Guanhua Zhang

Institute for Visualisation and Interactive Systems, University of Stuttgart

guanhua.zhang@vis.uni-stuttgart.de +49 711 685 Pfaffenwaldring 5a, 70569 Stuttgart, Germany University of Stuttgart, SimTech Building, Room 01.033

Biography

Guanhua Zhang is a PhD student in the Perceptual User Interfaces group since September 2020. She holds a Bachelor’s degree in Computer Science and Technology from Beijing University of Posts and Telecommunications and a Master’s degree in Computer Science and Technology from Tsinghua University. Her research interests include affective computing, wearable interaction and deep learning.

Publications

1. An Efficient LSTM Network for Emotion Recognition from Multichannel EEG Signals

   Xiaobing Du, Cuixia Ma, Guanhua Zhang, Jinyao Li, Yu-Kun Lai, Guozhen Zhao, Xiaoming Deng, Yong-Jin Liu, Hongan Wang

   IEEE Transactions on Affective Computing (TAC), 2020.

   Abstract Links BibTeX Project

   Most previous EEG-based emotion recognition methods studied hand-crafted EEG features extracted from different electrodes. In this paper, we study the relation among different EEG electrodes and propose a deep learning method to automatically extract the spatial features that characterize the functional relation between EEG signals at different electrodes. Our proposed deep model is called ATtention-based LSTM with Domain Discriminator (ATDD-LSTM) that can characterize nonlinear relations among EEG signals of different electrodes. To achieve state-of-the-art emotion recognition performance, the architecture of ATDD-LSTM has two distinguishing characteristics: (1) By applying the attention mechanism to the feature vectors produced by LSTM, ATDD-LSTM automatically selects suitable EEG channels for emotion recognition, which makes the learned model concentrate on the emotion related channels in response to a given emotion; (2) To minimize the significant feature distribution shift between different sessions and/or subjects, ATDD-LSTM uses a domain discriminator to modify the data representation space and generate domain-invariant features. We evaluate the proposed ATDD-LSTM model on three public EEG emotional databases (DEAP, SEED and CMEED) for emotion recognition. The experimental results demonstrate that our ATDD-LSTM model achieves superior performance on subject-dependent (for the same subject), subject-independent (for different subjects) and cross-session (for the same subject) evaluation.

   doi:10.1109/TAFFC.2020.3013711

   @inproceedings{du20_tac, title = {An Efficient LSTM Network for Emotion Recognition from Multichannel EEG Signals}, author = {Du, Xiaobing and Ma, Cuixia and Zhang, Guanhua and Li, Jinyao and Lai, Yu-Kun and Zhao, Guozhen and Deng, Xiaoming and Liu, Yong-Jin and Wang, Hongan}, year = {2020}, booktitle = {IEEE Transactions on Affective Computing (TAC)}, doi = {10.1109/TAFFC.2020.3013711} }

1. 

   A review of EEG features for emotion recognition (in Chinese)

   Guanhua Zhang, Minjing Yu, Guo Chen, Yiheng Han, Dan Zhang, Guozhen Zhao, Yong-Jin Liu

   SCIENTIA SINICA Informationis, 49(9), pp. 1097-1118, 2019.

   Abstract Links BibTeX Project

   Emotion recognition is an important research topic in the human-machine interaction field, and it can be applied to medicine, education, psychology, military, and other areas. Electroencephalogram (EEG) signals are mostly used among various indices of emotion recognition. High accuracy of emotion classifiers can be achieved by extracting the most relevant and discriminant features of emotion states. This study surveys EEG features that are extensively used in current emotion recognition studies by introducing EEG features from the following four viewpoints: time domain, frequency domain, time–frequency domain, and space domain. An SLDA algorithm is imported to three public EEG-emotion datasets (SEED, DREAMER, and CAS-THU) to evaluate feature capabilities that distinguish emotion valence. Existing problems and future investigations are also discussed in this paper.

   doi:10.1360/N112018-00337

   zhang19_ssi.pdf

   @article{zhang19_ssi, title = {A review of EEG features for emotion recognition (in Chinese)}, author = {Zhang, Guanhua and Yu, Minjing and Chen, Guo and Han, Yiheng and Zhang, Dan and Zhao, Guozhen and Liu, Yong-Jin}, year = {2019}, journal = {SCIENTIA SINICA Informationis}, doi = {10.1360/N112018-00337}, pages = {1097-1118}, volume = {49}, number = {9} }