A framework for a real time intelligent and interactive Brain Computer Interface. (April 2015)

• Record Type: Journal Article
• Authors: Kumar, Shitij; Sahin, Ferat
• Is Part Of: Computers & electrical engineering. Volume 43(2015)
• Page Start: 193
• Page End: 214
• Publication Date: 2015-04
• Subjects: Electroencephalography (EEG) -- Data collection -- Brain–computer interface (BCI) -- Event-related potentials (ERP) -- Classification; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.03.024
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Highlights: A framework for a real time implementation of a Brain Computer Interface. Implementation & comparison of different feature extraction methods and classifiers. Accuracy & processing time comparison for detection of event related potentials-ERP. An implementation of a prototype system using the proposed BCI framework. Real time EEG data collection and classification of ERPs using Hex-O-Speller. Abstract: This research proposes a framework for a real time implementation of a Brain Computer Interface (BCI). This interface is designed with a future objective of providing a testing platform as an interactive and intelligent Image Search and Retrieval tool that allows users, disabled or otherwise, to browse and search for images using non-invasive electroencephalography (EEG) signals. As a proof of concept, a prototype system was designed and implemented to test real time data collection and navigation through the interface by detection and classification of event-related potentials (ERPs). A comparison of different feature extraction methods and classifiers for the detection of ERPs is performed on a standard data set to determine the best fit for the real time implementation of the BCI. The preliminary results of the real time implementation of the prototype BCI system are quantified by the success rate of the user/subject in navigating through the interface and spelling a search keyword using the mental-typewriter Hex-O-Speller.