AIMC Topic: Brain-Computer Interfaces

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

Scientific reports Oct 7, 2023
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...
Brain Robotics Brain-Computer Interfaces Neurofeedback Bayes Theorem Electroencephalography
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Group-level brain decoding with deep learning.

Human brain mapping Sep 27, 2023
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...
Brain Deep Learning Humans Magnetoencephalography Brain Mapping Brain-Computer Interfaces
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Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models.

Journal of neural engineering Sep 20, 2023
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...
Electrocorticography Sensorimotor Cortex Humans Communication Speech Deep Learning Brain-Computer Interfaces
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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 Aug 31, 2023
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...
Robotic Surgical Procedures Robotics Humans Brain-Computer Interfaces Evoked Potentials, Visual Bayes Theorem
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Decoding movement kinematics from EEG using an interpretable convolutional neural network.

Computers in biology and medicine Aug 8, 2023
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...
Electroencephalography Brain-Computer Interfaces Algorithms Biomechanical Phenomena Movement Humans Neural Networks, Computer Machine Learning
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Jump-GRS: a multi-phase approach to structured pruning of neural networks for neural decoding.

Journal of neural engineering Jul 31, 2023
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...
Brain-Computer Interfaces Neurons Calcium Algorithms Neural Networks, Computer
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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 Jul 12, 2023
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...
Humans Brain-Computer Interfaces Neural Networks, Computer
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EEG motor imagery classification using deep learning approaches in naïve BCI users.

Biomedical physics & engineering express Jun 23, 2023
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 ...
Brain-Computer Interfaces Imagination Humans Deep Learning Electroencephalography Reproducibility of Results
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Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal.

IEEE transactions on cybernetics Jun 15, 2023
The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroencephalography (EEG) is essential for strength and endurance augmentation using exoskeleton/exosuit. For system development, the conventional classificatio...
Electroencephalography Hand Brain-Computer Interfaces Biomechanical Phenomena Deep Learning Algorithms
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Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction.

Computers in biology and medicine Jun 8, 2023
Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stat...
Algorithms Computers Humans Artificial Intelligence Brain Brain-Computer Interfaces Machine Learning Quality of Life
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