AIMC Topic: Brain-Computer Interfaces

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A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification.

Biosensors
Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it has been used to optimize efficiency. Recently, classification methods for Convolutional Neural Network (CNN)-based electroencephalography ...

Effects of a Brain-Computer Interface-Operated Lower Limb Rehabilitation Robot on Motor Function Recovery in Patients with Stroke.

Journal of healthcare engineering
PURPOSE: To observe the effect of a brain-computer interface-operated lower limb rehabilitation robot (BCI-LLRR) on functional recovery from stroke and to explore mechanisms.

Attentional load classification in multiple object tracking task using optimized support vector machine classifier: a step towards cognitive brain-computer interface.

Journal of medical engineering & technology
Cognitive brain-computer interface (cBCI) is an emerging area with applications in neurorehabilitation and performance monitoring. cBCI works on the cognitive brain signal that does not require a person to pay much effort unlike the motor brain-compu...

Neural interface systems with on-device computing: machine learning and neuromorphic architectures.

Current opinion in biotechnology
Development of neural interface and brain-machine interface (BMI) systems enables the treatment of neurological disorders including cognitive, sensory, and motor dysfunctions. While neural interfaces have steadily decreased in form factor, recent dev...

A Deep Learning-Based Classification Method for Different Frequency EEG Data.

Computational and mathematical methods in medicine
In recent years, the research on electroencephalography (EEG) has focused on the feature extraction of EEG signals. The development of convenient and simple EEG acquisition devices has produced a variety of EEG signal sources and the diversity of the...

Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network.

Computational and mathematical methods in medicine
Emotion recognition plays an important role in the field of human-computer interaction (HCI). Automatic emotion recognition based on EEG is an important topic in brain-computer interface (BCI) applications. Currently, deep learning has been widely us...

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...