Xiaolong Wu
Nanchang University
6 Papers
Xiaolong Wu is an academic researcher from Nanchang University. The author has contributed to research in topics: Computer science & Medicine. The author has co-authored 1 publications.
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Papers
Decoding continuous kinetic information of grasp from stereo-electroencephalographic (SEEG) recordings
Xiaolong Wu,Guangye Li,Shize Jiang,Scott Wellington,Shengjie Liu,Zehan Wu,Benjamin Metcalfe,Liang Chen,Dingguo Zhang +8 more
TL;DR: The result presented in this study demonstrated the potential of SEEG recordings for future BCI application and verified the possibility of decoding continuously changing grasp force using SEEg recordings.
11
Multi-feature fusion method based on WOSF and MSE for four-class MI EEG identification
TL;DR: The proposed Multi-Feature Fusion Method based on Wavelength Optimal Spatial Filter and Multiscale Entropy for classifying the electroencephalogram signals (EEG) in four kinds of motor imagery tasks can effectively improve the accuracy of EEG classification in multiclass motor imagery and will be useful for neurorehabilitation through motor imagery for hemiplegic patients.
8
Data augmentation for invasive brain-computer interfaces based on stereo-electroencephalography (SEEG).
Xiaolong Wu,Guangye Li,Xin Gao,Benjamin Metcalfe,Liang Chen +4 more
TL;DR: This paper demonstrated that a generative model that preserves temporal dependence is superior in data generation and boosting deep learning performance for SEEG signals, the first time that DA methods are applied to invasive BCIs based on SEEG.
2
The superiority verification of morphological features in the EEG-based assessment of depression
Xiaolong Wu,Jianhong Yang +1 more
TL;DR: In this article , morphological features combine the two perspectives of EEG rhythm and potential to distinguish depression group better than classifiers and LSTM based on morphological pattern encoding facilitate depression assessment.
Deep Learning With Convolutional Neural Networks for Motor Brain-Computer Interfaces Based on Stereo-Electroencephalography (SEEG)
TL;DR: In this article , the decoding performance of deep learning methods on SEEG signals was evaluated, and it was shown that an extra spatial convolution layer was beneficial, and the decoding process can be partially interpreted from spatial and spectral perspectives.