Analyzing neural signals and providing feedback in realtime is one of the core
characteristics of a brain–computer interface (BCI). As this feature may be employed to …

The application of closed-loop approaches in systems neuroscience and therapeutic
stimulation holds great promise for revolutionizing our understanding of the brain and for …

Classifiers that can be implemented on chip with minimal computational and memory
resources are essential for edge computing in emerging applications such as medical and …

A nonlinear ADC dedicated to the digitization of neural signals in implantable brain-machine
interfaces is presented. Benefitting from an exponential quantization function, effective …

An 8-channel wireless neural signal processing IC, which can perform real-time spike
detection, alignment, and feature extraction, and wireless data transmission is proposed. A …

Reliability, scalability and clinical viability are of utmost importance in the design of wireless
Brain Machine Interface systems (BMIs). This paper reports on the design and …

Brain–machine interfaces (BMIs) may be used to investigate neural mechanisms or to treat
the symptoms of neurological disease and are hence powerful tools in research and clinical …

This paper reports the design of a fully reconfigurable biological signal co-processor,
designed to enhance processing capabilities of a generic controller proposed by the …

Conditioning raw neural signals recorded through microelectrode arrays implanted in the
brain is an important first step before information extraction can take place. This paper …

Intracortical signals are usually affected by high levels of noise [0 dB signal-to-noise ratio
(SNR) is not uncommon] often due to magnetic or electrical coupling between surrounding …