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

被引：0

作者：

Niu, Songyu ^{[1
]}

Zhai, Di-Hua ^{[1
,2
]}

Xia, Yuanqing ^{[1
,3
]}

机构：

[1] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China

[2] Beijing Inst Technol, Yangtze Delta Reg Acad, Jiaxing 314001, Peoples R China

[3] Zhongyuan Univ Technol, Zhengzhou 100081, Peoples R China

来源：

IEEE ROBOTICS AND AUTOMATION LETTERS | 2025年 / 10卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Electroencephalography; Filter banks; Accuracy; Correlation; Visualization; Feature extraction; Vectors; Training; Steady-state; Correlation coefficient; Brain-machine interfaces; human-robot collaboration; intention recognition; steady-state visual evokedpotential (SSVEP); FREQUENCY RECOGNITION; BRAIN;

D O I：

10.1109/LRA.2024.3518301

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

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.

引用

页码：939 / 946

页数：8

共 19 条

[1] Force decoding using local field potentials in primary motor cortex: PLS or Kalman filter regression? [J].

Beni, Nargess Heydari ;

Foodeh, Reza ;

Shalchyan, Vahid ;

Daliri, Mohammad Reza .

PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE, 2020, 43 (01) :175-186

[2] Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface [J].

Chen, Xiaogang ;

Wang, Yijun ;

Gao, Shangkai ;

Jung, Tzyy-Ping ;

Gao, Xiaorong .

JOURNAL OF NEURAL ENGINEERING, 2015, 12 (04)

[3] A novel training-free recognition method for SSVEP-based BCIs using dynamic window strategy [J].

Chen, Yonghao ;

Yang, Chen ;

Chen, Xiaogang ;

Wang, Yijun ;

Gao, Xiaorong .

JOURNAL OF NEURAL ENGINEERING, 2021, 18 (03)

[4] A Deep Neural Network for SSVEP-Based Brain-Computer Interfaces [J].

Guney, Osman Berke ;

Oblokulov, Muhtasham ;

Ozkan, Huseyin .

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2022, 69 (02) :932-944

[5] Canonical correlation analysis: An overview with application to learning methods [J].

Hardoon, DR ;

Szedmak, S ;

Shawe-Taylor, J .

NEURAL COMPUTATION, 2004, 16 (12) :2639-2664

[6] A Cross-Space CNN With Customized Characteristics for Motor Imagery EEG Classification [J].

Hu, Ying ;

Liu, Yan ;

Zhang, Siqi ;

Zhang, Ting ;

Dai, Bin ;

Peng, Bo ;

Yang, Hongbo ;

Dai, Yakang .

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2023, 31 :1554-1565

[7] SSVEP-Based Brain-Computer Interface for Part-Picking Robotic Co-Worker [J].

Li, Yao ;

Kesavadas, Thenkurussi .

JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING, 2022, 22 (02)

[8] Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs [J].

Lin, Zhonglin ;

Zhang, Changshui ;

Wu, Wei ;

Gao, Xiaorong .

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2006, 53 (12) :2610-2614

[9] BETA: A Large Benchmark Database Toward SSVEP-BCI Application [J].

Liu, Bingchuan ;

Huang, Xiaoshan ;

Wang, Yijun ;

Chen, Xiaogang ;

Gao, Xiaorong .

FRONTIERS IN NEUROSCIENCE, 2020, 14

[10] Indoor Simulated Training Environment for Brain-Controlled Wheelchair Based on Steady-State Visual Evoked Potentials [J].

Liu, Ming ;

Wang, Kangning ;

Chen, Xiaogang ;

Zhao, Jing ;

Chen, Yuanyuan ;

Wang, Huiquan ;

Wang, Jinhai ;

Xu, Shengpu .

FRONTIERS IN NEUROROBOTICS, 2020, 13 :1-15

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