TY - GEN
T1 - Decoding EEG by visual-guided deep neural networks
AU - Jiao, Zhicheng
AU - You, Haoxuan
AU - Yang, Fan
AU - Li, Xin
AU - Zhang, Han
AU - Shen, Dinggang
N1 - Funding Information:
In this research, we perform the deep learning experiments on a Titan V graphics card provided by the NVIDIA Academic Program of GPU Grant Program.
Publisher Copyright:
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Decoding visual stimuli from brain activities is an interdisciplinary study of neuroscience and computer vision. With the emerging of Human-AI Collaboration, Human-Computer Interaction, and the development of advanced machine learning models, brain decoding based on deep learning attracts more attention. Electroencephalogram (EEG) is a widely used neurophysiology tool. Inspired by the success of deep learning on image representation and neural decoding, we proposed a visual-guided EEG decoding method that contains a decoding stage and a generation stage. In the classification stage, we designed a visual-guided convolutional neural network (CNN) to obtain more discriminative representations from EEG, which are applied to achieve the classification results. In the generation stage, the visual-guided EEG features are input to our improved deep generative model with a visual consistence module to generate corresponding visual stimuli. With the help of our visual-guided strategies, the proposed method outperforms traditional machine learning methods and deep learning models in the EEG decoding task.
AB - Decoding visual stimuli from brain activities is an interdisciplinary study of neuroscience and computer vision. With the emerging of Human-AI Collaboration, Human-Computer Interaction, and the development of advanced machine learning models, brain decoding based on deep learning attracts more attention. Electroencephalogram (EEG) is a widely used neurophysiology tool. Inspired by the success of deep learning on image representation and neural decoding, we proposed a visual-guided EEG decoding method that contains a decoding stage and a generation stage. In the classification stage, we designed a visual-guided convolutional neural network (CNN) to obtain more discriminative representations from EEG, which are applied to achieve the classification results. In the generation stage, the visual-guided EEG features are input to our improved deep generative model with a visual consistence module to generate corresponding visual stimuli. With the help of our visual-guided strategies, the proposed method outperforms traditional machine learning methods and deep learning models in the EEG decoding task.
UR - http://www.scopus.com/inward/record.url?scp=85074915375&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2019/192
DO - 10.24963/ijcai.2019/192
M3 - Conference contribution
AN - SCOPUS:85074915375
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 1387
EP - 1393
BT - Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
A2 - Kraus, Sarit
PB - International Joint Conferences on Artificial Intelligence
T2 - 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Y2 - 10 August 2019 through 16 August 2019
ER -