Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks.

In brain-computer interface (BCI), building accurate electroencephalogram (EEG) classifiers for specific mental tasks is critical for BCI performance. The classifiers are developed by machine learning (ML) and deep learning (DL) techniques, requiring a large dataset for training to build reliable and accurate models. However, collecting large enough EEG datasets is difficult due to intra-/inter-subject variabilities and experimental costs.

Spatio-Temporal Explanation of 3D-EEGNet for Motor Imagery EEG Classification Using Permutation and Saliency.

Recently, convolutional neural network (CNN)-based classification models have shown good performance for motor imagery (MI) brain-computer interfaces (BCI) using electroencephalogram (EEG) in end-to-end learning. Although a few explainable artificial intelligence (XAI) techniques have been developed, it is still challenging to interpret the CNN models for EEG-based BCI classification effectively. In this research, we propose 3D-EEGNet as a 3D CNN model to improve both the explainability and performance of MI EEG classification.

The surgical treatment for spinal intradural extramedullary tumors.

Background: We wanted to investigate the results of surgical treatment and analyze the factors that have an influence on the neurologic symptoms and prognosis of spinal intradural extramedullary (IDEM) tumors.

Methods: The spinal IDEM tumor patients (11 cases) who had been treated by surgical excision and who were followed up more than 1 year were retrospectively analyzed. Pain was evaluated by the visual analogue scale (VAS) and the neurologic function was assessed by Nurick's grade.