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.
Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW, Australia.
Front Neurosci. 2022 Apr 4;16:792318. doi: 10.3389/fnins.2022.792318. eCollection 2022.
Brain-computer interfaces (BCI) relying on electroencephalography (EEG) based neuroimaging mode has shown prospects for real-world usage due to its portability and optional selectivity of fewer channels for compactness. However, noise and artifacts often limit the capacity of BCI systems especially for event-related potentials such as P300 and error-related negativity (ERN), whose biomarkers are present in short time segments at the time-series level. Contrary to EEG, invasive recording is less prone to noise but requires a tedious surgical procedure. But EEG signal is the result of aggregation of neuronal spiking information underneath the scalp surface and transforming the relevant BCI task's EEG signal to spike representation could potentially help improve the BCI performance. In this study, we designed an approach using a spiking neural network (SNN) which is trained using surrogate-gradient descent to generate task-related multi-channel EEG template signals of all classes. The trained model is in turn leveraged to obtain the latent spike representation for each EEG sample. Comparing the classification performance of EEG signal and its spike-representation, the proposed approach enhanced the performance of ERN dataset from 79.22 to 82.27% with naive bayes and for P300 dataset, the accuracy was improved from 67.73 to 69.87% using xGboost. In addition, principal component analysis and correlation metrics were evaluated on both EEG signals and their spike-representation to identify the reason for such improvement.
基于脑电图（EEG）的神经成像模式的脑机接口（BCI）因其便携性以及为实现紧凑性而选择较少通道的可选性，已展现出在现实世界中应用的前景。然而，噪声和伪迹常常限制了BCI系统的能力，特别是对于诸如P300和错误相关负波（ERN）等事件相关电位而言，其生物标志物在时间序列水平上仅存在于短时间段内。与EEG相反，侵入性记录较少受到噪声影响，但需要繁琐的外科手术过程。而EEG信号是头皮表面下神经元放电信息聚集的结果，将相关BCI任务的EEG信号转换为放电表示可能有助于提高BCI性能。在本研究中，我们设计了一种使用脉冲神经网络（SNN）的方法，该方法使用替代梯度下降进行训练，以生成所有类别的与任务相关的多通道EEG模板信号。然后利用训练好的模型为每个EEG样本获取潜在的放电表示。通过比较EEG信号及其放电表示的分类性能，所提出的方法在使用朴素贝叶斯时将ERN数据集的性能从79.22%提高到了82.27%，对于P300数据集，使用XGBoost时准确率从67.73%提高到了69.87%。此外，还对EEG信号及其放电表示进行了主成分分析和相关性度量评估，以确定这种性能提升的原因。
