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.
Disclosure statement
No potential conflict of interest was reported by the author.
Additional information
Notes on contributors
Akshay Katyal
Akshay Katyal was born in 1985 in Ludhiana, Punjab, India. He has completed his BTech and MTech qualifications from Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Punjab, India in the years 2007 and 2009 respectively. He is currently pursuing his Ph.D degree at Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Punjab, India. His areas of interest are soft computing, Brain computer Interfacing & Biomedical Engineering.
Rajesh Singla
Rajesh Singla, an Indian Resident was born in 1975 in the state of Punjab. He Completed his B.E Degree from Thapar University in 1997, completed his MTech degree from IIT -Roorkee in 2006 & completed his doctorate degree from National Institute of Technology Jalandhar, Punjab, India in 2015. His area of interests are Brain Computer Interface, Rehabilitation Engineering, and Process Control. He is working as an Associate Professor in National Institute of Technology Jalandhar, India since 1998.