On the use of pairwise distance learning for brain signal classification with limited observations
作者：
Highlights：
• Dedicated Siamese neural networks improve learnability from limited EEG recordings.
• Cosine-loss measuring distance of spectral content is more robust to noisy EEG data.
• Pairing schema and spectral processing handle spatiotemporal nature of EEG data.
• Comprehensive models of discriminative brain patterning found from resting state EEG.
摘要
•Dedicated Siamese neural networks improve learnability from limited EEG recordings.•Cosine-loss measuring distance of spectral content is more robust to noisy EEG data.•Pairing schema and spectral processing handle spatiotemporal nature of EEG data.•Comprehensive models of discriminative brain patterning found from resting state EEG.
论文关键词：Pairwise learning,Schizophrenia,Classification,Electroencephalography
论文评审过程：Received 23 October 2019, Revised 30 March 2020, Accepted 31 March 2020, Available online 11 May 2020, Version of Record 16 May 2020.
论文官网地址：https://doi.org/10.1016/j.artmed.2020.101852
