Towards enhanced information transfer rate: a comparative study based on classification techniques. Issue 4 (3rd July 2020)

• Record Type: Journal Article
• Authors: Katyal, Akshay; Singla, Rajesh
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 8:Issue 4(2020)
• Page Start: 446
• Page End: 457
• Publication Date: 2020-07-03
• Subjects: BCI -- P300 -- SSVEP -- hybrid BCI -- ITR -- SVM; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; 616.0757
• DOI: 10.1080/21681163.2020.1727775
• Languages: English
• ISSNs: 2168-1163

Abstract

ABSTRACT: One of the most important performance parameters for Assistive Devices (AD's) based on Brain Computer Interfaces (BCIs) is the Information Transfer Rate (ITR). This study compares a hybrid BCI to a Steady State Visually Evoked Potential (SSVEP) based BCI, along with a comparison of classification techniques used for translation of user intentions. The hybrid BCI paradigm combined SSVEP & P300, where SSVEP decodes user intentions and P300 is used for Time Division Multiplexing (TDM). The classification protocols were categorised as single-step supervised classifiers and two-step unsupervised classifier. It was observed that the classification accuracy for translation of human intentions for the traditional SSVEP paradigm (93.78%) was higher than the hybrid BCI (90.76%) proposed, but still the hybrid BCI is paradigm option for development of ADs (high ITR of 81.10 bits/minute). The study compared the two classification protocols using the statistical t-value test, which concluded that (99.9% confidence level) the mean classification accuracy and mean ITR were greater for the single-step supervised classification and also that the mean FAR was lower for the single-step supervised classification. The proposed hybrid BCI with single-step supervised learning classification protocol emerged as best BCI option for the development of AD's.