Multichannel neural recording with a 128 Mbps UWB wireless transmitter for implantable brain-machine interfaces

A Multi-Channel Neural Recording System with Adaptive Electrode Selection for High-Density Neural Interface

There is an increasing demand for real-time neural signal monitoring from a large number of electrode contacts to provide adequate spatial and temporal resolution for brain mapping and high-resolution neural interface. This paper proposes a novel multi-channel neural recording system that records neural signals from a large number of electrodes with a smaller number of recording channels. The system utilizes an adaptive electrode selection technique to automatically scan the electrode arrays and record from selected electrodes where neural spikes are detected. The proposed neural recording IC was fabricated in CMOS 180 nm process and tested with in vitro environments. Experiment results with pre-recorded neural data indicate that neural spikes can be separated and amplified with the proposed system and counted in real-time.

Virtual Human Models for Electromagnetic Studies and Their Applications

Numerical simulation of electromagnetic, thermal, and mechanical responses of the human body to different stimuli in magnetic resonance imaging safety, antenna research, electromagnetic tomography, and electromagnetic stimulation is currently limited by the availability of anatomically adequate and numerically efficient cross-platform computational models or "virtual humans." The objective of this study is to provide a comprehensive review of modern human models and body region models available in the field and their important features.

A testbed for optimizing electrodes embedded in the skull or in artificial skull replacement pieces used after injury

BACKGROUND

Custom-fitted skull replacement pieces are often used after a head injury or surgery to replace damaged bone. Chronic brain recordings are beneficial after injury/surgery for monitoring brain health and seizure development. Embedding electrodes directly in these artificial skull replacement pieces would be a novel, low-risk way to perform chronic brain monitoring in these patients. Similarly, embedding electrodes directly in healthy skull would be a viable minimally-invasive option for many other neuroscience and neurotechnology applications requiring chronic brain recordings.

NEW METHOD

We demonstrate a preclinical testbed that can be used for refining electrode designs embedded in artificial skull replacement pieces or for embedding directly into the skull itself. Options are explored to increase the surface area of the contacts without increasing recording contact diameter to maximize recording resolution.

RESULTS

Embedding electrodes in real or artificial skull allows one to lower electrode impedance without increasing the recording contact diameter by making use of conductive channels that extend into the skull. The higher density of small contacts embedded in the artificial skull in this testbed enables one to optimize electrode spacing for use in real bone.

COMPARISON WITH EXISTING METHODS

For brain monitoring applications, skull-embedded electrodes fill a gap between electroencephalograms recorded on the scalp surface and the more invasive epidural or subdural electrode sheets.

CONCLUSIONS

Embedding electrodes into the skull or in skull replacement pieces may provide a safe, convenient, minimally-invasive alternative for chronic brain monitoring. The manufacturing methods described here will facilitate further testing of skull-embedded electrodes in animal models.

Feasibility of an ultra-low power digital signal processor platform as a basis for a fully implantable brain-computer interface system

A fully implantable brain-computer interface (BCI) can be a practical tool to restore independence to those affected by spinal cord injury. We envision that such a BCI system will invasively acquire brain signals (e.g. electrocorticogram) and translate them into control commands for external prostheses. The feasibility of such a system was tested by implementing its benchtop analogue, centered around a commercial, ultra-low power (ULP) digital signal processor (DSP, TMS320C5517, Texas Instruments). A suite of signal processing and BCI algorithms, including (de)multiplexing, Fast Fourier Transform, power spectral density, principal component analysis, linear discriminant analysis, Bayes rule, and finite state machine was implemented and tested in the DSP. The system's signal acquisition fidelity was tested and characterized by acquiring harmonic signals from a function generator. In addition, the BCI decoding performance was tested, first with signals from a function generator, and subsequently using human electroencephalogram (EEG) during eyes opening and closing task. On average, the system spent 322 ms to process and analyze 2 s of data. Crosstalk (<;-65 dB) and harmonic distortion (~1%) were minimal. Timing jitter averaged 49 μs per 1000 ms. The online BCI decoding accuracies were 100% for both function generator and EEG data. These results show that a complex BCI algorithm can be executed on an ULP DSP without compromising performance. This suggests that the proposed hardware platform may be used as a basis for future, fully implantable BCI systems.