Feature Extraction and Classification Methods for a Motor Task Brain Computer Interface: A Comparative Evaluation for Two Databases
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 8
Abstract
A comparative evaluation is performed on two databases using three feature extraction techniques and five classification methods for a motor imagery paradigm based on Mu rhythm. In order to extract the features from electroencephalographic signals, three methods are proposed: independent component analysis, Itakura distance and phase synchronization. The last one consists of: phase locking value, phase lag index and weighted phase lag index. The classification of the extracted features is performed using linear discriminant analysis, quadratic discriminant analysis, Mahalanobis distance based on classifier, the k-nearest neighbors and support vector machine. The aim of this comparison is to evaluate which feature extraction method and which classifier is more appropriate in a motor brain computer interface paradigm. The results suggest that the effectiveness of the feature extraction method depends on the classification method used.
Authors and Affiliations
Oana Diana Eva, Anca Mihaela Lazar
Keywords
Related Articles
- EP ID EP260444
- DOI 10.14569/IJACSA.2017.080834
- Views 73
- Downloads 0
Ethical Issues and Related Considerations Involved with Artificial Intelligence and Autonomous Systems
The applications of artificial intelligence (AI) and automated systems (AS) demonstrate excellent outcomes in various sectors of industrial units to replace the human from jobs. However, the competitive world with evolut...
Diabetes Monitoring System Using Mobile Computing Technologies
Diabetes is a chronic disease that needs to regularly be monitored to keep the blood sugar levels within normal ranges. This monitoring depends on the diabetic treatment plan that is periodically reviewed by the endocrin...
Traffic Sign Detection and Recognition using Features Combination and Random Forests
In this paper, we present a computer vision based system for fast robust Traffic Sign Detection and Recognition (TSDR), consisting of three steps. The first step consists on image enhancement and thresholding using the t...
Mitigating Address Spoofing Attacks in Hybrid SDN
Address spoofing attacks like ARP spoofing and DDoS attacks are mostly launched in a networking environment to degrade the performance. These attacks sometimes break down the network services before the administrator com...
An Analysis on Natural Image Small Patches
The method of computational homology is used to analyze natural image 8 × 8 and 9x9-patches locally. Our experimental results show that there exist subspaces of the spaces of 8x8 and 9x9-patches that are topologically eq...