Intracortical brain-computer interfaces offer superior spatial and temporal resolutions, but face challenges as the increasing number of recording channels introduces high amounts of data to be transferred. This requires power-hungry data serialization and telemetry, leading to potential tissue damage risks. To address this challenge, this paper introduces an event-based neural compressive telemetry (NCT) consisting of 8 channel-rotating Δ-ADCs, an event-driven serializer supporting a proposed ternary address event representation protocol, and an event-based LVDS driver. Leveraging a high sparsity of extracellular spikes and high spatial correlation of the high-density recordings, the proposed NCT achieves a compression ratio of >11.4×, while consumes only 1 µW per channel, which is 127× more efficient than state of the art. The NCT well preserves the spike waveform fidelity, and has a low normalized RMS error <23% even with a spike amplitude down to only 31 µV.

This article presents a networked wireless telemetry for 384-sensor-pixel neural data acquisition, designed to support high throughput, low power consumption, and meter-scale communication distance. The proposed event-based Address-Event- $\mu $ -Packet (AE $\mu $ P) encoder with differential binary phase shift keying (DBPSK) modulation addresses the challenging throughput-power trade-off for neural telemetry without compromising transmission range and neural signal fidelity. The encoder compresses data and minimizes protocol overhead as well as memory access. In addition, a duty-cycling impulse-radio ultra-wideband (IR-UWB) transmitter is proposed as a communication link for the event-based encoder. To support the multiaccess operation, a duty-cycling frequency-locked loop (FLL) is adopted to provide fast-settling packet-level frequency control for frequency division multiplexing (FDM). The FLL achieves warm-start lock within $1~\mu $ s with 2.73-MHz rms variation, meeting the frequency accuracy required for DBPSK at a 125-MHz symbol rate. The measured three-access-node spectra comply with Federal Communications Commission (FCC)/ETSI masks in 6–8.5GHz. The neural signal integrity is validated by evaluating the rms error between the reconstruction signals and the original in vivo data captured by a high-pixel-count Neuropixels probe. The presented AE $\mu $ P achieves an $11.52\times $ compression ratio. Fabricated in 65nm CMOS with an active area of 0.12mm², the presented telemetry is capable of transmitting 384 pixels of neural data with low compression error (3.6- $\mu $ Vrms), at record-low power of 1mW, and with a 6.6-m air communication range.

Intracortical brain computer interfaces hold the potential to revolutionize neurotherapeutics, but they must overcome technological challenges such as the high data rates generated by high-channel-count neural sensors and the stringent power and volume constraints of implantable devices. In addition, the brain-wide coverage needed for a deeper understanding of brain processes challenges the synchronization between distributed neural sensors and the central neural hub. To address these challenges, we present a deterministic-latency and power-efficient serializer–deserializer (SerDes) telemetry network that effectively mitigates the synchronization issue under strict power and volume constraints. The serializer on the sensor side employs event-based sampling and a packet-based address-event representation transmission protocol, achieving a low power consumption of only 127 µ W and a low latency variation <10 µ s. A crystal-free clock source is employed on the sensor side to minimize power consumption, with serialized data encoded using Manchester coding scheme. The deserializer on the hub handles the bit period uncertainty by counting and extracting the bit period of received data with a clock only ∼2.2× faster than the serializer clock. The proposed counting-based Manchester decoder achieves a wide frequency coverage up to 204 000 ppm of frequency variation. The deserializer achieves a measured Manchester decoding bit error rate (BER <10 ⁻⁶ ), with a total estimated power consumption below 415 µ W. The SerDes performance has been validated with in vivo pre-recorded data, demonstrating a compression ratio greater than 7, while preserving a high signal fidelity with an average RMSE <6 µ V _RMS .

Intracortical brain-computer interfaces (iBCIs) hold promise for restoring motor, sensory, and cognitive functions, including applications in paralysis treatment and speech decoding. High-density microelectrode arrays (MEAs) provide fine spatial and temporal discrimination of neural activity, but the considerable upsurge in data generation poses challenges for wireless transmission from miniaturized implants due to constraints on power, bandwidth, heat dissipation, and device size. To address these constraints, we propose a two-stage wireless iBCI architecture comprising a transdural galvanic-coupled body channel communication (BCC) link from a free-floating MEA to an intracranial unit, followed by a transcutaneous link to an external unit. This study focuses on the transdural BCC telemetry system, which provides compact, wideband, and energy-efficient data transmission. Phantom tests, and ex vivo experiments using a human cadaveric head specimen validate the system, demonstrating wireless transmission up to 500 Mbps with 20% duty cycling and bit error rates below 10⁻⁵. The system incorporates the send-on-delta encoder (SODA), achieving up to 11.4× data compression and reducing thermal load for meeting the safety guidelines. Safety is further examined using brain-on-a-chip models, which demonstrate that the system does not evoke unintended neural activity, supporting the platform’s long-term viability for high-resolution iBCIs.