Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …

Classification of electroencephalography (EEG) signals corresponding to imagined speech
production is important for the development of a direct-speech brain–computer interface (DS …

Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement
unit (IMU) sensors to detect gait events (ie, initial and final foot contact). However, these …

Electroencephalography (EEG) signals can be analyzed in the temporal, spatial, or
frequency domains. Noise and artifacts during the data acquisition phase contaminate these …

Achieving the shift towards Industry 4.0 is only feasible through the active integration of the
shopfloor into the transformation process. Several shopfloor management (SM) systems can …

We propose an open-source Python library, called DN3, designed to accelerate deep
learning (DL) analysis with encephalographic data. This library focuses on making …

Machine learning competitions such as those organized by Kaggle or KDD represent a
useful benchmark for data science research. In this work, we present our winning solution to …

Modern deep neural networks (DNNs) have shown promising results in brain studies
involving multi-channel electroencephalogram (EEG) signals. The representations produced …

EEG correlation at a distance: A re-analysis of two studies using a machine learning
approach[version 2; peer review: 1 approved Page 1 Open Peer Review Any reports and …

EEG correlation at a distance: A re-analysis of two studies using a machine learning
approach[version 1; referees: awaiting peer Page 1 Open Peer Review Discuss this article (0) …