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Brain-Computer Interfaces

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EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.

IEEE/ACM transactions on computational biology and bioinformatics
Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI a...

Advanced Machine-Learning Methods for Brain-Computer Interfacing.

IEEE/ACM transactions on computational biology and bioinformatics
The brain-computer interface (BCI) connects the brain and the external world through an information transmission channel by interpreting the physiological information of the brain during thinking activities. The effective classification of electroenc...

Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods.

Sensors (Basel, Switzerland)
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
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
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
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
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
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)
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
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...