| Pub. Date | : Jul, 2019 |
|---|---|
| Product Name | : The IUP Journal of Computer Sciences |
| Product Type | : Article |
| Product Code | : IJCS11907 |
| Author Name | : Sorush Niknamian |
| Availability | : YES |
| Subject/Domain | : Management |
| Download Format | : PDF Format |
| No. of Pages | : 27 |
Brain Computer Interface (BCI) is a communication system between human brain and a computer or a peripheral device which by recording brain signals directly sends messages and commands from the human brain to computer. According to brain activity patterns of Electroencephalography (EEG), BCIs are divided into different types. The most important of these patterns is called ERP (Event-Related Potentials) which appears after particular events in the EEG signal. Combining various types of BCI systems is called hybrid BCI, which increases the efficiency of BCI system. The paper concentrates on a hybrid P300-Steady State Visual Evoked Potential (SSVEP) speller BCI system in order to enhance the accuracy. The paper aims at designing a BCI system based on P300 and SSVEP patterns, which sequentially can rectify the weakness points of the conventional BCIs. Our hybrid BCI system consists of two sections: SSVEP condition and P300 condition. We used two different types of software to design our hybrid BCI and achieved a relatively high classification accuracy with the hybrid system. In order to develop a practical daily use EEG system, signals were captured with a standard low-cost EMOTIV-Epoc system. We designed a hybrid SSVEP-P300 BCI platform by two different software. The hybrid BCI system consists of two sections, including SSVEP condition and P300 condition. From a set of six frequencies in a 6×6 speller matrix we had, we could detect one-group characters with the same frequencies through six groups. Secondly, we could detect desired character that is there in the selected group of 36 characters. This hybrid system can help SVEP condition to choose more commands using P300 condition so that it can help to increase the number of commands from 6 to 36 commands. In our system, we can achieve only six choices by SSVEP condition.
A Brain Computer Interface (BCI) system can communicate without movement based on brain signals measured with Electroencephalography (EEG). BCIs usually rely on one of the three types of control signals: P300 components of the Event-Related Potential (ERP), Steady State Visual Evoked Potential (SSVEP) and Event-Related Desynchronization (ERD). Research about BCI has widely developed over the past few decades. BCI systems are used in various areas. However, different BCIs have their own advantages and disadvantages. In order to improve the performance of BCIs, Pfurtscheller et al. (2010) proposed the hybrid BCI system, increasing the advantages and reducing the disadvantages from different BCIs.
Brain Computer Interface (BCI), Steady State Visually Evoked Potential (SSVEP), P300 potential, Hybrid BCI, Electroencephalogram (EEG), EMOTIV-Epoc