论文标题
针对基于脑电图的BCI的生理信息增强
Towards physiology-informed data augmentation for EEG-based BCIs
论文作者
论文摘要
大多数基于脑电图的大脑计算机界面（BCIS）都需要大量的培训数据来校准分类模型，这是因为脑电图数据的差异很高，EEG数据的变异性很高，这些数据在参与者之间表现出来，但从会议之间的参与者到参与者（当然，从试验到试验）。通常，模型越复杂，需要训练的数据就越多。我们建议一种新颖的技术来通过从手头数据集中生成新数据来增强培训数据。与现有技术不同，我们的方法使用源本地化和HEAD模型使用向后和正向投影来修改模型的当前源偶极子，从而以生理意义的方式生成参与性的变异性。在本手稿中，我们解释了该方法并显示了独立于参与者的运动塑料分类的第一个初步结果。当使用深层神经网络，浅神经网络和LDA使用所提出的数据增强方法时，精度提高了13、6和2个百分点。
Most EEG-based Brain-Computer Interfaces (BCIs) require a considerable amount of training data to calibrate the classification model, owing to the high variability in the EEG data, which manifests itself between participants, but also within participants from session to session (and, of course, from trial to trial). In general, the more complex the model, the more data for training is needed. We suggest a novel technique for augmenting the training data by generating new data from the data set at hand. Different from existing techniques, our method uses backward and forward projection using source localization and a head model to modify the current source dipoles of the model, thereby generating inter-participant variability in a physiologically meaningful way. In this manuscript, we explain the method and show first preliminary results for participant-independent motor-imagery classification. The accuracy was increased when using the proposed method of data augmentation by 13, 6 and 2 percentage points when using a deep neural network, a shallow neural network and LDA, respectively.
