EEG Wave Identification in Human Brain with Emotiv EPOC for Motor Imagery

Muhammad N. Fakhruzzaman, Edwin Riksakomara, Hatma Suryotrisongko

Research output: Contribution to journalConference articlepeer-review

40 Citations (Scopus)

Abstract

Brain Computer Interfaces, abbreviated as BCI, is a technology which allows users to take action in computer by focusing on which action the user wants to do. BCI processes brainwave which recorded by means of electroencephalography so it is can be known to computers. Inside the brain, nerve impulses is passed across corresponding nerves to command body part into action, as a result, the respective body part responded with the action that brain commanded. This chain of events can be harnessed by BCI so that brainwaves can be acquired. Using a proper classification method, brainwaves data can be used as a digital command to computer, eliminating the need of actual real-world action to act in a computer. One of the BCI's interesting topic is Motor Imagery, a topic which deeply examine brain activity when imagining motoric activity such as moving left hand. Brain activity is recorded by electroencephalography so that BCI can use the data as a reference to translate brain activity into actual motor activity or as computer trigger. Emotiv EPOC is one of the common BCI distributed to public users. The purpose of this research is to determine how BCI can identify and distinguish human's brainwave when performing different activities. Emotiv EPOC is used in this research with a purpose of whether Emotiv EPOC can be used for Motor Imagery. A simple method is used in this research, using Graz BCI scenario provided in OpenVibe installation bundle and then assess the test results with 5 seconds timeframe. A total of 6 scenarios is used for testing purpose. The success rates of those 6 scenarios are: scenario 1 with 76,67% success rate, scenario 2 with 91,67% success rate, scenario 3 with 28,33% success rate, scenario 4 with 13,33% success rate, scenario 5 with 60% success rate, and scenario 6 with 76,67% success rate. The result from this research indicates that Emotiv EPOC can be a possible option but not recommended for implementing motor imagery application.

Original languageEnglish
Pages (from-to)269-276
Number of pages8
JournalProcedia Computer Science
Volume72
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event3rd Information Systems International Conference, 2015 - Shenzhen, China
Duration: 16 Apr 201518 Apr 2015

Keywords

  • Brain Computer Interfaces
  • Brain Waves
  • Electroencephalography
  • Motor Imagery

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