Electrode Array
Electrode arrays are collections of electrodes used to measure bioelectrical signals from various sources, such as the brain, muscles, or heart, with the primary objective of improving signal acquisition and analysis for diverse applications. Current research focuses on enhancing signal quality by addressing issues like electrode shift and improving data processing through advanced machine learning models, including deep neural networks (e.g., CNNs, LSTMs, U-Nets), graph neural networks, and generative adversarial networks, to improve accuracy and robustness. These advancements have significant implications for various fields, including neurology (e.g., epilepsy diagnosis, brain-computer interfaces), cardiology (ECG analysis and digital twins), and prosthetics (nerve interfaces and EMG-based control), by enabling more precise and reliable measurements and analyses.
Papers
Information encoding and decoding in in-vitro neural networks on micro electrode arrays through stimulation timing
Trym A. E. Lindell, Ola H. Ramstad, Ionna Sandvig, Axel Sandvig, Stefano Nichele
BDAN: Mitigating Temporal Difference Across Electrodes in Cross-Subject Motor Imagery Classification via Generative Bridging Domain
Zhige Chen, Rui Yang, Mengjie Huang, Chengxuan Qin, Zidong Wang
Localising the Seizure Onset Zone from Single-Pulse Electrical Stimulation Responses with a CNN Transformer
Jamie Norris, Aswin Chari, Dorien van Blooijs, Gerald Cooray, Karl Friston, Martin Tisdall, Richard Rosch
Artificial Neural Networks-based Real-time Classification of ENG Signals for Implanted Nerve Interfaces
Antonio Coviello, Francesco Linsalata, Umberto Spagnolini, Maurizio Magarini
Design, Fabrication and Evaluation of a Stretchable High-Density Electromyography Array
Rejin John Varghese, Matteo Pizzi, Aritra Kundu, Agnese Grison, Etienne Burdet, Dario Farina