AIMC Topic: Brain-Computer Interfaces

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Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients.

Journal of visualized experiments : JoVE Apr 18, 2025
This study introduces a Brain-Computer Interface (BCI)-controlled upper limb assistive robot for post-stroke rehabilitation. The system utilizes electroencephalogram (EEG) and electrooculogram (EOG) signals to help users assist upper limb function in...
Female Aged Humans Brain-Computer Interfaces Upper Extremity Electrooculography Adult Stroke Rehabilitation Male Middle Aged Robotics Electroencephalography Activities of Daily Living
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Integrative neurorehabilitation using brain-computer interface: From motor function to mental health after stroke.

Bioscience trends Apr 17, 2025
Stroke remains a leading cause of mortality and long-term disability worldwide, frequently resulting in impairments in motor control, cognition, and emotional regulation. Conventional rehabilitation approaches, while partially effective, often lack i...
Artificial Intelligence Mental Health Virtual Reality Stroke Rehabilitation Neurological Rehabilitation Humans Brain-Computer Interfaces Neurofeedback Cognition Electroencephalography Stroke
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Improved performance of fNIRS-BCI by stacking of deep learning-derived frequency domain features.

PloS one Apr 17, 2025
The functional near-infrared spectroscopy-based brain-computer interface (fNIRS-BCI) systems recognize patterns in brain signals and generate control commands, thereby enabling individuals with motor disabilities to regain autonomy. In this study han...
Spectroscopy, Near-Infrared Deep Learning Hand Strength Young Adult Humans Neural Networks, Computer Adult Brain-Computer Interfaces Algorithms Male Female
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Multi-scale convolutional transformer network for motor imagery brain-computer interface.

Scientific reports Apr 15, 2025
Brain-computer interface (BCI) systems allow users to communicate with external devices by translating neural signals into real-time commands. Convolutional neural networks (CNNs) have been effectively utilized for decoding motor imagery electroencep...
Imagination Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Humans Neural Networks, Computer Brain
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Exploring cortical excitability in children with cerebral palsy through lower limb robot training based on MI-BCI.

Scientific reports Apr 10, 2025
This study aims to compare brain activity differences under the motor imagery-brain-computer interface (MI-BCI), motor imagery (MI), and resting (REST) paradigms through EEG microstate and functional connectivity (FC) analysis, providing a theoretica...
Brain-Computer Interfaces Cerebral Palsy Male Child, Preschool Child Lower Extremity Robotics Electroencephalography Humans Female
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Decoding SSVEP Via Calibration-Free TFA-Net: A Novel Network Using Time-Frequency Features.

IEEE journal of biomedical and health informatics Apr 4, 2025
Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) signals offer high information transfer rates and non-invasive brain-to-device connectivity, making them highly practical. In recent years, deep learning technique...
Algorithms Female Young Adult Adult Humans Neural Networks, Computer Signal Processing, Computer-Assisted Brain-Computer Interfaces Evoked Potentials, Visual Male Deep Learning Electroencephalography
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DC-ASTGCN: EEG Emotion Recognition Based on Fusion Deep Convolutional and Adaptive Spatio-Temporal Graph Convolutional Networks.

IEEE journal of biomedical and health informatics Apr 4, 2025
Thanks to advancements in artificial intelligence and brain-computer interface (BCI) research, there has been increasing attention towards emotion recognition techniques based on electroencephalogram (EEG) recently. The complexity of EEG data poses a...
Signal Processing, Computer-Assisted Brain-Computer Interfaces Emotions Humans Adult Electroencephalography Neural Networks, Computer Deep Learning Brain
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LGFormer: integrating local and global representations for EEG decoding.

Journal of neural engineering Apr 3, 2025
Electroencephalography (EEG) decoding is challenging because of its temporal variability and low signal-to-noise ratio, which complicate the extraction of meaningful information from signals. Although convolutional neural networks (CNNs) effectively ...
Neural Networks, Computer Brain Electroencephalography Humans Brain-Computer Interfaces
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A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Apr 1, 2025
Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Fun...
Calibration Brain-Computer Interfaces Neurofeedback Tomography, Optical Computer Systems Image Processing, Computer-Assisted Artifacts Male Brain Spectroscopy, Near-Infrared Female Deep Learning Humans Young Adult Algorithms Adult
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Shared autonomy between human electroencephalography and TD3 deep reinforcement learning: A multi-agent copilot approach.

Annals of the New York Academy of Sciences Mar 30, 2025
Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment....
Reinforcement, Psychology Deep Learning Reinforcement Machine Learning Humans Algorithms Electroencephalography Brain Brain-Computer Interfaces
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