Brain-Computer Interfaces

Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, …

Gumpy: a Python toolbox suitable for hybrid brain–computer interfaces

**Objective:** The objective of this work is to present gumpy, a new free and open source Python toolbox designed for hybrid brain–computer interface (BCI). Approach. Gumpy provides state-of-the-art algorithms and includes a rich selection of signal …

Decoding EEG Brain Signals using Recurrent Neural Networks

Brain-computer interfaces (BCIs) based on electroencephalography (EEG) enable direct communication between humans and computers by analyzing brain activity. Specifically, modern BCIs are capable of translating imagined movements into real- life …