Year-end Sale % OFF 
Abstract
Electroencephalography-based brain computer interface systems could provide alternative communication methods for severely disabled people who cannot use their neuromuscular systems. The P300 signal is one of the event related potentials that are used for brain computer interface systems. The most important performance parameter of a P300 based brain computer interface system is information transfer rate that is calculated by using classification accuracy and P300 signal detection time. Moreover, P300 speller has a very critical role for classification accuracy and information transfer rate in a P300 based brain computer interface. Although most of studies are about row column based P300 speller in literature, region based P300 speller proved that has higher classification accuracy than row column based one. There are very few studies about region based P300 speller. This study aims to investigate methods for obtaining higher classification accuracy and information transfer rate with using region based P300 speller that constituted audio and visual stimulus. This is the first research that using audio and visual stimulus for a region based P300 speller in literature. Previous studies about region based P300 spellers focused on spellers with only visual stimulus types. Our new paradigm presents region based P300 spellers with only audio, only visual, and audio-visual stimuli. Audio-visual P300 speller structure is the newest model for region based spellers. The subject focused on the desired character stimulus. We used the stepwise linear discriminant analysis method for classification that either included the desired P300 signal or not. According to stepwise linear discriminant analysis, the mean classification accuracy value of the experiment was 90.31% with the audio-visual region based P300 speller. With this new paradigm, classification accuracy in the audio-visual P300 speller was improved 15.69% and 66,99% according to the visual only and audio only P300 speller that we used in the experiments, respectively.
Highlights
Brain computer interface (BCI) systems allow users to send commands or messages to an electronic system or computer without using their peripheral motor systems [1], [2]
This study investigated whether or not it is possible to use a region based audio-visual P300 speller for subjects with high classification accuracy and information transfer rate (ITR)
Fazel-Rezai and Abhari and Pan et al showed that their approach had higher accuracy rates than the Farwell-Donchin paradigm that had a row column P300 speller [6], [22], [24]
Summary
Brain computer interface (BCI) systems allow users to send commands or messages to an electronic system or computer without using their peripheral motor systems [1], [2]. There are various BCIs in relation to the measurement methods of cerebral activities. Brain activity may be analyzed based on the functional magnetic resonance (FMRI), near infrared spectroscopy (NIRS), magnetoencephalography (MEG) and electroencephalography (EEG) measurement methods [3], [4]. The most commonly used method for BCIs is EEG, which is based on the measurement of electrical activity of the neurons in the. EEGs are preferred in BCI systems due to their high signal responses, which allow non-invasive measurements of real-time data to be processed [5]. EEG devices present portability opportunities for users [7]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
