Guanhua Zhang

Institute for Visualisation and Interactive Systems, University of Stuttgart

+49 711 685 Pfaffenwaldring 5a, 70569 Stuttgart, Germany University of Stuttgart, SimTech Building, Room 01.033 Google Scholar

Biography

Guanhua Zhang is a PhD student in the Perceptual User Interfaces group since September 2020 and in the International Max Planck Research School for Intelligent Systems (IMPRS-IS) since February 2021. 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.

Teaching

2020

Hauptseminar Machine Learning Approaches to Understanding User BehaviorMaster

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Publications

(Zhang, Yu, Liu, Zhao, & Zhang, 2021; Zhao, Zhang, Zhang, Zhang, & Liu, 2020; Du et al., 2020; Zhang et al., 2019)

1. SparseDGCNN: Recognizing Emotion from Multichannel EEG Signals

   Guanhua Zhang, Minjing Yu, Yong-Jin Liu, Guozhen Zhao, Dan Zhang

   IEEE Transactions on Affective Computing (TAFFC), , pp. 1–12, 2021.

   Abstract Links BibTeX Project

   Emotion recognition from EEG signals has attracted much attention in affective computing. Recently, a novel dynamic graph convolutional neural network (DGCNN) model was proposed, which simultaneously optimized the network parameters and a weighted graph G characterizing the strength of functional relation between each pair of two electrodes in the EEG recording equipment. In this paper, we propose a sparse DGCNN model which improves the DGCNN by imposing a sparseness constraint on G. Our work is based on an important observation: the tomography study reveals that different brain regions sampled by EEG electrodes may be related to different functions of the brain and then the functional relations among electrodes are possibly highly localized and sparse. However, introducing sparseness constraint into the graph G makes the loss function of sparse DGCNN non-differentiable at some singular points. To ensure that the training process of sparse DGCNN converges, we apply the forward-backward splitting method. To evaluate the performance of sparse DGCNN, we compare it with four representative recognition methods as well as different features and spectral bands. The results show that (1) sparse DGCNN has consistently better accuracy than representative methods and has a good scalability, and (2) DE, PSD and ASM features on γ bands convey most discriminative emotional information, and fusion of separate features and frequency bands can improve recognition performance.

   doi: 10.1109/TAFFC.2021.3051332

   @article{zhang21_taffc, title = {SparseDGCNN: Recognizing Emotion from Multichannel EEG Signals}, author = {{Zhang}, Guanhua and {Yu}, Minjing and {Liu}, Yong-Jin and {Zhao}, Guozhen and {Zhang}, Dan}, journal = {IEEE Transactions on Affective Computing (TAFFC)}, doi = {10.1109/TAFFC.2021.3051332}, year = {2021}, pages = {1--12} }

1. Multi-target Positive Emotion Recognition from EEG Signals

   Guozhen Zhao, Yulin Zhang, Guanhua Zhang, Dan Zhang, Yong-Jin Liu

   IEEE Transactions on Affective Computing (TAFFC), , pp. 1-13, 2020.

   Abstract Links BibTeX Project

   Compared with the widely studied negative emotions in which different classes are easy to distinguish, nowadays less attention is paid to the recognition of positive emotions that are not fully independent. In this paper, we propose to recognize multiple positive emotions by analyzing brain activities and explore the neural representation of different positive emotions. Thirty-seven participants volunteered to participate in our study, in which their brain activities were recorded when watching five selected film clips. First, 150 well-known power features extracted from Electroencephalography (EEG) signals and 105 multimedia content analysis features were collected as the pool of candidate features. Second, based on the collected features, we propose to use a linear model and a nonlinear model to predict the percentage of five positive emotions. Then, percentage values were converted to ranking numbers and Kendall rank correlation coefficients were calculated. Our results showed that (1) ensemble of regressor chains using LSTM as unit regressor obtained both the best regression results and the best Kendall rank correlation coefficient on EEG features merely, and (2) top features from alpha frequency bands of EEG signals could represent different positive emotions. These results demonstrate the effectiveness of selective EEG features on recognizing different positive emotions.

   doi: 10.1109/TAFFC.2020.3043135

   @article{zhao20_taffc, title = {Multi-target Positive Emotion Recognition from EEG Signals}, author = {Zhao, Guozhen and Zhang, Yulin and Zhang, Guanhua and Zhang, Dan and Liu, Yong-Jin}, year = {2020}, journal = {IEEE Transactions on Affective Computing (TAFFC)}, doi = {10.1109/TAFFC.2020.3043135}, pages = {1-13} }

2. 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 (TAFFC), , pp. 1-12, 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

   @article{du20_taffc, 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}, journal = {IEEE Transactions on Affective Computing (TAFFC)}, doi = {10.1109/TAFFC.2020.3013711}, pages = {1-12} }

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

   Paper: 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} }