Task-oriented EEG denoising generative adversarial network for enhancing SSVEP-BCI performance.

Pu Zeng, Liangwei Fan, You Luo, Hui Shen, Dewen Hu
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Abstract

Objective.The quality of electroencephalogram (EEG) signals directly impacts the performance of brain-computer interface (BCI) tasks. Many methods have been proposed to eliminate noise from EEG signals, but most of these methods focus solely on signal denoising itself, disregarding the impact on subsequent tasks, which deviates from the original intention of EEG denoising. The main objective of this study is to optimize EEG denoising models with a purpose of improving the performance of BCI tasks.Approach.To this end, we proposed an innovative task-oriented EEG denoising generative adversarial network (TOED-GAN) method. This network utilizes the generator of GAN to decompose and reconstruct clean signals from the raw EEG signals, and the discriminator to learn to distinguish the generated signals from the true clean signals, resulting in a remarkable increase of the signal-to-noise ratio by simultaneously enhancing task-related components and removing task-irrelevant noise from the original contaminated signals.Main results.We evaluated the performance of the model on a public dataset and a self-collected dataset respectively, with canonical correlation analysis classification tasks of the steady-state visual evoked potential (SSVEP) based BCI. Experimental results demonstrate that TOED-GAN exhibits excellent performance in removing EEG noise and improving performance for SSVEP-BCI, with accuracy improvement rates reaching 18.47% and 21.33% in contrast to the baseline methods of convolutional neural networks, respectively.Significance.This work proves that the proposed TOED-GAN, as an EEG denoising method tailored for SSVEP tasks, contributes to enhancing the performance of BCIs in practical application scenarios.

面向任务的脑电图去噪生成对抗网络,用于提高 SSVEP-BCI 性能。
目的: 脑电图(EEG)信号的质量直接影响脑机接口(BCI)任务的性能。人们提出了许多方法来消除脑电信号中的噪声,但这些方法大多只关注信号去噪本身,而忽略了对后续任务的影响,这偏离了脑电去噪的初衷。本研究的主要目的是优化脑电图去噪模型,以提高 BCI 任务的性能。 为此,我们提出了一种创新的任务导向脑电图去噪生成对抗网络(TOED-GAN)方法。该网络利用 GAN 的生成器从原始脑电信号中分解和重建干净信号,并利用鉴别器学习如何将生成的信号与真正的干净信号区分开来,从而通过同时增强任务相关成分和去除原始污染信号中与任务无关的噪声,显著提高信噪比 (SNR)。 主要结果 我们分别在一个公共数据集和一个自选数据集上评估了该模型的性能,并对基于稳态视觉诱发电位(SSVEP)的BCI进行了典型相关分析(CCA)分类任务。实验结果表明,TOED-GAN 在去除脑电噪声和提高 SSVEP-BCI 性能方面表现出色,与卷积神经网络的基线方法相比,准确率分别提高了 18.47% 和 21.33% 意义 这项工作证明,所提出的 TOED-GAN 作为一种为 SSVEP 任务定制的脑电去噪方法,有助于提高 BCI 在实际应用场景中的性能。
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