BRAIN COMPUTER INTERFACE IMPLEMENTATION USING CLUSTERING TECHNIQUES
Journal: International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol.6, No. 11)Publication Date: 2017-11-30
Authors : Shah Chintan V.A.Shah P.M.Pithadiya;
Page : 245-250
Keywords : Brain Computer Interface; k-means clustering; hierarchical clustering; Principal Component Analysis.;
Abstract
Brain Computer Interface (BCI) generally utilizes non-invasive EEG signals in order to detect intended movement. Normally all EEG electrode receive brain activity on scalp surface which is superimposition of different brain activity. As the number of channel is high we need to reduce it in order to properly detect movement related activity. In this work we have utilized different clustering algorithms to reduce insignificant channels. We have extracted features using statistical methods Principal Component Analysis (PCA). And the classification is done using unsupervised learning algorithm in order to improve generalization capability. The proposed approach reduces false positive rate and it also shows different person have different activity region
Other Latest Articles
- INTERNET OF THINGS (IOT) BASED HOME AUTOMATION SYSTEM
- RECENT THREATS TO CLOUD COMPUTING DATA AND ITS PREVENTION MEASURES
- AN EFFECTIVE AND SECURED TECHNIQUE FOR SIDE-CHANNEL ATTACKS IN CLOUD
- COMPARATIVE ANALYSIS OF A G+4 STRUCTURE CONSIDERING SLAB STIFFNESS ON STRUCTURAL BEHAVIOUR OF L SHAPEDBUILDING PLAN
- Germectomy of Third Molars in Orthodontic Therapy: Usefulness of Volumetric Analysis with TC Cone Beam
Last modified: 2017-11-20 19:55:19