期刊
2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021)
卷 -, 期 -, 页码 1255-1259出版社
IEEE
DOI: 10.1109/ICASSP39728.2021.9413712
关键词
eeg; electroencephalogram; convolutional network; self-attention; seizure; transfer learning
CHARM is a method for training a single neural network across inconsistent input channels, estimating a latent reordering from each input signal and mapping input channels to a canonical order using attention mechanisms. Experimental results demonstrate the efficacy of CHARM in improving transfer of pre-trained representations between datasets collected with different protocols.
We propose CHARM, a method for training a single neural network across inconsistent input channels. Our work is motivated by Electroencephalography (EEG), where data collection protocols from different headsets result in varying channel ordering and number, which limits the feasibility of transferring trained systems across datasets. Our approach builds upon attention mechanisms to estimate a latent reordering matrix from each input signal and map input channels to a canonical order. CHARM is differentiable and can be composed further with architectures expecting a consistent channel ordering to build end-to-end trainable classifiers. We perform experiments on four EEG classification datasets and demonstrate the efficacy of CHARM via simulated shuffling and masking of input channels. Moreover, our method improves the transfer of pre-trained representations between datasets collected with different protocols.
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