Brain control of bimanual movement enabled by recurrent neural networks
Brain-computer interfaces have so far focused largely on enabling the control of a single
effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion …
effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion …
Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing
Brain-computer interfaces (BCIs) can translate brain signals directly into commands for
external devices. Electroencephalography (EEG)-based BCIs mostly rely on the …
external devices. Electroencephalography (EEG)-based BCIs mostly rely on the …
Non-invasive Brain-Computer Interfaces: State of the Art and Trends
BJ Edelman, S Zhang, G Schalk… - IEEE Reviews in …, 2024 - ieeexplore.ieee.org
Brain-computer interface (BCI) is a rapidly evolving technology that has the potential to
widely influence research, clinical and recreational use. Non-invasive BCI approaches are …
widely influence research, clinical and recreational use. Non-invasive BCI approaches are …
Efficient diagnostic classification of diverse pathologies through contextual eye movement data analysis with a novel hybrid architecture
AE El Hmimdi, T Palpanas, Z Kapoula - Scientific Reports, 2024 - nature.com
The analysis of eye movements has proven valuable for understanding brain function and
the neuropathology of various disorders. This research aims to utilize eye movement data …
the neuropathology of various disorders. This research aims to utilize eye movement data …
A systematic evaluation of euclidean alignment with deep learning for eeg decoding
Objective: Electroencephalography signals are frequently used for various Brain–Computer
interface (BCI) tasks. While deep learning (DL) techniques have shown promising results …
interface (BCI) tasks. While deep learning (DL) techniques have shown promising results …
Signal to Image Conversion and Convolutional Neural Networks for Physiological Signal Processing: A Review
Physiological signals obtained from electroencephalography (EEG), electromyography
(EMG), and electrocardiography (ECG) provide valuable clinical information but pose …
(EMG), and electrocardiography (ECG) provide valuable clinical information but pose …
Investigating multilevel cognitive processing within error-free and error-prone feedback conditions in executed and observed car driving
Accident analyses repeatedly reported the considerable contribution of run-off-road
incidents to fatalities in road traffic, and despite considerable advances in assistive …
incidents to fatalities in road traffic, and despite considerable advances in assistive …
Boosting lower-limb motor imagery performance through an ensemble method for gait rehabilitation
Lower-limb exoskeletons have been used extensively in many rehabilitation applications to
assist disabled people with their therapies. Brain–machine interfaces (BMIs) further provide …
assist disabled people with their therapies. Brain–machine interfaces (BMIs) further provide …
[HTML][HTML] SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals
Deep learning has revolutionized EEG decoding, showcasing its ability to outperform
traditional machine learning models. However, unlike other fields, EEG decoding lacks …
traditional machine learning models. However, unlike other fields, EEG decoding lacks …
EEG complexity measures for detecting mind wandering during video-based learning
S Tang, Z Li - Scientific Reports, 2024 - nature.com
This study explores the efficacy of various EEG complexity measures in detecting mind
wandering during video-based learning. Employing a modified probe-caught method, we …
wandering during video-based learning. Employing a modified probe-caught method, we …