Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images

Sensors

Jae Won Bang¹  Jong-Suk Choi¹ 

[1] Division of Electronics and Electrical Engineering, Dongguk University-Seoul, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Korea;

关键词: EEG;    BCI;    LDA;    SVM;   

DOI  :  10.3390/s130506272

来源: mdpi

【 摘 要 】

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been used in various applications, including human–computer interfaces, diagnosis of brain diseases, and measurement of cognitive status. However, EEG signals can be contaminated with noise caused by user's head movements. Therefore, we propose a new method that combines an EEG acquisition device and a frontal viewing camera to isolate and exclude the sections of EEG data containing these noises. This method is novel in the following three ways. First, we compare the accuracies of detecting head movements based on the features of EEG signals in the frequency and time domains and on the motion features of images captured by the frontal viewing camera. Second, the features of EEG signals in the frequency domain and the motion features captured by the frontal viewing camera are selected as optimal ones. The dimension reduction of the features and feature selection are performed using linear discriminant analysis. Third, the combined features are used as inputs to support vector machine (SVM), which improves the accuracy in detecting head movements. The experimental results show that the proposed method can detect head movements with an average error rate of approximately 3.22%, which is smaller than that of other methods.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.