Stimulus Paradigm for an Asynchronous Concurrent SSVEP and EOG Based BCI Speller System

• Published in: IEEE access 2021, Vol.9, p.127484-127495
• Main Authors: Duraisamy, Saravanakumar, Reddy M, Ramasubba
• Format: Article
• Language: English
• Subjects: asynchronous speller; brain computer interface (BCI); Electric potential; Electrodes; Electroencephalography; electrooculogram (EOG); Electrooculography; Eye movements; Human-computer interface; Hybrid systems; Information transfer; Object detection; Performance measurement; Steady state visual evoked potential (SSVEP); Task analysis; Visual stimuli; Visualization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3112257

The brain computer interface (BCI) speller system can be classified into synchronous and asynchronous type. In synchronous type, one of the target characters is selected with specific interval periodically even if the user is making or not making attention on the target, whereas, in asynchronous case, the target character will not be selected until a confirmation signal is received from the user. In this proposed study a novel oddball stimulus paradigm is introduced on the hybrid steady state visual evoked potential (SSVEP) and electrooculogram (EOG) speller system to achieve an asynchronous control and high performance. The proposed system consists of forty characters grouped and indexed into five flickering unique frequency visual stimuli. Each visual stimulus is assigned with eight unique characters. The characters in each group are randomly highlighted by the oddball paradigm and the user performs blink eye movement in synchrony with the desired target character highlight. An asynchronous control is achieved by EOG signal and oddball paradigm because the target character will not be selected until the user performs blink eye movement. The stimulus paradigm helps the user to select the target group and desired target character, concurrently by SSVEP and EOG signal which increases the performance of the system. The proposed asynchronous speller system is tested and validated on ten subjects. The online classification accuracy and information transfer rate (ITR) of the proposed hybrid speller system are 99.38% and 116.58 bits/min respectively. Performance metrics of the proposed system are compared with the conventional speller systems and is found to be much superior to existing systems.