AIMC Topic: Brain-Computer Interfaces

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Dual Attention Relation Network With Fine-Tuning for Few-Shot EEG Motor Imagery Classification.

IEEE transactions on neural networks and learning systems Oct 29, 2024
Recently, motor imagery (MI) electroencephalography (EEG) classification techniques using deep learning have shown improved performance over conventional techniques. However, improving the classification accuracy on unseen subjects is still challengi...
Signal-To-Noise Ratio Imagination Signal Processing, Computer-Assisted Attention Brain-Computer Interfaces Humans Deep Learning Electroencephalography Neural Networks, Computer Algorithms
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Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration.

IEEE transactions on bio-medical engineering Oct 25, 2024
OBJECTIVE: To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real-time, with low computational overhead, on a mobile platform in a channe...
Humans Neural Networks, Computer Artifacts Brain-Computer Interfaces Smartphone Electroencephalography Signal Processing, Computer-Assisted Software Algorithms Deep Learning
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Transfer Learning With Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-Centre Data.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Oct 22, 2024
Machine learning and deep learning advancements have boosted Brain-Computer Interface (BCI) performance, but their wide-scale applicability is limited due to factors like individual health, hardware variations, and cultural differences affecting neur...
Male Middle Aged Female Deep Learning Neural Networks, Computer Young Adult Humans Machine Learning Adult Brain-Computer Interfaces Electroencephalography Event-Related Potentials, P300 Algorithms Calibration
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Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification.

Computers in biology and medicine Oct 18, 2024
In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Neverthele...
Signal Processing, Computer-Assisted Humans Attention Brain Neural Networks, Computer Brain-Computer Interfaces Deep Learning Imagination Electroencephalography
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Cortical ROI Importance Improves MI Decoding From EEG Using Fused Light Neural Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 27, 2024
Decoding motor imagery (MI) using deep learning in cortical level has potential in brain computer interface based intelligent rehabilitation. However, a mass of dipoles is inconvenient to extract the personalized features and requires a more complex ...
Male Deep Learning Electroencephalography Imagination Brain-Computer Interfaces Humans Female Cerebral Cortex Neural Networks, Computer Young Adult Adult Algorithms
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Subject-Independent Wearable P300 Brain-Computer Interface Based on Convolutional Neural Network and Metric Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 25, 2024
The calibration procedure for a wearable P300 brain-computer interface (BCI) greatly impact the user experience of the system. Each user needs to spend additional time establishing a decoder adapted to their own brainwaves. Therefore, achieving subje...
Female Young Adult Adult Neural Networks, Computer Machine Learning Algorithms Calibration Brain-Computer Interfaces Humans Male Event-Related Potentials, P300 Wearable Electronic Devices Electroencephalography
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Multi-source Selective Graph Domain Adaptation Network for cross-subject EEG emotion recognition.

Neural networks : the official journal of the International Neural Network Society Sep 24, 2024
Affective brain-computer interface is an important part of realizing emotional human-computer interaction. However, existing objective individual differences among subjects significantly hinder the application of electroencephalography (EEG) emotion ...
Emotions Electroencephalography Brain-Computer Interfaces Humans Neural Networks, Computer Brain
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Optimizing Real-Time MI-BCI Performance in Post-Stroke Patients: Impact of Time Window Duration on Classification Accuracy and Responsiveness.

Sensors (Basel, Switzerland) Sep 22, 2024
Brain-computer interfaces (BCIs) are promising tools for motor neurorehabilitation. Achieving a balance between classification accuracy and system responsiveness is crucial for real-time applications. This study aimed to assess how the duration of ti...
Brain-Computer Interfaces Stroke Rehabilitation Discriminant Analysis Electroencephalography Stroke Algorithms Female Humans Aged Time Factors Male Support Vector Machine Middle Aged
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Nanorobot-Based Direct Implantation of Flexible Neural Electrode for BCI.

IEEE transactions on bio-medical engineering Sep 19, 2024
Brain-Computer Interface (BCI) has gained remarkable prominence in biomedical community. While BCI holds vast potential across diverse domains, the implantation of neural electrodes poses multifaceted challenges to fully explore the power of BCI. Con...
Electrodes, Implanted Animals Mice Robotics Nanotechnology Brain-Computer Interfaces Brain Electroencephalography Equipment Design
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An Intersubject Brain-Computer Interface Based on Domain-Adversarial Training of Convolutional Neural Network.

IEEE transactions on bio-medical engineering Sep 19, 2024
OBJECTIVE: Attention decoding plays a vital role in daily life, where electroencephalography (EEG) has been widely involved. However, training a universally effective model for everyone is impractical due to substantial interindividual variability in...
Algorithms Brain Female Young Adult Humans Neural Networks, Computer Signal Processing, Computer-Assisted Adult Brain-Computer Interfaces Male Electroencephalography
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