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Designing a Hybrid Brain Computer Interface System

Mashayekh Bakhsh, Tara | 2017

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 49995 (55)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Shamsollahi, Mohammad Bagher
  7. Abstract:
  8. Brain Computer Interface (BCI) is a communication system between human brain and a computer or a peripheral device which by recording brain signals directly would send messages and commands from the human brain to computer.According to brain activity patterns of EEG, BCIs are divided into different types. The most important of these patterns called ERP (Event Related Potentials) which appears after particular events in the EEG signal. A significant ERP pattern is P300 potential. It occurs when patient recognizes oddball stimuli. SSVEP (Steady-State Visual Evoked Potential) is another type of patterns and is response of the brain to optical stimulations with certain frequencies and a strong spectral component with the same frequency can be observed in the spectrum of brain signals. Combining various types of BCI systems is called hybrid BCI and increases the efficiency of BCI system.In this project, our concentration is on a hybrid P300-SSVEP Speller BCI system in order to enhance the accuracy. The aim of this project is to design a BCI system based on P300 and SSVEP patterns, which sequentially can be able to rectify the weakness points of the conventional BCIs. Our hybrid BCI system consists of two sections including SSVEP condition and P300 condition. First, we have only six frequencies in a 6×6 speller matrix that we should detect one-group characters with same frequencies through six groups. Second, we must detect desired character that there is in selected group in previous section through 36 characters. We used two different types of software to design our hybrid BCI and we achieved a relatively high classification accuracy with our hybrid system.In order to develop a practical daily use EEG system, signals were captured with a standard low cost EMOTIV-Epoc system
  9. Keywords:
  10. Brain-Computer Interface (BCI) ; Electroencphalogram Signal ; Steady State Visual Evoked Potential (SSVEP) ; P300 Wave ; Hybrid Brain-computer Interface ; EMOTIV-EPOC System

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