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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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.