Area of research
Cognitive Neuroscience · Artificial Intelligence
Research interest
Research interests include EEG and Brain-Computer Interfaces, Blind Source Separation Techniques, Advanced Graph Neural Networks, and Topic Modeling.
Explainable End-to-End Seizure Prediction via Dynamic Multiscale Cross-Band Fusion Filter Network.
Lightweight Seizure Prediction Model based on Kernel-Enhanced Global Temporal Attention.
TriFusionNet: Multiscale dual-stream and dimension-adaptive fusion network for epileptic seizure prediction
DyMsCL: A dynamic multiscale contrastive learning framework for explainable end-to-end epileptic seizure detection
Ultrasound Active Piezoelectric Nanostimulators for Long‐Acting Wireless Deep Brain Electric Stimulation to Inhibit Epileptic Seizures
A topic-specific representation learning framework for acoustic scene classification
Efficient EEG Feature Learning Model Combining Random Convolutional Kernel with Wavelet Scattering for Seizure Detection.
Compact Convolutional Neural Network with Multi-Headed Attention Mechanism for Seizure Prediction
Compact Convolutional Neural Network with Multi-Headed Attention Mechanism for Seizure Prediction.
Both Cross-Patient and Patient-Specific Seizure Detection Based on Self-Organizing Fuzzy Logic.
EEG-Based Seizure detection using linear graph convolution network with focal loss.
Graph Attention Network with Focal Loss for Seizure Detection on Electroencephalography Signals.
Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding
Detecting Phishing Scams on Ethereum Based on Transaction Records
T-EDGE: Temporal WEighted MultiDiGraph Embedding for Ethereum Transaction Network Analysis
Recent advances and perspectives on constructing metal oxide semiconductor gas sensing materials for efficient formaldehyde detection
Scalp EEG classification using deep Bi-LSTM network for seizure detection.
Phishing Detection on Ethereum via Learning Representation of Transaction Subgraphs
Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces.
Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning
Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning.
Automatic seizure detection based on kernel robust probabilistic collaborative representation.
Epileptic seizure detection based on imbalanced classification and wavelet packet transform
EPILEPTIC EEG CLASSIFICATION BASED ON KERNEL SPARSE REPRESENTATION
Feature extraction and recognition of ictal EEG using EMD and SVM
Seizure Prediction Using Spike Rate of Intracranial EEG
Epileptic Seizure Detection Using Lacunarity and Bayesian Linear Discriminant Analysis in Intracranial EEG
Automatic Seizure Detection Using Wavelet Transform and SVM in Long-Term Intracranial EEG
Epileptic seizure detection with linear and nonlinear features