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 74
- Downloads 0
Qos-based Computing Resources Partitioning between Virtual Machines in the Cloud Architecture
Cloud services have been used very widely, but configuration of the parameters, including the efficient allocation of resources, is an important objective for the system architect. The article is devoted to solving the p...
A Novel Hybrid Quicksort Algorithm Vectorized using AVX-512 on Intel Skylake
The modern CPU’s design, which is composed of hierarchical memory and SIMD/vectorization capability, governs the potential for algorithms to be transformed into efficient implementations. The release of the AVX-512 chang...
Convolutional Neural Networks in Predicting Missing Text in Arabic
Missing text prediction is one of the major concerns of Natural Language Processing deep learning community’s at-tention. However, the majority of text prediction related research is performed in other languages but not...
Rule Based Approach for Arabic Part of Speech Tagging and Name Entity Recognition
The aim of this study is to build a tool for Part of Speech (POS) tagging and Name Entity Recognition for Arabic Language, the approach used to build this tool is a rule base technique. The POS Tagger contains two phases...
An Ecn Approach to Congestion Control Mechanisms in Mobile Adhoc Networks
Node(s)/link(s) of a network are subjected to overloading; network performance deteriorates substantially due to network congestion. Network congestion can be mitigated with the help of Explicit Congestion notification (...