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Brain-Computer Interfaces

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Deep-learning online EEG decoding brain-computer interface using error-related potentials recorded with a consumer-grade headset.

Biomedical physics & engineering express
Brain-computer interfaces (BCIs) allow subjects with sensorimotor disability to interact with the environment. Non-invasive BCIs relying on EEG signals such as event-related potentials (ERPs) have been established as a reliable compromise between spa...

Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography.

Sensors (Basel, Switzerland)
Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based ...

Robust asynchronous control of ERP-Based brain-Computer interfaces using deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Brain-computer interfaces (BCI) based on event-related potentials (ERP) are a promising technology for alternative and augmented communication in an assistive context. However, most approaches to date are synchronous, requir...

A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface.

Journal of neural engineering
Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been used to extract human intentions from electroencephalography (EEG) recordings. Those based on motor ima...

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...