Classification Of Eyelid Position And Eyeball Movement Using Eeg Signals

Malaysian Journal of Computer Science

F. Ibrahim¹  Mohd Yamani Idna Idris¹  H. Arof¹  R. Ramli¹  A.S.M. Khairuddin¹ 

关键词: EEG signals;    EOG artifacts;    Hybrid BCI;    Eyeball Movement;    Eyelid Position;    Real-time;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: University of Malaya * Faculty of Computer Science and Information Technology

【 摘 要 】

Contamination of EOG activities in EEG signals remains a significant problem in designing the hybrid BCIsystem. Since EEG signals have always been contaminated by EOG artifacts, we employ these artifacts asinputs into our system. Therefore, in this study we utilized theEEG and its EOG artifacts as inputs to the hybridBCI and evaluated the classification performance between thresholding and classifier techniques to determinethe eyelid position and eyeball movement from EEG signals and its EOG artifacts in real-time. The EEG signalsare recorded from the occipital (channel O2) and motor cortex (channel C3 and C4) on the scalp using 10-20montage system. First, alpha band signal at channel O2 is monitored and analyzed to determine the eyelidposition of eye closed and open. If the eyes are open, EOG traces in two delta band signals related to horizontaleyeball movement at channel C3 and C4 are examined to obtain the eyeball movement classification. A slidingwindow frame is utilized to analyze the EOG trace signals so that important cues are positioned at the center ofthe window for effective classification. A few features can be extracted from the EEG data in the window andutilized to determine the eyelid position and eyeball movement by thresholding. The data can also be utilizeddirectly as inputs to MLP or SVM classifiers and their performances are compared with the thresholdingscheme. The highest classification rate of 0.98% is obtained by the SVM classifiers with an average executiontime of just 0.53s. The result of this classification can be utilized in hybrid BCI for various applications.

【 授权许可】

Unknown