Structured Sampling and Recovery of iEEG Signals

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The modulation of the cervical vagus nerve (VN) using neural electrodes has shown great potential to treat cardiovascular, inflammatory, intestinal, or respiratory dysfunctions. Intraneural electrodes have shown great potentials for other neuroprosthetic a ...

Objective. Bioelectronic medicine is opening new perspectives for the treatment of some major chronic diseases through the physical modulation of autonomic nervous system activity. Being the main peripheral route for electrical signals between central nerv ...

Objective. Optic nerve's intraneural stimulation is an emerging neuroprosthetic approach to provide artificial vision to totally blind patients. An open question is the possibility to evoke individual non-overlapping phosphenes via selective intraneural op ...

Epilepsy is a chronic neurological disorder affecting more than 65 million people worldwide and manifested by recurrent unprovoked seizures. The unpredictability of seizures not only degrades the quality of life of the patients, but it can also be life-thr ...

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 IoT devices. This paper introduces a machine learning model based on oblique decision ...

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 signa ...

The mechanical mismatch between implantable interfaces and neural tissues may be reduced by employing soft polymeric materials. Here, we report on a simple strategy to prepare and pattern a soft electrode coating of neural interfacing devices based on a sc ...

Objective.Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community to find evidence of cortical involvement at walking initiation and during locomotion. However, the decoding of gait patterns from brain signals remains an open challenge. ...

Bioelectronic and neuroprosthetic interfaces rely on implanted microelectrode arrays (MEAs) to interact with the human body. Printing techniques, such as inkjet and screen printing, are attractive methods for the manufacturing of MEAs because they allow fl ...

Implanted microelectrode arrays sense local neuronal activity, signals which are used as control commands for brain computer interface (BCI) technology. Patients with tetraplegia have used BCI technology to achieve an extraordinary degree of interaction wi ...

Cognitive information has been exploited in non-invasive Brain Computer Interface (BCI) scenarios to provide autonomous external agents with additional information. In this context, Error-related potentials (ErrPs), temporal deflections in electroencephalo ...

Neural interfaces capable of multi-site electrical recording, on-site signal classification, and closed-loop therapy are critical for the diagnosis and treatment of neurological disorders. However, deploying machine learning algorithms on low-power neural ...

Understanding how the human brain cortex behaves when the dynamical balance is unexpectedly challenged can be useful to enable fall prevention strategies during daily activities. In this respect, we designed and tested a novel methodological approach to ea ...

Structured Sampling and Recovery of iEEG Signals

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Adaptation and Optimization of an Intraneural Electrode to Interface with the Cervical Vagus Nerve

Simultaneous decoding of cardiovascular and respiratory functional changes from pig intraneural vagus nerve signals

A machine-learning algorithm correctly classifies cortical evoked potentials from both visual stimulation and electrical stimulation of the optic nerve

EpilepsyGAN: Synthetic Epileptic Brain Activities with Privacy Preservation

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

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

Soft Printable Electrode Coating for Neural Interfaces

Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network

All-Printed Electrocorticography Array for In Vivo Neural Recordings

Longitudinal neural and vascular structural dynamics produced by chronic microelectrode implantation

Inferring subjective preferences on robot trajectories using EEG signals

Closed-Loop Neural Interfaces with Embedded Machine Learning

Cortical reactive balance responses to unexpected slippages while walking: a pilot study