Singanamalla Sai Kalyan Ranga, Lin Chin-Teng
Computational Intelligence and Brain Computer Interface Lab, School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.
Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia.
Front Neurosci. 2021 Apr 1;15:651762. doi: 10.3389/fnins.2021.651762. eCollection 2021.
With the advent of advanced machine learning methods, the performance of brain-computer interfaces (BCIs) has improved unprecedentedly. However, electroencephalography (EEG), a commonly used brain imaging method for BCI, is characterized by a tedious experimental setup, frequent data loss due to artifacts, and is time consuming for bulk trial recordings to take advantage of the capabilities of deep learning classifiers. Some studies have tried to address this issue by generating artificial EEG signals. However, a few of these methods are limited in retaining the prominent features or biomarker of the signal. And, other deep learning-based generative methods require a huge number of samples for training, and a majority of these models can handle data augmentation of one category or class of data at any training session. Therefore, there exists a necessity for a generative model that can generate synthetic EEG samples with as few available trials as possible and generate multi-class while retaining the biomarker of the signal. Since EEG signal represents an accumulation of action potentials from neuronal populations beneath the scalp surface and as spiking neural network (SNN), a biologically closer artificial neural network, communicates via spiking behavior, we propose an SNN-based approach using surrogate-gradient descent learning to reconstruct and generate multi-class artificial EEG signals from just a few original samples. The network was employed for augmenting motor imagery (MI) and steady-state visually evoked potential (SSVEP) data. These artificial data are further validated through classification and correlation metrics to assess its resemblance with original data and in-turn enhanced the MI classification performance.
随着先进机器学习方法的出现，脑机接口（BCI）的性能得到了前所未有的提升。然而，脑电图（EEG）作为BCI常用的脑成像方法，具有实验设置繁琐、因伪迹导致频繁数据丢失以及大量试验记录耗时等特点，难以充分利用深度学习分类器的能力。一些研究试图通过生成人工EEG信号来解决这个问题。然而，其中一些方法在保留信号的显著特征或生物标志物方面存在局限性。而且，其他基于深度学习的生成方法需要大量样本进行训练，并且这些模型中的大多数在任何训练阶段只能处理一类数据的数据增强。因此，有必要存在一种生成模型，该模型能够用尽可能少的可用试验生成合成EEG样本，并在保留信号生物标志物的同时生成多类别样本。由于EEG信号代表头皮表面下方神经元群体动作电位的积累，并且由于脉冲神经网络（SNN）作为一种在生物学上更接近的人工神经网络，通过脉冲行为进行通信，我们提出一种基于SNN的方法，使用替代梯度下降学习从仅几个原始样本中重建并生成多类别人工EEG信号。该网络用于增强运动想象（MI）和稳态视觉诱发电位（SSVEP）数据。这些人工数据通过分类和相关性指标进一步验证，以评估其与原始数据的相似性，进而提高MI分类性能。
