Your browser doesn't support javascript.
loading
A Methodology for Enhancing SSVEP Features Using Adaptive Filtering Based on the Spatial Distribution of EEG Signals.
Wang, Shengyu; Ji, Bowen; Shao, Dian; Chen, Wanru; Gao, Kunpeng.
Afiliação
  • Wang S; School of Information Science and Technology, Donghua University, Shanghai 201620, China.
  • Ji B; Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China.
  • Shao D; Innovation Center NPU Chongqing, Northwestern Polytechnical University, Chongqing 401135, China.
  • Chen W; Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China.
  • Gao K; School of Information Science and Technology, Donghua University, Shanghai 201620, China.
Micromachines (Basel) ; 14(5)2023 Apr 29.
Article em En | MEDLINE | ID: mdl-37241600
ABSTRACT
In this paper, we propose a classification algorithm of EEG signal based on canonical correlation analysis (CCA) and integrated with adaptive filtering. It can enhance the detection of steady-state visual evoked potentials (SSVEPs) in a brain-computer interface (BCI) speller. An adaptive filter is employed in front of the CCA algorithm to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. The ensemble method is developed to integrate recursive least squares (RLS) adaptive filter corresponding to multiple stimulation frequencies. The method is tested by the SSVEP signal recorded from six targets by actual experiment and the EEG in a public SSVEP dataset of 40 targets from Tsinghua University. The accuracy rates of the CCA method and the CCA-based integrated RLS filter algorithm (RLS-CCA method) are compared. Experiment results show that the proposed RLS-CCA-based method significantly improves the classification accuracy compared with the pure CCA method. Especially when the number of EEG leads is low (three occipital electrodes and five non occipital electrodes), its advantage is more significant, and accuracy reaches 91.23%, which is more suitable for wearable environments where high-density EEG is not easy to collect.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Micromachines (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Micromachines (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China