association with arm movements.
Abstract: Control signals for an object are developed from the neuron-originating electrical impulses detected by electrode arrays (28) implanted in a subject's cerebral cortex at the pre-motor locations known to have association with arm movements. A co-adaptive algorithm uses the firing rate of the electrode sensed neurons or neuron groupings to help develop the control signals. In a closed-loop environment, where the animal subject (10) can view its results (34), weighting factors in the algorithm are modified over a series of tests to emphasize cortical electrical impulses that result in movement of the object (40) as desired. At the same time, the animal subject (10) learns and modifies its cortical electrical activity to achieve movement of the object (40) as desired. In one specific embodiment, the object moved was a cursor portrayed as a sphere (40) in a virtual reality display (32, 34). In another embodiment, a robot arm (not shown) was controlled by an animal subject (10).
1,604 citations
TL;DR: In this article, a low-noise low-power biosignal amplifiers capable of amplifying signals in the millihertz-to-kilohertz range while rejecting large dc offsets generated at the electrode-tissue interface is presented.
Abstract: There is a need among scientists and clinicians for low-noise low-power biosignal amplifiers capable of amplifying signals in the millihertz-to-kilohertz range while rejecting large dc offsets generated at the electrode-tissue interface. The advent of fully implantable multielectrode arrays has created the need for fully integrated micropower amplifiers. We designed and tested a novel bioamplifier that uses a MOS-bipolar pseudoresistor element to amplify low-frequency signals down to the millihertz range while rejecting large dc offsets. We derive the theoretical noise-power tradeoff limit - the noise efficiency factor - for this amplifier and demonstrate that our VLSI implementation approaches this limit by selectively operating MOS transistors in either weak or strong inversion. The resulting amplifier, built in a standard 1.5-/spl mu/m CMOS process, passes signals from 0.025Hz to 7.2 kHz with an input-referred noise of 2.2 /spl mu/Vrms and a power dissipation of 80 /spl mu/W while consuming 0.16 mm/sup 2/ of chip area. Our design technique was also used to develop an electroencephalogram amplifier having a bandwidth of 30 Hz and a power dissipation of 0.9 /spl mu/W while maintaining a similar noise-power tradeoff.
1,572 citations
TL;DR: The results suggest that long-term control of complex prosthetic robot arm movements can be achieved by simple real-time transformations of neuronal population signals derived from multiple cortical areas in primates.
Abstract: Signals derived from the rat motor cortex can be used for controlling one-dimensional movements of a robot arm1. It remains unknown, however, whether real-time processing of cortical signals can be employed to reproduce, in a robotic device, the kind of complex arm movements used by primates to reach objects in space. Here we recorded the simultaneous activity of large populations of neurons, distributed in the premotor, primary motor and posterior parietal cortical areas, as non-human primates performed two distinct motor tasks. Accurate real-time predictions of one- and three-dimensional arm movement trajectories were obtained by applying both linear and nonlinear algorithms to cortical neuronal ensemble activity recorded from each animal. In addition, cortically derived signals were successfully used for real-time control of robotic devices, both locally and through the Internet. These results suggest that long-term control of complex prosthetic robot arm movements can be achieved by simple real-time transformations of neuronal population signals derived from multiple cortical areas in primates.
1,435 citations
TL;DR: In this paper, the activity from a few motor cortex neurons can be decoded into a signal that a monkey is able to use immediately to move a computer cursor to any new position in its workspace.
Abstract: Hands-free operation of a cursor can be achieved by a few neurons in the motor cortex. The activity of motor cortex (MI) neurons conveys movement intent sufficiently well to be used as a control signal to operate artificial devices1,2,3, but until now this has called for extensive training or has been confined to a limited movement repertoire2,3. Here we show how activity from a few (7–30) MI neurons can be decoded into a signal that a monkey is able to use immediately to move a computer cursor to any new position in its workspace (14° × 14° visual angle). Our results, which are based on recordings made by an electrode array that is suitable for human use4,5, indicate that neurally based control of movement may eventually be feasible in paralysed humans.
1,314 citations