TY - GEN
T1 - Reconstructing the Perceived Faces from Brain Signals without Large Number of Training Samples
AU - Zhang, H.
AU - Wei, Z. X.
AU - Zhou, J. Q.
AU - Tian, J.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Reconstructing the perceived faces from brain signals has become a promising work recently. However, the reconstruction accuracies rely on a large number of brain signals collected for training a stable reconstruction model, which is really time consuming, and greatly limits its application. In our current study, we develop a new framework that can efficiently perform high-quality face reconstruction with only a small number of brain signals as training samples. The framework consists of three mathematical models: principle component analysis (PCA), linear regression (LR) and conditional generative adversarial network (cGAN). We conducted a functional Magnetic Resonance Imaging (fMRI) experiment in which two subjects' brain signals were collected to test the efficiency of our proposed method. Results show that we can achieve state-of-the-art reconstruction performance from brain signals with a very limited number of fMRI training samples.
AB - Reconstructing the perceived faces from brain signals has become a promising work recently. However, the reconstruction accuracies rely on a large number of brain signals collected for training a stable reconstruction model, which is really time consuming, and greatly limits its application. In our current study, we develop a new framework that can efficiently perform high-quality face reconstruction with only a small number of brain signals as training samples. The framework consists of three mathematical models: principle component analysis (PCA), linear regression (LR) and conditional generative adversarial network (cGAN). We conducted a functional Magnetic Resonance Imaging (fMRI) experiment in which two subjects' brain signals were collected to test the efficiency of our proposed method. Results show that we can achieve state-of-the-art reconstruction performance from brain signals with a very limited number of fMRI training samples.
UR - https://www.scopus.com/pages/publications/85091006327
U2 - 10.1109/EMBC44109.2020.9176684
DO - 10.1109/EMBC44109.2020.9176684
M3 - 会议稿件
AN - SCOPUS:85091006327
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1108
EP - 1111
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -