Unsupervised Accuracy Estimation for Brain-Computer Interfaces Based on Selective Auditory Attention Decoding

Engineering, Electrical Engineering, Information Science, Artificial Intelligence, Medicine, Neurology, Life Sciences, Bioengineering,
Brain-Computer Interface   Selective Auditory Attention Decoding   Unsupervised Accuracy Estimation   Stimulus Reconstruction   Neurofeedback  

Unsupervised Accuracy Estimation for Brain-Computer Interfaces Based on Selective Auditory Attention Decoding Academic Background In complex auditory environments, humans can selectively focus on a specific sound source while ignoring other interfering sounds—a phenomenon known as the “cocktail party effect.” Selective Auditory Attention Decoding (...

A Temporal Dependency Learning CNN with Attention Mechanism for MI-EEG Decoding

Information Science, Automation, Artificial Intelligence, Medicine, Neurology, Life Sciences, Bioengineering,
Temporal Dependency Learning   Convolutional Neural Networks   Brain-Computer Interface   Motor Imagery   Attention Mechanism  

MI-EEG Decoding Using a Temporal Dependency Learning Convolutional Neural Network (CNN) Based on Attention Mechanism Research Background and Problem Description Brain-Computer Interface (BCI) systems provide a new way of communicating with computers by real-time translation of brain signals. In recent years, BCI technology has played an important r...

A Transformer-Based Approach Combining Deep Learning Network and Spatial-Temporal Information for Raw EEG Classification

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology, Life Sciences, Bioengineering,
Transformer   EEG classification   Deep learning   Spatial-temporal information   Attention mechanism   Brain-Computer Interface  

Research Background and Objectives In recent years, Brain-Computer Interface (BCI) systems have been widely applied in the fields of neuroengineering and neuroscience. Electroencephalogram (EEG), as a data tool reflecting the activities of different neuronal groups in the central nervous system, has become a core research topic in these fields. How...

ADFCNN: Attention-Based Dual-Scale Fusion Convolutional Neural Network for Motor Imagery Brain–Computer Interface

ADFCNN: Attention-Based Dual-Scale Fusion Convolutional Neural Network for Motor Imagery Brain–Computer Interface

Information Science, Automation, Artificial Intelligence, Medicine, Neurology, Life Sciences, Bioengineering,
Attention   Dual-Scale Fusion   Convolutional Neural Network   Motor Imagery   Brain-Computer Interface   EEG   Self-Attention Mechanism  

Brain-Computer Interface (BCI) has emerged as an enhanced communication and control technology in recent years. In BCI based on electrophysiological characteristics (such as Electroencephalogram, EEG), Motor Imagery (MI) is an important branch that decodes users’ motor intentions for use in clinical rehabilitation, intelligent wheelchair control, c...

An Intersubject Brain-Computer Interface Based on Domain-Adversarial Training of Convolutional Neural Network for Online Attention Decoding

An Intersubject Brain-Computer Interface Based on Domain-Adversarial Training of Convolutional Neural Network for Online Attention Decoding

Information Science, Artificial Intelligence, Medicine, Neurology, Life Sciences, Bioengineering,
Brain-Computer Interface   Attention Decoding   Domain-Adversarial Training   Convolutional Neural Network   EEG Signals   Online Experiments   Intersubject Variability  

Cross-Subject Brain-Computer Interface: Real-time Attention Decoding Based on Domain-Adversarial Training with Convolutional Neural Networks Academic Background Attention decoding plays a crucial role in our daily lives and its implementation based on electroencephalogram (EEG) has garnered extensive attention. However, due to significant inter-ind...