论文标题
从异质的EEG信号中学习，并重新排序可区分的频道
Learning from Heterogeneous EEG Signals with Differentiable Channel Reordering
论文作者
论文摘要
我们提出了魅力，这是一种跨输入通道训练单个神经网络的方法。我们的工作是由脑电图（EEG）激励的，其中来自不同耳机的数据收集协议导致频道顺序和数字不同，这限制了跨数据集传输训练有素的系统的可行性。我们的方法建立在注意机制上，以估算每个输入信号的潜在重新排序矩阵，并映射输入通道到规范顺序。魅力是可微分的，可以通过架构进行进一步组成，期望一致的渠道订购端到端可训练的分类器。我们在四个EEG分类数据集上执行实验，并通过模拟输入通道的模拟洗牌和掩盖来证明魅力的功效。此外，我们的方法改善了使用不同协议收集的数据集之间的预训练表示的转移。
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.
