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Brain Computer Interfaces

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Brain Computer Interfaces Studiengang Medizinische Informatik BACHELOR THESIS Brain Computer Interfaces OpenViBE as a Platform for a P300 Speller Herbert S. Kisakye Matrikelnummer: 172588 E-Mail: [email protected] Heilbronn August 6, 2012 Supervisors: Prof. Dr. Rolf Bendl Dipl.-Inform. Med. C. Maier ACKNOWLEDGEMENTS This thesis is based on research I did in the Bio-Signal Laboratory at the Heilbronn University of Applied Sciences, in the summer of 2012. Without the help of different individuals I would never have been able to achieve much I am greatly indebted to Dipl.-Inform. Christoph Maier , for his readiness to take time out of his busy schedule, working on his Doctorate, to answer questions and give me invaluable guidance. I would like to thank Prof.Dr. Rolf Bendl immensely, for pointing me in this direction of a relatively new yet exciting field. Of course this work would never have come to fruition without the people, mostly fellow students at the university, that sat through many hours of repetitive, sometimes monotonous experiments and those that helped with a few technical aspects. Thank you all so much. Lastly a special thank you to all my friends and family for the emotional support. You kept me going. Herbert Sunday Kisakye III Abstract Aside from hardware, a major component of a Brain Computer Interface is the software that provides the tools for translating raw acquired brain signals into commands to control an application or a device. There’s a range of software, some proprietary, like MATLAB and some free and open source (FOSS), accessible under the GNU General Public License (GNU GPL). OpenViBE is one such freely accessible software. This thesis carries out a functionality and usability test of the platform, looking at its portability, architecture and communication protocols. To investigate the feasibility of reproducing the P300 xDAWN speller BCI presented by OpenViBE, users focused on a character on a 6x6 alphanumeric grid which contained a sequence of random flashes of the rows and columns. Visual stimulus is presented to a user every time the character they are focusing on is highlighted in a row or column. A TMSi analog-to-digital converter was used together with a 32- channel active electrode cap (actiCAP) to record user’s Electroencephalogram (EEG) which was then used in an offline session to train the spatial filter algorithm, and the classifier to identify the P300 evoked potentials, elicited as a user’s reaction to an external stimulus. In an online session, the users tried to spell with the application using the power of their brain signal. Aspects of evoked potentials (EP), both auditory (AEP) and visual (VEP) are further investigated as a validation of results of the P300 speller. IV List of Abbreviations ADC Analog Digital Converter EEG Electroencephalogram ECG/EKG Electrocardiogram EMG Electromyogram ECoG Electrocardiogram CNS Central Nervous System DFT Discrete Fourier Transformation FFT Fast Fourier Transformation LDA Linear Discriminant Analysis EP Evoked Potentials V/A-EP Visual/Auditory- Evoked Potentials SSVEP Steady State Visual Evoked Potentials BCI Brain-Computer Interfaces SNR Signal to Noise Ratio GUI Graphical User Interface VR Virtual Reality fMRI Functional Magnetic Resonance Imaging NIRS Near-Infrared Spectroscopy V Table of Contents ACKNOWLEDGEMENTS .......................................................................................... III ABSTRACT ............................................................................................................... IV LIST OF ABBREVIATIONS ........................................................................................ V 1 INTRODUCTION: HUMAN COMPUTER INTERACTION ................................... 1 1.1 OVERVIEW ......................................................................................................... 1 1.2 AIM AND SCOPE OF THE THESIS ............................................................................ 2 1.3 THE BCI FRAMEWORK ........................................................................................ 2 1.3.1 Principle of the BCI ................................................................................... 2 1.3.2 Integrating Biofeedback ............................................................................ 3 1.3.3 History of the BCI ...................................................................................... 5 1.3.4 Other Methods of Signal Acquisition ......................................................... 5 1.3.5 Classification of BCIs ................................................................................ 6 1.4 PHYSIOLOGY OF THE EEG .................................................................................. 7 1.4.1 Background............................................................................................... 7 1.4.2 Electrical Activity in the Brain .................................................................... 8 1.4.3 Neuron Activity .......................................................................................... 9 1.4.4 Action Potentials ..................................................................................... 10 1.4.5 Characteristic Frequency Bands of the EEG .......................................... 11 1.4.6 Geography of the EEG Waves: Mapping Function to Region ................. 13 1.5 GENERATION AND MEASUREMENT OF EEG ........................................................ 14 1.5.1 Generation in the brain ........................................................................... 14 1.5.2 Electrode positioning .............................................................................. 15 1.5.3 Montage and measurement modes ........................................................ 16 2 THE OPENVIBE PLATFORM ........................................................................... 19 2.1 OVERVIEW ....................................................................................................... 19 2.2 INSTALLATION AND COMPATIBILITY ..................................................................... 20 2.3 USER MODES ................................................................................................... 20 2.4 THE ARCHITECTURE .......................................................................................... 21 2.5 PLUG-INS ......................................................................................................... 22 2.6 TOOLS ............................................................................................................. 23 VI 2.7 EXISTING SCENARIONS AND TUTORIALS ............................................................. 24 3 EEG DATA ACQUISITION AND PREPROCESSING ....................................... 25 3.1 HARDWARE ...................................................................................................... 25 3.2 PREPARATION AND INTERFACING ....................................................................... 27 3.2.1 Dealing with Impedance ......................................................................... 27 3.2.2 Connecting the Server and the Designer ................................................ 28 3.2.3 Channel Selection and Naming .............................................................. 29 3.2.4 Sampling Frequency ............................................................................... 29 3.3 ARTIFACTS IN THE EEG .................................................................................... 30 3.3.1 Definition ................................................................................................. 30 3.3.2 Filtering ................................................................................................... 31 3.3.3 Averaging................................................................................................ 32 3.3.4 Signal to Noise Ratio .............................................................................. 33 3.4 EVOKED POTENTIALS ........................................................................................ 34 3.4.1 Definition and modalities ......................................................................... 34 3.4.2 Noise and EPs ........................................................................................ 35 4 EXPERIMENTS AND RESULTS ....................................................................... 37 4.1 THE P300 SPELLER ......................................................................................... 37 4.1.1 Experiment in brief .................................................................................. 37 4.1.2 Method .................................................................................................... 38 4.1.3 Performance ........................................................................................... 41 4.1.4 Problems and Mitigation ......................................................................... 42 4.1.5 Inference ................................................................................................. 44 4.2 VISUALIZING EVOKED POTENTIALS ..................................................................... 44 4.2.1 Visual Evoked Potentials ........................................................................ 44 4.2.2 Auditory Evoked Potentials ..................................................................... 47 4.3 INVESTIGATING LATENCIES ................................................................................ 48 5 DISCUSSION OF RESULTS ............................................................................
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