BCI Brain Computer Interface

1. BCIBrain Computer Interface by Omar Nada & Sina Firouzi

2. Introduction • What is it • A communication channel between brain and electronic device • Computer to brain/Brain to computer • Why we need it • Medical purposes • Repairing eyesight, hearing, movement of body parts • Entertainment and multimedia communications • Toys, video games, activity in virtual reality environments, controlling devices with thought, synthetic telepathy • Military • Mood control , commanding and telepresence • How does it work • Algorithms are used to translate brain activity into control signals • Brain can handle signals generated by electronic devices

3. Overview Source: wingsforlife.com

4. How does it work • Brain’s electrical activity produced by firing of electrically charged neurons is observed by sensors • Invasive sensors • Electrodes are implanted directly into gray matter • High quality of signals, risk of scar-tissue • Partially invasive • Electrodes are implanted inside skull but not into gray matter • Lower quality, less risk of scar-tissue • Non-invasive • Signals are observed from outside the skull • Low quality as skull dampens signal, no surgery, no scar-tissue, safest method • Electroencephalography (EEG) by observing the wave of ions released by neurons • Magnetoencephalography (MEG) by observing magnetic fields produced in brain • Functional magnetic resonance imaging (FMRI) • This information is translated using algorithms and used by electronic devices and vice versa

5. Using Invasive Sensors

6. BCI Projects • Assist Arm Robot • Carleton University • BCI + Assist • Berlin Brain-Computer Interface • Health Care

7. 1) Assist ARM Robot • Early phase One degree of freedom Assist Arm • Uses nerves and force sensor as input • Assist in a desired motion ( for recovery) MEG(using electrodes) Biceps & triceps Projected Motion impedance control schema Motion(force sensor) Up & Down directions

8. Assisted Movement force sensor electrodes Initial movement Assisted movement

9. Challenges • Same group muscles can control different joints • Body fat, muscle mass, muscle fatigue affect measurements • Different people give different values ( like PWM) • Lack of volunteers!!!! (especially for invasive methods) • Guessing the user Intensions!

10. Work Arounds / Solutions • Session Calibration • Using min and max values of voltages • Muscle Group Calibration • Run the above technique for all the group muscles used for readings • THEN: Work relatively • Use the session and group muscle boundaries to predict user intention

11. 2) Berlin BCI • The Berlin Brain-Computer Interface: EEG-based communication without subject training Benjamin Blankertz, Guido Dornhege, Matthias Krauledat, Klaus-Robert Müller, Volker Kunzmann, FlorianLosch, Gabriel Curio • Non-invasive • Key features • Use of well-established motor competences as control paradigms • High-dimensional features from 128-channel EEG • Advanced machine learning techniques

12. 2) Berlin BCI • Establishing a BCI system based on motor imagery that works without subject training • ‘Let the machine learn’ • System automatically adapts to the specific brain signals of each user by using advanced techniques of machine learning and signal processing • It is possible to transfer the results obtained with regard to movement intentions in healthy subjects to phantom movements in patients with traumatic amputations. • High information transfer rates can be obtained from single-trial classification of fast-paced motor commands

13. 3) Health Care • Health care example • Repairing damaged hearing • Sounds are received by an external device and signals are sent to brain • Repairing damaged eyesight • A camera sends signals to brain • Helping people with spine injuriesand paralyzed limbs by electrically stimulating muscles • Moving paralyzed body parts with help of robotic parts

14. 3) Health Care • Replacing damaged or lost body parts • Mechanical hands, fingers. • Helping people with severe paralysis to communicate with outside world using a computer. • Restore speech • Patient concentrates on a letter and computer receives and pronounces it

15. Feasible Future • What is in research • Are people able to willingly fire specific neurons in real-time? • Images seen by human eyes have been recorded in black and white. Recording color images is in research • Recording dreams and thoughts • What is coming out soon • Affordable non invasive sensors • Calibration using heart rates ( more accurate results)

16. Omar’s view of the future • BCI • Better control algorithm to decode the brain activities (cheap non invasive) coming to reality • Check out TED video emotivby Tan Le • Application • ‘HandsFree’ Driving • Thinking Pattern Authentication

17. Sina’s view of the future • Virtual reality • Being able to interact with others in a virtual 3D environment without using muscles or mouse • Using electronic devices without touch or any muscle movement • Well functioning moving body parts • Mood control • Sending signals to your brain can improve your mood

18. Conclusion • BCI = Brain + Computer + Communication Channel • BCI Applications • Carleton Assist ARM • Berlin BCI • Health applications • How we view the future from the BCI lens