The immense progress in physiological signal acquisition and processing in health
monitoring allowed a better understanding of patient disease detection and diagnosis. With …

The tremendous progress of big data acquisition and processing in the field of neural
engineering has enabled a better understanding of the patient's brain disorders with their …

In wearable sensors, energy efficiency is crucial, particularly during phases where devices
are not processing, but rather acquiring biosignals for subsequent analysis. This study …

The diagnosis of epilepsy based on visual inspection of electroencephalogram (EEG)
signals is inherently complex and prone to error, even for physicians, mainly due to the large …

Abstract Brain-Machine Interface (BMI) is a control paradigm involving using brain signal to
generate control commands for other devices. A non-invasive method of brain signal …

Extracting information from dense multi-channel neural sensors for accurate diagnosis of
brain disorders necessitates computationally expensive and advanced signal processing …

In the Era of Connection, every object collects data and sends them to the central servers
where hidden information is extracted, creating the Internet-of-things (IoT). Such objects, or …

Designing systems with many recording channels is a major challenge in brain-machine
interfaces. Power, bandwidth, and size requirements impose tight design constraints for …