A Wireless Neural Stimulator IC for Cortical Visual Prosthesis.

We propose a 0.25 × 0.25 × 0.3 mm (~0.02 mm3) optically powered mote for visual cortex stimulation to restore vision. Up to 1024 implanted motes can be individually addressed. The complete StiMote system was confirmed fully functional when optically powered and cortex stimulation was confirmed in-vivo with a live rat brain.

A Light-Tolerant Wireless Neural Recording IC for Motor Prediction With Near-Infrared-Based Power and Data Telemetry.

Miniaturized and wireless near-infrared (NIR) based neural recorders with optical powering and data telemetry have been introduced as a promising approach for safe long-term monitoring with the smallest physical dimension among state-of-the-art standalone recorders. However, a main challenge for the NIR based neural recording ICs is to maintain robust operation in the presence of light-induced parasitic short circuit current from junction diodes. This is especially true when the signal currents are kept small to reduce power consumption. In this work, we present a light-tolerant and low-power neural recording IC for motor prediction that can fully function in up to 300 µW/mm2 of light exposure. It achieves best-in-class power consumption of 0.57 µW at 38° C with a 4.1 NEF pseudo-resistorless amplifier, an on-chip neural feature extractor, and individual mote level gain control. Applying the 20-channel pre-recorded neural signals of a monkey, the IC predicts finger position and velocity with correlation coefficient up to 0.870 and 0.569, respectively, with individual mote level gain control enabled. In addition, wireless measurement is demonstrated through optical power and data telemetry using a custom PV/LED GaAs chip wire bonded to the proposed IC.

A low-power communication scheme for wireless, 1000 channel brain-machine interfaces.

Objective. Brain-machine interfaces (BMIs) have the potential to restore motor function but are currently limited by electrode count and long-term recording stability. These challenges may be solved through the use of free-floating 'motes' which wirelessly transmit recorded neural signals, if power consumption can be kept within safe levels when scaling to thousands of motes. Here, we evaluated a pulse-interval modulation (PIM) communication scheme for infrared (IR)-based motes that aims to reduce the wireless data rate and system power consumption.Approach. To test PIM's ability to efficiently communicate neural information, we simulated the communication scheme in a real-time closed-loop BMI with non-human primates. Additionally, we performed circuit simulations of an IR-based 1000-mote system to calculate communication accuracy and total power consumption.Main results. We found that PIM at 1 kb/s per channel maintained strong correlations with true firing rate and matched online BMI performance of a traditional wired system. Closed-loop BMI tests suggest that lags as small as 30 ms can have significant performance effects. Finally, unlike other IR communication schemes, PIM is feasible in terms of power, and neural data can accurately be recovered on a receiver using 3 mW for 1000 channels.Significance.These results suggest that PIM-based communication could significantly reduce power usage of wireless motes to enable higher channel-counts for high-performance BMIs.

A Power-Efficient Brain-Machine Interface System With a Sub-mw Feature Extraction and Decoding ASIC Demonstrated in Nonhuman Primates.

Intracortical brain-machine interfaces have shown promise for restoring function to people with paralysis, but their translation to portable and implantable devices is hindered by their high power consumption. Recent devices have drastically reduced power consumption compared to standard experimental brain-machine interfaces, but still require wired or wireless connections to computing hardware for feature extraction and inference. Here, we introduce a Neural Recording And Decoding (NeuRAD) application specific integrated circuit (ASIC) in 180 nm CMOS that can extract neural spiking features and predict two-dimensional behaviors in real-time. To reduce amplifier and feature extraction power consumption, the NeuRAD has a hardware accelerator for extracting spiking band power (SBP) from intracortical spiking signals and includes an M0 processor with a fixed-point Matrix Acceleration Unit (MAU) for efficient and flexible decoding. We validated device functionality by recording SBP from a nonhuman primate implanted with a Utah microelectrode array and predicting the one- and two-dimensional finger movements the monkey was attempting to execute in closed-loop using a steady-state Kalman filter (SSKF). Using the NeuRAD's real-time predictions, the monkey achieved 100% success rate and 0.82 s mean target acquisition time to control one-dimensional finger movements using just 581 µW. To predict two-dimensional finger movements, the NeuRAD consumed 588 µW to enable the monkey to achieve a 96% success rate and 2.4 s mean acquisition time. By employing SBP, ASIC brain-machine interfaces can close the gap to enable fully implantable therapies for people with paralysis.

Bridging the"Last Millimeter" Gap of Brain-Machine Interfaces via Near-Infrared Wireless Power Transfer and Data Communications.

Arrays of floating neural sensors with high channel count that cover an area of square centimeters and larger would be transformative for neural engineering and brain-machine interfaces. Meeting the power and wireless data communications requirements within the size constraints for each neural sensor has been elusive due to the need to incorporate sensing, computing, communications, and power functionality in a package of approximately 100 micrometers on a side. In this work, we demonstrate a near infrared optical power and data communication link for a neural recording system that satisfies size requirements to achieve dense arrays and power requirements to prevent tissue heating. The optical link is demonstrated using an integrated optoelectronic device consisting of a tandem photovoltaic cell and microscale light emitting diode. End-to-end functionality of a wireless neural link within system constraints is demonstrated using a pre-recorded neural signal between a self-powered CMOS integrated circuit and single photon avalanche photodiode.

A Light Tolerant Neural Recording IC for Near-Infrared-Powered Free Floating Motes.

A key challenge for near-infrared (NIR) powered neural recording ICs is to maintain robust operation in the presence of parasitic short circuit current from junction diodes when exposed to light. This is especially so when intentional currents are kept small to reduce power consumption. We present a neural recording IC that is tolerant up to 300 µW/mm2 light exposure (above tissue limit) and consumes 0.57 µW at 38°C, making it lowest power among standalone motes while incorporating on-chip feature extraction and individual gain control.

Dual-Junction GaAs Photovoltaics for Low Irradiance Wireless Power Transfer in Submillimeter-Scale Sensor Nodes.

Dual-junction GaAs photovoltaic (PV) cells and modules at sub millimeter scale are demonstrated for efficient wireless power transfer for Internet of Things (IoT) and bio-implantable applications under low-flux illumination. The dual-junction approach meets demanding requirements for these applications by increasing the output voltage per cell with reduced area losses from isolation and interconnects. A single PV cell (150 µm × 150 µm) based on the dual-junction design demonstrates power conversion efficiency above 22% with greater than 1.2 V output voltage under low-flux 850 nm near-infrared LED illumination at 6.62 µW/mm2, which is sufficient for batteryless operation of miniaturized CMOS IC chips. The output voltage of dual-junction PV modules with 4 series-connected cells demonstrates greater than 5 V for direct battery charging while maintaining a module power conversion efficiency of more than 23%.