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Brain-Computer Interfaces

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Cybersecurity in neural interfaces: Survey and future trends.

Computers in biology and medicine
With the joint advancement in areas such as pervasive neural data sensing, neural computing, neuromodulation and artificial intelligence, neural interface has become a promising technology facilitating both the closed-loop neurorehabilitation for neu...

Brain-computer interface for robot control with eye artifacts for assistive applications.

Scientific reports
Human-robot interaction is a rapidly developing field and robots have been taking more active roles in our daily lives. Patient care is one of the fields in which robots are becoming more present, especially for people with disabilities. People with ...

Bayesian learning from multi-way EEG feedback for robot navigation and target identification.

Scientific reports
Many brain-computer interfaces require a high mental workload. Recent research has shown that this could be greatly alleviated through machine learning, inferring user intentions via reactive brain responses. These signals are generated spontaneously...

Group-level brain decoding with deep learning.

Human brain mapping
Decoding brain imaging data are gaining popularity, with applications in brain-computer interfaces and the study of neural representations. Decoding is typically subject-specific and does not generalise well over subjects, due to high amounts of betw...

Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models.

Journal of neural engineering
Development of brain-computer interface (BCI) technology is key for enabling communication in individuals who have lost the faculty of speech due to severe motor paralysis. A BCI control strategy that is gaining attention employs speech decoding from...

SSVEP-Based Brain-Computer Interface Controlled Robotic Platform With Velocity Modulation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been extensively studied due to many benefits, such as non-invasiveness, high information transfer rate, and ease of use. SSVEP-based BCI has been investigated i...

Decoding movement kinematics from EEG using an interpretable convolutional neural network.

Computers in biology and medicine
Continuous decoding of hand kinematics has been recently explored for the intuitive control of electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs). Deep neural networks (DNNs) are emerging as powerful decoders, for their ability to au...

Jump-GRS: a multi-phase approach to structured pruning of neural networks for neural decoding.

Journal of neural engineering
Neural decoding, an important area of neural engineering, helps to link neural activity to behavior. Deep neural networks (DNNs), which are becoming increasingly popular in many application fields of machine learning, show promising performance in ne...

A 0.99-to-4.38 uJ/class Event-Driven Hybrid Neural Network Processor for Full-Spectrum Neural Signal Analyses.

IEEE transactions on biomedical circuits and systems
Versatile and energy-efficient neural signal processors are in high demand in brain-machine interfaces and closed-loop neuromodulation applications. In this paper, we propose an energy-efficient processor for neural signal analyses. The proposed proc...

EEG motor imagery classification using deep learning approaches in naïve BCI users.

Biomedical physics & engineering express
Motor Imagery (MI)-Brain Computer-Interfaces (BCI) illiteracy defines that not all subjects can achieve a good performance in MI-BCI systems due to different factors related to the fatigue, substance consumption, concentration, and experience in the ...