Brain Machine Interfacenew

3,197
-1

Published on

Published in: Real Estate
1 Comment
1 Like
Statistics
Notes
No Downloads
Views
Total Views
3,197
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
363
Comments
1
Likes
1
Embeds 0
No embeds

No notes for slide

Brain Machine Interfacenew

  1. 1. BRAIN - MACHINE INTERFACE <br />PRESENTED BY:-<br />TUHIN DAS<br />
  2. 2. What is Brain-Machine Interface?<br />It is a collaboration in which a brain accepts and controls a mechanical device as a natural part of its representation of the body<br />By reading signals from an array of neurons and using computer chips and programs to translate the signals into action, scientists hope it will be possible for a person suffering from paralysis to control a motorized wheelchair or a prosthetic limb by just thinking about it<br />
  3. 3. Concept of brain gate<br />The BrainGate system is a neuromotor prosthetic device consisting of an array of one hundred silicon microelectrodes, each of which is 1mm long and thinner than a human hair. The electrodes are arranged less than half a millimetre apart on the array, which is attached to a 13cm-long cable ribbon cable connecting it to a computer<br />
  4. 4. Biological inspiration<br />Dendrites<br />Soma (cell body)<br />Axon<br />
  5. 5. Biological inspiration<br />dendrites<br />axon<br />synapses<br />The information transmission happens at the synapses. <br />
  6. 6. Artificial neurons<br />Neurons work by processing information. They receive and provide information in form of spikes.<br />x1<br />x2<br />x3<br />…<br />xn-1<br />xn<br />w1<br />Output<br />w2<br />Inputs<br />y<br />w3<br />.<br />.<br />.<br />wn-1<br />wn<br />The McCullogh-Pitts model<br />
  7. 7. Artificial neurons<br />Nonlinear generalization of the McCullogh-Pitts neuron:<br />y is the neuron’s output, x is the vector of inputs, and w is the vector of synaptic weights.<br />Examples:<br />sigmoidal neuron<br />Gaussian neuron<br />
  8. 8. Neural network mathematics<br />Neural network: input / output transformation<br />W is the matrix of all weight vectors.<br />
  9. 9. Cortical neural signal extraction: non-invasive vs. invasive recording<br />EEG <br />Rhythms β and μ, P300, Slow cortical potential (SCP)<br />Sampling rate 200-1000Hz, <br /># of channels, from 1 or 2 to 128 or 256 <br />Electrodes<br />Bioactive, allowing growth of nerve, or bio-inactive multiple mircowires or multichannel electrode arrays<br />Superficial motor areas or deep brain structures<br />Primary motor, parietal, premotor, frontoparietal, basal ganglia<br />
  10. 10. HOMONCULUS: The littleguy<br />
  11. 11. Cortical neural signal extraction: ECoG<br />resting<br />imagining saying the word ‘move’<br />(d) Imagery is associated with decrease in µ (8–12 Hz) and β (18–26 Hz) bands.<br />A brain–computer interface using electrocorticographic signals in humans*Leuthardt et al 2004 J. Neural Eng. 1 63-71 <br />
  12. 12. CONTROLLING OF MACHINE<br /> :- BY HUMAN BRAIN<br />
  13. 13. The Brain-Controlled Vehicle<br />Signal Processing Algorithms/Command Extraction<br />Neural Interface<br />Neural Signals<br />Directional control<br />Control Command<br />Vehicle State Signal<br />Environmental Feedback<br />Vehicle<br />Sensors<br />
  14. 14. Application<br />It can help to restore any loss of senses <br />1.sense of vision<br />2.sense of hearing<br />3.sense of movement<br />creation of human robots<br />Creation of detective rats<br />
  15. 15. Biggest reward<br />On 7th november 2009 toyota launched it’s wheel chair that works on thought<br />It has a reaction time of 123 milliseconds<br />
  16. 16. THANK YOU…..<br />

×