BRAIN-COMPUTER INTERFACE SYSTEMS REVIEW A HYBRID METHOD FOR INCREASING THE NUMBER OF COMMANDS IN SSV

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e-ISSN: 2582-5208

International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:03/Issue:11/November-2021 Impact Factor- 6.752 www.irjmets.com

BRAIN-COMPUTER INTERFACE SYSTEMS REVIEW A HYBRID METHOD FOR INCREASING THE NUMBER OF COMMANDS IN SSVEPS BCIS Yaqoub E Althuwaini*1, Seyed A Kaboli*2 *1London

South Bank University, School Of Engineering, London, United Kingdom.

*2Cranfield

University, Aerospace Solution Dept., Cranfield, United Kingdom.

ABSTRACT Brain-computer interface (BCI) systems integrated with the steady-state visual evoked potential (SSVEP) provide a broad spectrum of info throughput and need short training than BCI systems utilizing different brain signals. A repetitive visual stimulus (RVS) must be shown to the individual to evoke an SSVEP. Coupling visual templates or additional light triggers designed to produce amplified lighting may be used to make the RVS hold a graphic screen. The SSVEP characteristics are influenced by the RVS attributes (e.g., Frequency, color), dependent on the visualization system. This affects BCI information throughput as well as user protection and convenience. In this paper, the historical evolution of BCIs with particular consideration on SSVEP based BCIs was reviewed, and a hybrid method for increasing the number of commands in SSVEP based BCI system using a combined color and frequency model was proposed for wheelchair control. Keywords: Brain-Computer Interface (BCI), SteadyState Visual Evoked Potential (SSVEP), Color Detection, Frequency Detection, Psychtoolbox.

I.

INTRODUCTION

Brain-Computer Interface (BCI) constructs a direct correspondence channel between the brain and external devices by coding and decoding mental activities. Recently, Electroencephalogram (EEG) based BCIs have slowly moved from the lab to the public's eyesight, and one can find more application scenarios increasingly, for example, diagnosis, rehabilitation, disability support, and fatigue monitoring. Among various applications, steady-state visual evoked potential (SSVEP) based BCI has attracted a lot more and much more interest because of its increased Information Transfer Rate (ITR), reduced user education, and simple operation. Scientists have developed many decoding and encoding algorithms to enhance system performance. To facilitate the analysis of algorithms' overall performance, wide-open datasets for SSVEP based BCIs have emerged in the recent past. These wide-open datasets high effectiveness has benefited the reports in highspeed BCI spellers for scientists. Nevertheless, to enhance the practicality of SSVEP based BCIs, a wearable BCI device is in demand that is great. [1] In contrast to the BCI system, which has a regular bulky, expensive, and wired EEG process, a wearable BCI device is much better in practice due to its advantages, for example, portability, easy preparation, mobility, and cost that is low. Nevertheless, in much more complicated locations, wearable BCI methods' practical applications encounter far more difficulties in data acquisition, information analysis, and user experience. As much as we know, a public dataset with many topics for a wearable SSVEP based BCI is missing. An EEG electrode is a crucial element in using a wearable BCI feature. Wet electrodes and dry electrodes are two kinds of scalp electrodes that are generally utilised to get EEG signals. Wet electrodes have improved signal quality and are far more comfortable to wear. Nevertheless, the wet electrodes' planning before the test requires professional technical assistance and filtering the conductive paste after usage is also time-consuming. Besides, wet electrodes cannot be used for a very long duration recording when the gel will dry over a length of time. The dry electrode does not need conductive paste and then provides a durable and convenient EEG acquisition method. Besides, the dried-up electrode is ideal for making high-density electrode arrays that collect EEG signals with good spatial resolution. [2] Nevertheless, dried-up electrodes' signal quality and user experience are even worse because of the small media of electrodes onto the head. However, many scientific studies have compared the difference between dry and wet electrodes in EEG recording and BCI applications. A detailed comparison of BCI performance and user experience between the two types of electrodes is missing for a wearable SSVEP based BCI. www.irjmets.com

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