Sensing enabled implantable devices and next-generation neurotechnology allow real-time
adjustments of invasive neuromodulation. The identification of symptom and disease …

Highlights•Neural interfaces continue to improve in channel count and form factor.•Low-
power machine learning and neuromorphic processors can be integrated onto neural …

Closed-loop neural interfaces with on-chip machine learning can detect and suppress
disease symptoms in neurological disorders or restore lost functions in paralyzed patients …

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

Closed-loop neuromodulation can alleviate disease symptoms and provide sensory
feedback in various neurological disorders and injuries [1]. Energy-efficient realization of …

This paper presents a trainable hybrid approach involving a shallow autoencoder (AE) and
a conventional classifier for epileptic seizure detection. The signal segments of a channel of …

Objective. Accurate decoding of individual finger movements is crucial for advanced
prosthetic control. In this work, we introduce the use of Riemannian-space features and …

Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson's disease (PD)
that has undergone technological evolution that parallels an expansion in clinical …

Objective. Various on-workstation neural-spike-based brain machine interface (BMI)
systems have reached the point of in-human trials, but on-node and on-implant BMI systems …

Neuroscience and neurotechnology are currently being revolutionized by artificial
intelligence (AI) and machine learning. AI is widely used to study and interpret neural …