AIMC Topic: Brain-Computer Interfaces
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Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods.
Sensors (Basel, Switzerland)
Sep 29, 2021
The use of imagined speech with electroencephalographic (EEG) signals is a promising field of brain-computer interfaces (BCI) that seeks communication between areas of the cerebral cortex related to language and devices or machines. However, the comp...
Optimizing Motor Intention Detection With Deep Learning: Towards Management of Intraoperative Awareness.
IEEE transactions on bio-medical engineering
Sep 20, 2021
OBJECTIVE: This article shows the interest in deep learning techniques to detect motor imagery (MI) from raw electroencephalographic (EEG) signals when a functional electrical stimulation is added or not. Impacts of electrode montages and bandwidth a...
Generative Adversarial Networks-Based Data Augmentation for Brain-Computer Interface.
IEEE transactions on neural networks and learning systems
Aug 31, 2021
The performance of a classifier in a brain-computer interface (BCI) system is highly dependent on the quality and quantity of training data. Typically, the training data are collected in a laboratory where the users perform tasks in a controlled envi...
Deep learning multimodal fNIRS and EEG signals for bimanual grip force decoding.
Journal of neural engineering
Aug 31, 2021
Non-invasive brain-machine interfaces (BMIs) offer an alternative, safe and accessible way to interact with the environment. To enable meaningful and stable physical interactions, BMIs need to decode forces. Although previously addressed in the unima...
A Hybrid-Domain Deep Learning-Based BCI For Discriminating Hand Motion Planning From EEG Sources.
International journal of neural systems
Aug 11, 2021
In this paper, a hybrid-domain deep learning (DL)-based neural system is proposed to decode hand movement preparation phases from electroencephalographic (EEG) recordings. The system exploits information extracted from the temporal-domain and time-fr...
Time-Frequency Representation and Convolutional Neural Network-Based Emotion Recognition.
IEEE transactions on neural networks and learning systems
Jul 6, 2021
Emotions composed of cognizant logical reactions toward various situations. Such mental responses stem from physiological, cognitive, and behavioral changes. Electroencephalogram (EEG) signals provide a noninvasive and nonradioactive solution for emo...
A Hybrid Brain-Computer Interface for Real-Life Meal-Assist Robot Control.
Sensors (Basel, Switzerland)
Jul 4, 2021
Assistant devices such as meal-assist robots aid individuals with disabilities and support the elderly in performing daily activities. However, existing meal-assist robots are inconvenient to operate due to non-intuitive user interfaces, requiring ad...
Exploring the Use of Brain-Computer Interfaces in Stroke Neurorehabilitation.
BioMed research international
Jun 18, 2021
With the continuous development of artificial intelligence technology, "brain-computer interfaces" are gradually entering the field of medical rehabilitation. As a result, brain-computer interfaces (BCIs) have been included in many countries' strateg...
Development and Application of Medicine-Engineering Integration in the Rehabilitation of Traumatic Brain Injury.
BioMed research international
Jun 12, 2021
The rapid progress of the combination of medicine and engineering provides better chances for the clinical treatment and healthcare engineering. Traumatic brain injury (TBI) and its related symptoms have become a major global health problem. At prese...
Benefits of deep learning classification of continuous noninvasive brain-computer interface control.
Journal of neural engineering
Jun 9, 2021
. Noninvasive brain-computer interfaces (BCIs) assist paralyzed patients by providing access to the world without requiring surgical intervention. Prior work has suggested that EEG motor imagery based BCI can benefit from increased decoding accuracy ...
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