Improvement of brain–computer interface in motor imagery training through the designing of a dynamic experiment and FBCSP

Lin, Chun‐Ling; Chen, Liang-Ting

doi:10.1016/j.heliyon.2023.e13745

Cited by 11 publications

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MI trials are band-pass filtered using a fifth-order Butterworth band-pass filter from 8 to 30 Hz. The feature extraction by the CSP that is widely employed in MI-BCI systems [41]. It is a spatially filtered feature extraction method for classification tasks and aims at extracting the spatial components of each class from raw MI trials.…”

Section: Csp-based MI Feature Extractionmentioning

confidence: 99%

Personalized motor imagery prediction model based on individual difference of ERP

Deng,

Li,

Zuo

et al. 2024

J. Neural Eng.

View full text Add to dashboard Cite

Objective. Motor imagery-based brain-computer interaction (MI-BCI) is a novel method of achieving human and external environment interaction that can assist individuals with motor disorders to rehabilitate. However, individual differences limit the utility of the MI-BCI. In this study, a personalized MI prediction model based on the individual difference of event-related potential (ERP) is proposed to solve the MI individual difference. Approach. A novel paradigm named action observation-based multi-delayed matching posture task (AO-multi-DMPT) evokes ERP during a DMPT phase by retrieving picture stimuli and videos, and generates MI EEG through action observation and autonomous imagery in an AO-MI phase. Based on the correlation between the ERP and MI, a logistic regression-based personalized MI prediction model is built to predict each individual‘s suitable MI action. 32 subjects conducted the MI task with or without the help of the prediction model to select the MI action. Then classification accuracy of the MI task is used to evaluate the proposed model and three traditional MI methods. Main results. The personalized MI prediction model successfully predicts suitable action among 3 sets of daily actions. Under suitable MI action, the individual’s ERP amplitude and ERD intensity are the largest, which helps to improve the accuracy by 14.25%. Significance. The personalized MI prediction model that uses the temporal ERP features to predict the classification accuracy of MI is feasible for improving the individual’s MI-BCI performance, providing a new personalized solution for the individual difference and practical BCI application.

show abstract

Section: Csp-based MI Feature Extractionmentioning

confidence: 99%