A novel untrained SSVEP-EEG feature enhancement method using canonical correlation analysis and underdamped second-order stochastic resonance

Ruiquan Chen; Guanghua Xu; Huanqing Zhang; Xun Zhang; Baoyu Li; Jiahuan Wang; Sicong Zhang

doi:10.3389/fnins.2023.1246940

A novel untrained SSVEP-EEG feature enhancement method using canonical correlation analysis and underdamped second-order stochastic resonance

Front Neurosci. 2023 Oct 4:17:1246940. doi: 10.3389/fnins.2023.1246940. eCollection 2023.

Authors

Ruiquan Chen¹, Guanghua Xu², Huanqing Zhang¹, Xun Zhang¹, Baoyu Li¹, Jiahuan Wang¹, Sicong Zhang¹

Affiliations

¹ School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
² State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.

Abstract

Objective: Compared with the light-flashing paradigm, the ring-shaped motion checkerboard patterns avoid uncomfortable flicker or brightness modulation, improving the practical interactivity of brain-computer interface (BCI) applications. However, due to fewer harmonic responses and more concentrated frequency energy elicited by the ring-shaped checkerboard patterns, the mainstream untrained algorithms such as canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods have poor recognition performance and low information transmission rate (ITR).

Methods: To address this issue, a novel untrained SSVEP-EEG feature enhancement method using CCA and underdamped second-order stochastic resonance (USSR) is proposed to extract electroencephalogram (EEG) features.

Results: In contrast to typical unsupervised dimensionality reduction methods such as common average reference (CAR), principal component analysis (PCA), multidimensional scaling (MDS), and locally linear embedding (LLE), CCA exhibits higher adaptability for SSVEP rhythm components.

Conclusion: This study recruits 42 subjects to evaluate the proposed method and experimental results show that the untrained method can achieve higher detection accuracy and robustness.

Significance: This untrained method provides the possibility of applying a nonlinear model from one-dimensional signals to multi-dimensional signals.

Keywords: brain-computer interface; canonical correlation analysis; information transmission rate; motion checkerboard patterns; underdamped second-order stochastic resonance.

Grants and funding

This work was supported in part by the key Research and Development Projects of Shaanxi Province under grant no.2021ZD0204300, in part by the Science and Technology Plan Project of Xi’an under grant 20KYPT0001-10, and in part by the Key Research and Development Program of Shaanxi Province of China under grant 2021GXLH-Z-008.