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,...

Vision Transformers, Ensemble Model, and Transfer Learning Leveraging Explainable AI for Brain Tumor Detection and Classification

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Oncology,
brain tumor classification   deep learning   ensemble learning   multiclass classification   transfer learning   explainable AI   vision transformers  

In recent years, due to the high incidence and lethality of brain tumors, rapid and accurate detection and classification of brain tumors have become particularly important. Brain tumors include both malignant and non-malignant types, and their abnormal growth can cause long-term damage to the brain. Magnetic Resonance Imaging (MRI) is a commonly u...

Multi-view Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classification

Information Science, Computer Science, Artificial Intelligence, Medicine, Basic Medical Sciences, Medical Laboratory Science, Neurology, Life Sciences, Bioengineering,
sleep stage classification   spatial-temporal graph convolution   transfer learning   domain generalization   brain networks  

Sleep stage classification is crucial for sleep quality assessment and disease diagnosis. However, existing classification methods still face numerous challenges in handling the spatial and temporal features of time-varying multi-channel brain signals, coping with individual differences in biological signals, and model interpretability. Traditional...

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 Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology,
brain-computer interfaces   deep learning   euclidean alignment   EEG decoding   transfer learning  

Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding Background Introduction Electroencephalogram (EEG) signals are widely used in brain-computer interface (BCI) tasks due to their non-invasive nature, portability, and low acquisition cost. However, EEG signals suffer from low signal-to-noise ratio, sensitivity to electr...