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Electrode grids are used in neuroscience research and clinical practice to record electrical activity from the surface of the brain. However, existing passive electrocorticography (ECoG) technologies are unable to offer both high spatial resolution and wide cortical coverage, while ensuring a compact acquisition system. The electrode count and density are restricted by the fact that each electrode must be individually wired. This work presents an active micro-electrocorticography (µECoG) implant that tackles this limitation by incorporating metal oxide thin-film transistors (TFTs) into a flexible electrode array, allowing to address multiple electrodes through a single shared readout line. By combining the array with an incremental-ΔΣ readout integrated circuit (ROIC), the system is capable of recording from up to 256 electrodes virtually simultaneously, thanks to the implemented 16:1 time-division multiplexing scheme, offering lower noise levels than existing active µECoG arrays. In vivo validation is demonstrated acutely in mice by recording spontaneous activity and somatosensory evoked potentials over a cortical surface of ≈8×8 mm 2 . The proposed neural interface overcomes the wiring bottleneck limiting ECoG arrays, holding promise as a powerful tool for improved mapping of the cerebral cortex and as an enabling technology for future brain-machine interfaces., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

The recording of biopotential signals using techniques such as electroencephalography (EEG) and electrocardiography (ECG) poses important challenges to the design of the front-end readout circuits in terms of noise, electrode DC offset cancellation and motion artifact tolerance. In this paper, we present a 2 nd -order hybrid-CTDT Δ∑-∑ modulator front-end architecture that tackles these challenges by taking advantage of the over-sampling and noise-shaping characteristics of a traditional Δ∑ modulator, while employing an extra ∑-stage in the feedback loop to remove electrode DC offsets and accommodate motion artifacts. To meet the stringent noise requirements of this application, a capacitively-coupled chopper-stabilized amplifier located in the forward path of the modulator loop serves simultaneously as an input stage and an active adder. A prototype of this direct-to-digital front-end chip is fabricated in a standard 0.18-μm CMOS process and achieves a peak SNR of 105.6 dB and a dynamic range of 108.3 dB, for a maximum input range of 720 mV pp . The measured input-referred noise is 0.98 μV rms over a bandwidth of 0.5-100 Hz, and the measured CMRR is >100 dB. ECG and EEG measurements in human subjects demonstrate the capability of this architecture to acquire biopotential signals in the presence of large motion artifacts.

Although CMOS fabrication has enabled a quick evolution in the design of high-density neural probes and neural-recording chips, the scaling and miniaturization of the complete data-acquisition systems has happened at a slower pace. This is mainly due to the complexity and the many requirements that change depending on the specific experimental settings. In essence, the fundamental challenge of a neural-recording system is getting the signals describing the largest possible set of neurons out of the brain and down to data storage for analysis. This requires a complete system optimization that considers the physical, electrical, thermal and signal-processing requirements, while accounting for available technology, manufacturing constraints and budget. Here we present a scalable and open-standards-based open-source data-acquisition system capable of recording from over 10,000 channels of raw neural data simultaneously. The components and their interfaces have been optimized to ensure robustness and minimum invasiveness in small-rodent electrophysiology.

In vivo recording of neural action-potential and local-field-potential signals requires the use of high-resolution penetrating probes. Several international initiatives to better understand the brain are driving technology efforts towards maximizing the number of recording sites while minimizing the neural probe dimensions. We designed and fabricated (0.13- μm SOI Al CMOS) a 384-channel configurable neural probe for large-scale in vivo recording of neural signals. Up to 966 selectable active electrodes were integrated along an implantable shank (70 μm wide, 10 mm long, 20  μm thick), achieving a crosstalk of [Formula: see text] dB. The probe base (5 × 9 mm 2 ) implements dual-band recording and a 171.6 Mbps digital interface. Measurement results show a total input-referred noise of 6.4 μ V rms and a total power consumption of 49.1  μW/channel.

Since a few decades, micro-fabricated neural probes are being used, together with microelectronic interfaces, to get more insight in the activity of neuronal networks. The need for higher temporal and spatial recording resolutions imposes new challenges on the design of integrated neural interfaces with respect to power consumption, data handling and versatility. In this paper, we present an integrated acquisition system for in vitro and in vivo recording of neural activity. The ASIC consists of 16 low-noise, fully-differential input channels with independent programmability of its amplification (from 100 to 6000 V/V) and filtering (1-6000 Hz range) capabilities. Each channel is AC-coupled and implements a fourth-order band-pass filter in order to steeply attenuate out-of-band noise and DC input offsets. The system achieves an input-referred noise density of 37 nV/√Hz, a NEF of 5.1, a CMRR > 60 dB, a THD < 1% and a sampling rate of 30 kS/s per channel, while consuming a maximum of 70 μA per channel from a single 3.3 V. The ASIC was implemented in a 0.35 μm CMOS technology and has a total area of 5.6 × 4.5 mm². The recording system was successfully validated in in vitro and in vivo experiments, achieving simultaneous multichannel recordings of cell activity with satisfactory signal-to-noise ratios.