联合 SG 滤波和扩展典型相关性分析的脑控轮椅

The Brain-Controlled Wheelchair (BCW) integrates Brain-Computer Interface (BCI) technology with an electric wheelchair,empowering individuals with motor impairments to maneuver the wheelchair by using their thoughts,thereby enhancing their quality of life.However,the efficacy and practical applicability of the existing BCW system necessitate urgent enhancement.This paper integrates steady-state visual evoked potentials and electrooculography (EOG) to develop an embedded asynchronous BCW system,aiming to enhance its overall performance and practical utility.First,by leveraging the distinct characteristics of EOG waveform peaks and troughs,the slope threshold method is employed for real-time detection of blink events,thereby provisioning an asynchronous control mechanism for the initiation and cessation of the BCW system.Second,a Savitzky-Golay filter based wavelet packet decomposition is proposed for the smoothing and filtering of EEG signals,with its parameters optimized through grid search,effectively eliminating various low-frequency motion artifacts while preserving the original information.Employing extended canonical correlation analysis to identify the frequency components that represent specific visually evoked activities within the signal,and constructing signal templates and artificially generated reference signals by using offline datasets to comprehensively compute signal correlations and decode them,thereby providing accurate control commands for the BCW system.Finally,the proposed algorithm is integrated into embedded devices,with the effectiveness of the embedded BCW system verified through experimental validation.Experimental results show that through the online BCW evaluation experimental results the average accuracy of blink event detection in the straight line test scene is 83.29%,that the average classification accuracy of online EEG signals is 82.93%,and that the task completion rate is up to 87.5%.The average accuracy of blink event detection in complex environment test scenarios is 83.66%,the average classification accuracy of online EEG signals is 81.75%,and the task completion rate is up to 62.50%.This study enhances the overall performance and practicality of BCW,laying a crucial foundation for its commercialization and everyday use.