Wirelessly Powered and Bi-Directional Data Communication System With Adaptive Conversion Chain for Multisite Biomedical Implants Over Single Inductive Link.

A wirelessly powered and data communication system is presented which is implemented as a full system, designed for multisite implanted biomedical applications. The system is capable of receiving wireless power and data communication for each implant separately, using inductive links with different resonance frequencies. To achieve this, dual-band coils are presented in the system.

Epileptic Seizure Detection With Patient-Specific Feature and Channel Selection for Low-power Applications.

An accurate epileptic seizure detector using intracranial electroencephalography (iEEG) recordings, suitable for low-power wearable/implantable applications, is presented. Eleven time-domain features with low hardware complexity are employed in the feature pool of the seizure detector. A novel two-step feature ranking algorithm based on maximum discrimination minimum redundancy (MDMR) is proposed to identify the most discriminating features in a patient-specific manner during a training phase.

Dynamic Probabilistic Pruning: A General Framework for Hardware-Constrained Pruning at Different Granularities.

Unstructured neural network pruning algorithms have achieved impressive compression ratios. However, the resulting-typically irregular-sparse matrices hamper efficient hardware implementations, leading to additional memory usage and complex control logic that diminishes the benefits of unstructured pruning. This has spurred structured coarse-grained pruning solutions that prune entire feature maps or even layers, enabling efficient implementation at the expense of reduced flexibility.

Two-stage Hardware-Friendly Epileptic Seizure Detection Method with a Dynamic Feature Selection.

A novel low-complexity method of detecting epileptic seizures from intracranial encephalography (iEEG) signals is presented. In the proposed algorithm, coastline, energy and nonlinear energy features of iEEG signals are extracted in a patient-specific two-stage seizure detection system. The detection stage of the proposed system, which extracts two times more features than the monitoring stage, is only powered on when the monitoring stage detects a seizure occurrence.

A Review of Microelectronic Systems and Circuit Techniques for Electrical Neural Recording Aimed at Closed-Loop Epilepsy Control.

Closed-loop implantable electronics offer a new trend in therapeutic systems aimed at controlling some neurological diseases such as epilepsy. Seizures are detected and electrical stimulation applied to the brain or groups of nerves. To this aim, the signal recording chain must be very carefully designed so as to operate in low-power and low-latency, while enhancing the probability of correct event detection.

Compact, Energy-Efficient High-Frequency Switched Capacitor Neural Stimulator With Active Charge Balancing.

Safety and energy efficiency are two major concerns for implantable neural stimulators. This paper presents a novel high-frequency, switched capacitor (HFSC) stimulation and active charge balancing scheme, which achieves high energy efficiency and well-controlled stimulation charge in the presence of large electrode impedance variations. Furthermore, the HFSC can be implemented in a compact size without any external component to simultaneously enable multichannel stimulation by deploying multiple stimulators.

Compact low-power cortical recording architecture for compressive multichannel data acquisition.

This paper introduces an area- and power-efficient approach for compressive recording of cortical signals used in an implantable system prior to transmission. Recent research on compressive sensing has shown promising results for sub-Nyquist sampling of sparse biological signals. Still, any large-scale implementation of this technique faces critical issues caused by the increased hardware intensity.

Design techniques and analysis of high-resolution neural recording systems targeting epilepsy focus localization.

The design of a high-density neural recording system targeting epilepsy monitoring is presented. Circuit challenges and techniques are discussed to optimize the amplifier topology and the included OTA. A new platform supporting active recording devices targeting wireless and high-resolution focus localization in epilepsy diagnosis is also proposed.

A cell-electrode interface noise model for high-density microelectrode arrays.

A cell-electrode interface noise model is developed which is dedicated to enable the co-simulation of the cell-electrode electrical characteristics, along with the electronics of novel CMOS-based MEA. The electrode noise is investigated for Pt and Pt black electrodes. It is shown that the electrode noise can be the dominant noise source in the full system.

An electrical model of the cell-electrode interface for high-density microelectrode arrays.

A point-contact model is presented, and an area-contact model has been analytically derived in order to model the electrical characteristic of the cell-electrode interface of high-density neuron cultures. The area-contact model is presented as a model more suitable for subcellular multi-electrode resolution, which is a requisite for modeling and simulating the electrical behavior of novel high-density microelectrode arrays. Furthermore, when the electrode is aligned and centered with the cell, an optimum electrode diameter for recording the electrical activity of neural cells can be analytically derived, which is between 7-8 microm with a typical load capacitance of 10 pF.