Publications by authors named "Zhuobin Yang"
Article Synopsis
- Emotion recognition is vital for enhancing human-computer interaction, and the study introduces the Mixed Attention-based Convolution and Transformer Network (MACTN) as a novel model using EEG data to capture emotional states.
- The model employs depth-wise and separable convolutions for local feature extraction and a self-attention-based transformer for global context, along with channel attention to optimize emotion-channel relationships.
- MACTN shows significant improvements in emotion recognition accuracy in both online and offline evaluations and won the Emotional BCI Competition at the World Robot Contest, with its source code available for public use.
Patient-dependent seizure detection based on intracranial electroencephalography (iEEG) has made significant progress. However, due to the difference in the locations and number of iEEG electrodes used for each patient, patient-independent seizure detection based on iEEG has not been carried out. Additionally, current seizure detection algorithms based on deep learning have outperformed traditional machine learning algorithms in many performance metrics.
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