An Improved Multidimensional Filter Bank Canonical Correlation Analysis for Recognition of SSVEP-Based BCIs

IF 4.6 2区计算机科学 Q2 ROBOTICS

IEEE Robotics and Automation Letters Pub Date : 2024-12-16 DOI:10.1109/LRA.2024.3518301

Songyu Niu;Di-Hua Zhai;Yuanqing Xia

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引用次数: 0

Abstract

This letter presents an improved multidimensional filter bank canonical correlation analysis (FBCCA) method for the brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP). This is a training-free SSVEP recognition method based on FBCCA, which integrates partial least squares regression (PLSR) and adaptive multidimensional extension (AME). Compared to FBCCA, this new method can further eliminate noise and artifacts from EEG signals during dimensionality reduction and regression by minimizing distribution errors. Additionally, it more effectively utilizes the valuable information from multi-channel EEG signals, thereby enhancing the recognition performance of SSVEP. Offline experiments conducted on two different open-source datasets verified that this method achieves advanced performance in training-free methods across different gaze times. In online tests on a real-time eight-target BCI system, the method achieved a peak accuracy of 98.44% and an information transfer rate (ITR) of 45.68 bits/min. This method improves the accuracy and efficiency of training-free SSVEP recognition, facilitating the wider application of BCI systems in real-life scenarios.

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基于ssvep的脑机接口识别改进多维滤波器组典型相关分析

本文提出了一种改进的基于稳态视觉诱发电位（SSVEP）的多维滤波组典型相关分析（FBCCA）方法。将偏最小二乘回归（PLSR）和自适应多维扩展（AME）相结合，提出了一种基于FBCCA的无训练SSVEP识别方法。与FBCCA方法相比，该方法通过减小分布误差，进一步消除了脑电信号降维和回归过程中的噪声和伪影。此外，该方法更有效地利用了多通道脑电信号中的有价值信息，从而提高了SSVEP的识别性能。在两个不同的开源数据集上进行的离线实验验证了该方法在不同凝视时间内的性能优于无训练方法。在实时八目标BCI系统的在线测试中，该方法的峰值准确率为98.44%，信息传输速率（ITR）为45.68 bits/min。该方法提高了无需训练的SSVEP识别的准确性和效率，促进了BCI系统在现实场景中的更广泛应用。

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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来源期刊

IEEE Robotics and Automation Letters Computer Science-Computer Science Applications

CiteScore

9.60

自引率

15.40%

发文量

1428

期刊介绍： The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.