AIMC Topic: Brain-Computer Interfaces
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Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.
Journal of neuroengineering and rehabilitation
Apr 5, 2024
BACKGROUND: This research focused on the development of a motor imagery (MI) based brain-machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BM...
Applying Common Spatial Pattern and Convolutional Neural Network to Classify Movements via EEG Signals.
Clinical EEG and neuroscience
Mar 24, 2024
Developing an electroencephalography (EEG)-based brain-computer interface (BCI) system is crucial to enhancing the control of external prostheses by accurately distinguishing various movements through brain signals. This innovation can provide comfor...
Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human-robot collaboration.
Journal of neural engineering
Mar 19, 2024
. Brain-computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human rob...
Optimized FFNN with multichannel CSP-ICA framework of EEG signal for BCI.
Computer methods in biomechanics and biomedical engineering
Feb 26, 2024
The electroencephalogram (EEG) of the patient is used to identify their motor intention, which is then converted into a control signal through a brain-computer interface (BCI) based on motor imagery. Whenever gathering features from EEG signals, maki...
A Novel Data Augmentation Approach Using Mask Encoding for Deep Learning-Based Asynchronous SSVEP-BCI.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Feb 22, 2024
Deep learning (DL)-based methods have been successfully employed as asynchronous classification algorithms in the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. However, these methods often suffer from the l...
Channel Selection for Stereo- Electroencephalography (SEEG)-Based Invasive Brain-Computer Interfaces Using Deep Learning Methods.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Feb 19, 2024
Brain-computer interfaces (BCIs) can enable direct communication with assistive devices by recording and decoding signals from the brain. To achieve high performance, many electrodes will be used, such as the recently developed invasive BCIs with cha...
Unraveling motor imagery brain patterns using explainable artificial intelligence based on Shapley values.
Computer methods and programs in biomedicine
Jan 30, 2024
BACKGROUND AND OBJECTIVE: Motor imagery (MI) based brain-computer interfaces (BCIs) are widely used in rehabilitation due to the close relationship that exists between MI and motor execution (ME). However, the underlying brain mechanisms of MI remain...
Decoding Single and Paired Phonemes Using 7T Functional MRI.
Brain topography
Jan 23, 2024
Several studies have shown that mouth movements related to the pronunciation of individual phonemes are represented in the sensorimotor cortex. This would theoretically allow for brain computer interfaces that are capable of decoding continuous speec...
Brain control of bimanual movement enabled by recurrent neural networks.
Scientific reports
Jan 18, 2024
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 could unlock greater functionality for people with paralysis (e.g...
Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain-computer interface.
Journal of neural engineering
Jan 17, 2024
Motor imagery (MI) brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been developed primarily for stroke rehabilitation, however, due to limited stroke data, current deep learning methods for cross-subject classification rely ...
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