Control of a Virtual Hand Using a Brain-Computer Interface System | Finding Suitable Features and Classifiers for an Asynchronous BCI System in the Offline State to Control a Virtual Hand in 6-DOF

Nida Aziz

ISBN 10: 384430441X / ISBN 13: 9783844304411

Published by LAP LAMBERT Academic Publishing, 2011

About this Item

Control of a Virtual Hand Using a Brain-Computer Interface System | Finding Suitable Features and Classifiers for an Asynchronous BCI System in the Offline State to Control a Virtual Hand in 6-DOF | Nida Aziz | Taschenbuch | 104 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844304411 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.

Seller Inventory # 107116587

Bibliographic Details

• Title: Control of a Virtual Hand Using a Brain-Computer Interface System | Finding Suitable Features and Classifiers for an Asynchronous BCI System in the Offline State to Control a Virtual Hand in 6-DOF
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2011
• Language: English
• ISBN 10: 384430441X
• ISBN 13: 9783844304411
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 7D millimeters 220 millimeters width by 150 millimeters height by 7 millimeters depth
• Weight: 173 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

An Asynchronous BCI was designed using non-cue based data collected from several participants in two separate sessions with different protocols. Several different features were extracted and evaluated using the DBI and the best of these, the joint time- frequency (JTF) feature, was chosen for detecting onset and classifying mental tasks. For onset detection, two different methods were employed and compared on the basis of classification performance obtained from confusion matrices, repeatability and ease of application. Different classifiers were tested for onset detection of which Linear Discriminant Analysis (LDA) classifier showed better results. The classified onset using JTF features and LDA classifier was then used to select the active period data in both sessions, which was then sent to the mental task classifier. Mental Task classification was done using Neural Networks, Support Vector Machines and LDA. The performance of the mental task classifier was also evaluated using confusion matrices. A virtual hand was developed graphically in MATLAB and was programmed to move in 6 different directions according to the classified outputs.

About the Author

The author was born in Pakistan. She did her graduate study in Mechatronics Engineering from National University of Science and Technology(NUST), Pakistan and her Postgraduate study in Robotics from UK. Currently she is a Lecturer at NUST.