SEMINAR BRAIN MACHINE INTERFACEPRESENTED BY GUIDED BYSRUTHI.S.KUMAR MAHESWARI R ROLL NO:53 GUEST LUCTURER
INTRODUCTION Brain Machine interface is a new communication link between a functioning human brain and the outside world. Electronic interfaces with the brain that can send and receive signals from the brain. Signals from the brain are taken to brain via implants and transforms the mental decision to control signals
THE HUMAN BRAIN relevant part - cerebral cortex. Cerebral cortex is responsible for many higher order functions like problem solving, language comprehension and processing of complex visual information.
MAIN PRINCIPLEBioelectrical activity ofnerves and muscles .When the neuron fires, thereis a voltage change across thecell which is monitored andanalyzed.A neuron depolarizes togenerate an impulse; thisaction causes changes in theelectric field around theneuron.1 for impulse generated and 0for no impulse
BMI APPROACHES1. Pattern recognition approach based on mental tasks.2. Operant conditioning approach based on the self-regulation of the EEG response.
1.INVASIVE directly implanted into the grey matter. produce the highest quality signals prone to building up of scar- tissue
2.PARTIALLY INVASIVE Implanted inside the skull but rest outside. produce better resolution signals than non-invasive BCIs Electrocorticography (ECoG) uses the same technology, the electrodes are embedded in a thin plastic pad that is placed above the cortex.
3.NON INVASIVE Electroencephalography-recording is obtained by placing electrodes on the scalp with a conductive gel or paste. FMRI(Functional Magnetic Resonance Imaging) exploits the changes in the magnetic properties of hemoglobin as it carries oxygen. Activation of a part of the brain increases oxygen levels there increasing the ratio of oxyhemoglobin to deoxyhemoglobin.
MAIN PARTSIMPLANT DEVICESSIGNAL PROCESSING SECTIONEXTERNAL DEVICEFEEDBACK SECTION
1.IMPLANT DEVICESImplanted array ofmicroelectrodes intothe frontal and parietallobes.provide the electricalcontact between theskin which transformsthe ionic current onthe skin to theelectrical current inthe wires.
2.SIGNAL PROCESSING MULTICHANNEL ACQUISITION SYSTEMS At this section amplification, initial filtering of EEG signal and possible artifact removal takes place. SPIKE DETECTION Spike detection will allow the BMI to transmit only the action potential waveforms. SIGNAL ANALYSIS In this stage, certain features are extracted from the preprocessed and digitized EEG signal which are input to the classifier. Classifier recognize different mental tasks.
3.EXTERNAL DEVICES The classifier’s output is the input for the device control. The device control simply transforms the classification to a particular action. Examples are robotic arm, thought controlled wheel chair etc
4.FEEDBACK Feedback is needed for learning and for control. In the BMIs based on the operant conditioning approach, feedback training is essential for the user to acquire the control of his or her EEG response. The BMIs based on the pattern recognition approach and using mental tasks do not definitely require feedback training.
PROS AND CONS Can help people The signals are with inabilities to weak and are prone control wheel chairs to interference . or other devices Surgery to brain with brain activity. might be risky and To develop better cause brain death. sensing system. There are chemical BCIs are linguistic reactions involved in independent and can brain which BCI be used any where devices cannot pick across the world. up.
APPLICATION1. Auditory and visual prosthesis 2.Functional-neuromuscular stimulation (FNS) 3.Prosthetic limb control
CONCLUSION A potential therapeutic tool. Brain-Computer Interface (BCI) is a method of communication based on voluntary neural activity generated by the brain. have the ability to give people back their lost capabilities.
REFERENCES P. Sajda, K-R. Mueller, and K.V. Shenoy, eds., special issue, “Brain Computer Interfaces,” IEEE Signal Processing Magazine,Jan. 2008 Wolpaw, J.R. et al. (2002) Brain–computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 Birbaumer, N. (2006) Brain–computer-interface research: coming of age. Clin. Neurophysiol. 117, 479–483 www.betterhumans.com www.howstuffworks.com