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Integrated recording of neural electrical potentials from the brain poses great challenges due to stringent dynamic range requirements to resolve small-signal amplitudes buried in noise amidst large artifact and stimulation transients, as well as stringent power and volume constraints to enable minimally invasive untethered operation. Here, we present a 16-channel neural recording system-on-chip with greater than 90-dB input dynamic range and less than 1- $\mu \text{V}_{\mathrm {rms}}$ input-referred noise from dc to 500 Hz, at 0.8- $\mu \text{W}$ power consumption, and 0.024-mm2 area per channel in a 65-nm CMOS process. Each recording channel features a hybrid analog–digital second-order oversampling analog-to-digital converter (ADC), with the biopotential signal coupling directly to the second integrator for high conversion gain and dynamic offset subtraction in the digital domain. This bypasses the need for high-pass filtering pre-amplification in neural recording systems, which often leads to signal distortion. The integrated ADC-direct neural recording offers record figure-of-merit with a noise efficiency factor (NEF) of the combined front end and ADC of 1.81, and a corresponding power efficiency factor (PEF) of 2.6. Predictive digital autoranging of the binary quantizer further supports rapid transient recovery while maintaining fully dc-coupled operation. Hence, the neural ADC is capable of recording ${\le}$ 0.01-Hz slow potentials as well as recovering from $\ge$ 200-mVpp transients within ${\le}$ 1 ms that are important prerequisites to effective electrocortical recording for brain activity mapping. In vivo recordings from marmoset primate frontal cortex demonstrate its unique capabilities in resolving ultra-slow local field potentials indicative of subject arousal state.

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI.