Evomoe: Evolutionary Mixture-of-Experts for SSVEP-EEG Classification with User-Independent Training

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology, Life Sciences, Bioengineering, Biophysics,
brain-computer interface   electroencephalography   evolutionary mixture of experts   individual differences   user-independent training   SSVEP   transfer learning  

Interpretation of “EVOMOE: Evolutionary Mixture-of-Experts for SSVEP-EEG Classification with User-Independent Training” 1. Research Background and Problem Statement Brain-computer interface (BCI) technology has recently shown broad application prospects in neuroengineering, assistive technology for disabilities, rehabilitation, emotion recognition,...

Time Synchronization Between Parietal–Frontocentral Connectivity with MRCP and Gait in Post-Stroke Bipedal Tasks

Engineering, Electrical Engineering, Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging,
stroke   brain-computer interface   functional connectivity   motor execution   motor preparation   bipedal classification   rehabilitation  

Time Synchronization of Motor-Related Cortical Potentials and Parieto-Frontocentral Connectivity in Bilateral Tasks of Stroke Patients Background In stroke rehabilitation research, functional connectivity (FC), motor-related cortical potentials (MRCP), and gait activities are common metrics related to rehabilitation outcomes. Although these have be...

Multi-Feature Attention Convolutional Neural Network for Motor Imagery Decoding

Engineering, Electrical Engineering, Information Science, Automation, Artificial Intelligence, Medicine, Medical Laboratory Science, Neurology, Life Sciences, Bioengineering,
multi-feature attention   convolutional neural networks   motor imagery   brain-computer interface   lightweight model  

Brain-Computer Interface (BCI) is a communication method that connects the nervous system to the external environment. Motor Imagery (MI) is the cornerstone of BCI research, referring to the internal rehearsal before physical execution. Non-invasive techniques such as Electroencephalography (EEG) can record neural activities with high temporal reso...

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...

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...