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...

Active Dynamic Weighting for Multi-Domain Adaptation

Information Science, Artificial Intelligence,
multi-source domain adaptation   transfer learning   active learning   distribution alignment   dynamic boundary loss  

Background Introduction Multi-source Unsupervised Domain Adaptation (MUDA) aims to transfer knowledge from multiple labeled source domains to an unlabeled target domain. However, existing methods often merely seek to blend distributions between different domains or combine multiple single-source models in the decision process through weighted fusio...