BCIBrain Computer Interface

by Omar Nada & Sina Firouzi

IntroductionWhat is it

A communication channel between brain and electronic device Computer to brain/Brain to computer

Why we need itMedical 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

Overview

Source: wingsforlife.com

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

Using Invasive Sensors

BCI ProjectsAssist Arm Robot

Carleton UniversityBCI + Assist

Berlin Brain-Computer InterfaceHealth Care

1) Assist ARM RobotEarly phase One degree of freedom Assist

ArmUses nerves and force sensor as inputAssist in a desired motion ( for recovery)

MEG(using electrodes)Biceps & triceps

Motion(force sensor)Up & Down directions

impedance control schema

Projected Motion

Assisted Movement

Initial movement Assisted movement

electrodes

force sensor

ChallengesSame group muscles can control different

jointsBody fat, muscle mass, muscle fatigue affect

measurementsDifferent people give different values ( like

PWM)Lack of volunteers!!!! (especially for invasive

methods)Guessing the user Intensions!

Work Arounds / SolutionsSession Calibration

Using min and max values of voltagesMuscle Group Calibration

Run the above technique for all the group muscles used for readings

THEN: Work relativelyUse the session and group muscle boundaries

to predict user intention

2) Berlin BCIThe Berlin Brain-Computer Interface: EEG-based

communication without subject training Benjamin Blankertz, Guido Dornhege, Matthias Krauledat, Klaus-Robert Müller, Volker

Kunzmann, Florian Losch, Gabriel Curio

Non-invasiveKey features

Use of well-established motor competences as control paradigms

High-dimensional features from 128-channel EEGAdvanced machine learning techniques

2) Berlin BCIEstablishing 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

3) Health CareHealth care example

Repairing damaged hearing Sounds are received by an external device and

signals are sent to brainRepairing damaged eyesight

A camera sends signals to brainHelping people with spine injuries and

paralyzed limbs by electrically stimulating muscles

Moving paralyzed body parts with help of robotic partsBrain Compute

rMoving

part

3) Health CareReplacing 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

Feasible FutureWhat 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 soonAffordable non invasive sensorsCalibration using heart rates ( more accurate

results)

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 emotiv by Tan Le

Application ‘HandsFree’ DrivingThinking Pattern Authentication

Sina’s view of the futureVirtual 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

Conclusion

BCI = Brain + Computer + Communication Channel

BCI Applications

Carleton Assist ARMBerlin BCIHealth applications

How we view the future from the BCI lens