A 32-Channel Time-Multiplexed Artifact-Aware Neural Recording System

Abstract

This paper presents a low-power, low-noise microsystem for the recording of neural local field potentials or intracranial electroencephalographic signals. It features 32 timemultiplexed channels at the electrode interface and offers the possibility to spatially delta encode data to take advantage of the large correlation of signals captured from nearby channels. The circuit also implements a mixed-signal voltage-triggered autoranging algorithm which allows to attenuate large interferers in digital domain while preserving neural information. This effectively increases the system dynamic range and avoids the onset of saturation. A prototype, fabricated in a standard 180 nm CMOS process, has been experimentally verified in-vitro with cellular cultures of primary cortical neurons from mice. The system shows an integrated input-referred noise in the 0.5-200 Hz band of 1.4 µVrms for a spot noise of about 85 nV/ √ Hz. The system draws 1.5 µW per channel from 1.2 V supply and obtains 71 dB + 26 dB dynamic range when the artifact-aware autoranging mechanism is enabled, without penalising other critical specifications such as crosstalk between channels or commonmode and power supply rejection ratios.

Power-to-Noise Optimization in the Design of Neural Recording Amplifier Based on Current Scaling, Source Degeneration Resistor, and Current Reuse

“…The PEF can be calculated as And the PEF of the amplifier is calculated to be 10.17. Figure 11 [6,7,9,13,[15][16][17][20][21][22][23][24][25][26][27][28][29][30][31] shows the input-referred noise versus the supply current of the amplifier. The proposed work features a low input-referred noise while achieving a competitive NEF.…”

Detection and Removal of Hyper-synchronous Artifacts in Massively Parallel Spike Recordings

“…Since the placement of the individual channels in the connector to the head stage does not seem to explain the cross-talk Figure S2, the circuitry inside the headstage is our primary suspect for cross-talk. There the analog signals from many channels are simultaneously amplified, filtered and converted to digital; steps known to be very sensitive to cross-talk (Pérez-Prieto & Delgado-Restituto, 2021; Perez-Prieto et al, 2021). Indeed, in the case of macaque Y, previous recordings had used the analog signal processing headstage ‘Samtec’ and ‘Patientcable’ (Blackrock Microsystems) (Riehle et al, 2013; Brochier et al, 2018), and after updating it to ‘Cereplex E’ (Blackrock Microsystems) we observed far fewer artifacts, likely due to the improved insulation and circuit design.…”

“…Several methods have been suggested to remove the synchrofacts from neural recordings. Hardware implementations can avoid cross-talk by improving the isolation and design of the circuits (Blot & Barbour, 2014; Nelson et al, 2017; Pérez-Prieto & Delgado-Restituto, 2021; Perez-Prieto et al, 2021). Most neuroscientists do not design their own custom hardware, and hence have to use post-hoc methods to remove the synchrofacts from their data.…”

“…Yet, to the best of our knowledge, only a few studies have focused on cross-talk in electrophysiology systems (Musial et al, 2002; Nelson et al, 2017; Pérez-Prieto & Delgado-Restituto, 2021). One approach to avoid cross-talk in these systems is through hardware implementations, like improving the isolation and design of the circuits (Blot & Barbour, 2014; Nelson et al, 2017; Pérez-Prieto & Delgado-Restituto, 2021; Perez-Prieto et al, 2021). However, most neuroscientists do not design their own custom hardware and thus the effects of cross-talk have to be mitigated with post-hoc methods.…”

Recent Trends and Future Prospects of Neural Recording Circuits and Systems: A Tutorial Brief

“…2(b) could significantly reduce the number of amplifiers and ADCs. Similar recording architectures are also explored in [7], [8]. This multiplexing technique can also be co-designed with direct digitalization converters explained in the following sections.…”