EEG-Based BCI System to Detect Fingers Movements

Brain Sciences

Kais Belwafi¹  Basel Alebdi¹  Yousef Almassad¹  Homoud Alobaedallah¹  Hatim Aboalsamh¹  Ziyad AlSamhan¹  Sofien Gannouni¹ 

[1] College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia;

关键词: EEG;    brain-computer interface;    prosthetic finger;    decoding finger movement;    multi-class classification;   

DOI  :  10.3390/brainsci10120965

来源: DOAJ

【 摘 要 】

The advancement of assistive technologies toward the restoration of the mobility of paralyzed and/or amputated limbs will go a long way. Herein, we propose a system that adopts the brain-computer interface technology to control prosthetic fingers with the use of brain signals. To predict the movements of each finger, complex electroencephalogram (EEG) signal processing algorithms should be applied to remove the outliers, extract features, and be able to handle separately the five human fingers. The proposed method deals with a multi-class classification problem. Our machine learning strategy to solve this problem is built on an ensemble of one-class classifiers, each of which is dedicated to the prediction of the intention to move a specific finger. Regions of the brain that are sensitive to the movements of the fingers are identified and located. The average accuracy of the proposed EEG signal processing chain reached 81% for five subjects. Unlike the majority of existing prototypes that allow only one single finger to be controlled and only one movement to be performed at a time, the system proposed will enable multiple fingers to perform movements simultaneously. Although the proposed system classifies five tasks, the obtained accuracy is too high compared with a binary classification system. The proposed system contributes to the advancement of a novel prosthetic solution that allows people with severe disabilities to perform daily tasks in an easy manner.

【 授权许可】

Unknown