A auto-segmented multi-time window dual-scale neural network for brain-computer interfaces based on event-related potentials

计算机科学 模式识别(心理学) 卷积神经网络 卷积(计算机科学) 脑电图 人工智能 窗口(计算) 脑-机接口 人工神经网络 集合(抽象数据类型) 心理学 操作系统 精神科 程序设计语言
作者
Xueqing Zhao,Ren Xu,Ruitian Xu,Xingyu Wang,Andrzej Cichocki,Jing Jin
出处
期刊:Journal of Neural Engineering [IOP Publishing]
卷期号:21 (4): 046008-046008
标识
DOI:10.1088/1741-2552/ad558a
摘要

Abstract Objective. Event-related potentials (ERPs) are cerebral responses to cognitive processes, also referred to as cognitive potentials. Accurately decoding ERPs can help to advance research on brain-computer interfaces (BCIs). The spatial pattern of ERP varies with time. In recent years, convolutional neural networks (CNNs) have shown promising results in electroencephalography (EEG) classification, specifically for ERP-based BCIs. Approach. This study proposes an auto-segmented multi-time window dual-scale neural network (AWDSNet). The combination of a multi-window design and a lightweight base network gives AWDSNet good performance at an acceptable cost of computing. For each individual, we create a time window set by calculating the correlation of signed R -squared values, which enables us to determine the length and number of windows automatically. The signal data are segmented based on the obtained window sets in sub-plus-global mode. Then, the multi-window data are fed into a dual-scale CNN model, where the sizes of the convolution kernels are determined by the window sizes. The use of dual-scale spatiotemporal convolution focuses on feature details while also having a large enough receptive length, and the grouping parallelism undermines the increase in the number of parameters that come with dual scaling. Main results. We evaluated the performance of AWDSNet on a public dataset and a self-collected dataset. A comparison was made with four popular methods including EEGNet, DeepConvNet, EEG-Inception, and PPNN. The experimental results show that AWDSNet has excellent classification performance with acceptable computational complexity. Significance. These results indicate that AWDSNet has great potential for applications in ERP decoding.
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