EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding

EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding

Engineering, Electrical Engineering, Information Science, Artificial Intelligence, Medicine, Neurology,
brain-computer interface   motor imagery   attention collapse   temporal convolution network   transfer learning  

Research Background Brain-Computer Interface (BCI) technology enables direct communication between the brain and external devices. It is widely used in fields such as human-computer interaction, motor rehabilitation, and healthcare. Common BCI paradigms include steady-state visual evoked potentials (SSVEP), P300, and motor imagery (MI). Among these...

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...

Wavelet-Based Temporal-Spectral-Attention Correlation Coefficient for Motor Imagery EEG Classification

Engineering, Electrical Engineering, Information Science, Computer Science, Artificial Intelligence, Medicine, Basic Medical Sciences, Neurology, Medical Imaging, Life Sciences, Bioengineering,
brain-computer interface   motor imagery   EEG channel attention   temporal-spectral attention   wavelet transform   correlation coefficient  

Brain-Computer Interface (BCI) technology has rapidly developed in recent years and is considered a cutting-edge technology that allows external devices to be controlled directly by the brain without the need for peripheral nerves and muscles. Particularly in the application of Motor Imagery Electroencephalography (MI-EEG), BCI technology has shown...

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 Attention-Based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG

Engineering, Electrical Engineering, Information Science, Artificial Intelligence, Medicine, Medical Laboratory Science, Neurology,
Attention   Deep Learning   Sleep Stage   Single-Channel EEG   Feature Extraction   Automatic Classification   Temporal Dependencies  

The IEEE “Transactions on Neural Systems and Rehabilitation Engineering” published a paper titled “Sleep Stage Classification Using Attention-Based Deep Learning for Single-Channel EEG” in Volume 29, 2021. The author of the article include Emadeldeen Edele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan. The main go...

Expanding the Clinical Application of OPM-MEG Using an Effective Automatic Suppression Method for the Dental Brace Metal Artifact

Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Biophysics,
magnetoencephalography   optically pumped magnetometers   clinical applications   metallic artifacts   artifact suppression  

Expanding the Clinical Application of OPM-MEG: An Effective Method for Automatically Suppressing Metal Artifacts from Dental Braces Background Magnetoencephalography (MEG) is a technique that uses multi-channel magnetic field measurement sensors to reconstruct the neural current distribution and functional networks of the brain. Compared to electro...