通过基于生成式对抗网络的新型框架扩展短时长 SSVEP 数据

IF 3.1 3区 工程技术 Q2 NEUROSCIENCES Cognitive Neurodynamics Pub Date : 2024-05-31 DOI:10.1007/s11571-024-10134-9
Yudong Pan, Ning Li, Yangsong Zhang, Peng Xu, Dezhong Yao
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引用次数: 0

摘要

基于稳态视觉诱发电位(SSVEPs)的脑机接口(BCI)因其高信息传输率(ITR)和可用目标数量而受到广泛关注。然而,频率识别方法的性能在很大程度上取决于用户校准数据量和数据长度,这阻碍了其在实际应用中的部署。最近,基于生成式对抗网络(GANs)的数据生成方法被广泛用于创建合成脑电图数据,有望解决这些问题。本文提出了一种基于生成式对抗网络(GAN)的端到端信号转换网络,用于时间窗口长度扩展(Time-window length Extension),称为 TEGAN。TEGAN 可将短时长的 SSVEP 信号转换成长时长的人工 SSVEP 信号。此外,我们还引入了两阶段训练策略和 LeCam-发散正则化项,以便在网络实施过程中对 GAN 的训练过程进行正则化。我们在两个公开的 SSVEP 数据集(4 级和 12 级数据集)上对所提出的 TEGAN 进行了评估。在 TEGAN 的帮助下,传统频率识别方法和基于深度学习的方法在有限的校准数据下的性能得到了显著提高。各种频率识别方法的分类性能差距也有所缩小。本研究证实了所提出的方法在延长短时 SSVEP 信号的数据长度以开发高性能 BCI 系统方面的可行性。所提出的基于 GAN 的方法在缩短校准时间和削减各种基于 BCI 的实际应用预算方面具有巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Short-length SSVEP data extension by a novel generative adversarial networks based framework

Steady-state visual evoked potentials (SSVEPs) based brain–computer interface (BCI) has received considerable attention due to its high information transfer rate (ITR) and available quantity of targets. However, the performance of frequency identification methods heavily hinges on the amount of user calibration data and data length, which hinders the deployment in real-world applications. Recently, generative adversarial networks (GANs)-based data generation methods have been widely adopted to create synthetic electroencephalography data, holds promise to address these issues. In this paper, we proposed a GAN-based end-to-end signal transformation network for Time-window length Extension, termed as TEGAN. TEGAN transforms short-length SSVEP signals into long-length artificial SSVEP signals. Additionally, we introduced a two-stage training strategy and the LeCam-divergence regularization term to regularize the training process of GAN during the network implementation. The proposed TEGAN was evaluated on two public SSVEP datasets (a 4-class and 12-class dataset). With the assistance of TEGAN, the performance of traditional frequency recognition methods and deep learning-based methods have been significantly improved under limited calibration data. And the classification performance gap of various frequency recognition methods has been narrowed. This study substantiates the feasibility of the proposed method to extend the data length for short-time SSVEP signals for developing a high-performance BCI system. The proposed GAN-based methods have the great potential of shortening the calibration time and cutting down the budget for various real-world BCI-based applications.

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来源期刊
Cognitive Neurodynamics
Cognitive Neurodynamics 医学-神经科学
CiteScore
6.90
自引率
18.90%
发文量
140
审稿时长
12 months
期刊介绍: Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models. The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome. The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged. 1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics. 2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages. 3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.
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