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Emotiv Epoc/EEG/BCI

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Technical seminar on a brain computer interfacing device known as emotiv epoc and its working principles eeg and bci.

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Emotiv Epoc/EEG/BCI

  1. 1. Technical Seminar on “Emotiv Epoc/EEG/BCI” By Suhail Ahmed Khan 1RN07EC096
  2. 2. Introduction <ul><li>Emotiv Epoc is a brain computer interfacing device </li></ul><ul><li>It is basically a headset that can read and interpret brain waves </li></ul>
  3. 3. <ul><li>Emotiv Epoc is based on two working principles which are- Electroencephalography and Brain-Computer Interfacing </li></ul><ul><li>Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by firing of neurons within the brain. </li></ul><ul><li>A brain-computer interface (BCI), sometimes called a direct neural interface or a brain-machine interface, is a direct communication pathway between a human brain and an external device. </li></ul>
  4. 4. Working Principles <ul><li>The Emotiv system measures the electrical activity associated with the brain and the muscles of the face. What the brain-computer interface essentially does is process the electrical signals linked with the brain activity and convert them into a command or a language that can be understood by the machine. </li></ul>
  5. 5. <ul><li>This Block Diagram gives us the general idea of brain computer interface </li></ul><ul><li>BCI is of three forms- invasive(where the BCI is directly implanted in the brain), Partially invasive(Planted partially inside the skull) and Non invasive ( where the BCI is planted completely on the external of the skull) </li></ul>
  6. 6. <ul><li>Electroencephalography belongs to the Non Invasive BCI category. </li></ul><ul><li>It is a neurological test that involves attaching electrodes to the head of a person to measure and record electrical activity in the brain over time. </li></ul><ul><li>It works using the most common Artificial Neural Networks Learning and Training techniques, namely the Back Propagation algorithm and McCulloch-Pitts model. </li></ul>
  7. 7. Biological Neuron Biological Terminology ANN terminology Neuron Node/unit/cell Synapse Connection/link Synaptic Efficiency Connection Strength/weight Firing frequency Node output
  8. 8. McCulloch-Pitts Model <ul><li>Simplest form of Artificial Neural Model. </li></ul><ul><li>Here different inputs, X 1 to X n are fed to a summer along with some known weights (W 0 to W n ) </li></ul><ul><li>These are given to an activation function, which determines the output in terms of avtivation value or induced local field. </li></ul><ul><li>Some common activation functions are the threshold function and sigmoid functions. </li></ul><ul><li>This is classified under supervised learning </li></ul>
  9. 9. Back Propagation Algorithm <ul><li>It is a supervised learning method, and is a generalization of the delta rule. </li></ul><ul><li>There are two phases in BP: Propagation and weight update using delta rule. </li></ul><ul><li>Error at the output can be calculated directly at the output neurons using d j (n)-y i (n) but this corresponds to hidden neurons. </li></ul><ul><li>Hence the hidden neurons need to be penalized or awarded. </li></ul><ul><li>This is done by back propagating the error backwards. </li></ul>
  10. 10. <ul><li>The  delta rule  is a gradient descent learning rule for updating the weights of the artificial neurons in a single-layer  perceptron. </li></ul><ul><li>For a neuron j with activation function g(x), the delta rule for j’s ith weight w ji is given by, </li></ul>
  11. 11. Hardware Components <ul><li>The main physical components are the 14 electrodes, the gyroscope, sensors and a Lithium-poly battery. </li></ul><ul><li>The device connects wirelessly with the computer or the embedded system. </li></ul>
  12. 12. Specifications Number of Channels 14 (plus CMS/DRL references) Channel names (Int. 10-20 locations) AF3, AF4, F3, F4, F7, F8, FC5, FC6, P7, P8, T7, T8, O1, O2 Sampling method Sequential sampling, Single ADC Sampling rate ~128Hz (2048Hz internal) Resolution 16 bits (14 bits effective) 1 LSB = 1.95μV Bandwidth 0.2 - 45Hz, digital notch filters at 40Hz and 60Hz Dynamic range (input referred) 256mVpp Coupling mode AC coupled Connectivity Proprietary wireless, 2.4GHz band Battery type Li-poly Battery life (typical) 12 hours Impedance measurement Contact quality using patented system
  13. 13. Operation
  14. 14. Training <ul><li>Just like any other ANN device, the EPOC has to be trained under supervised learning to give the desired outputs. </li></ul>
  15. 15. <ul><li>  This sort of training is usually called Cognitive Training, where Data from training to classify their individual brainwave ‘signature’, for each focussed and intent thought trained. </li></ul><ul><li>There are four different mental states or facial gesture and predefined Actions that can be triggered when they occur. </li></ul>
  16. 16. Software <ul><li>ABI is a simple software for the Modular EEG that implements an experimental Brain Computer Interface (BCI). </li></ul><ul><li>Multichannel Human Computer </li></ul><ul><li>Interface allows traditional input of </li></ul><ul><li>conscious control while providing a </li></ul><ul><li>feedback loop based on user </li></ul><ul><li>perception </li></ul>
  17. 17. <ul><li>The programming language used in the API (application program interface) is known as Python C. </li></ul><ul><li>Example code is as shown below: </li></ul>consumer_key = 'x31x00x35x54x38x10x37x42x31x00x35x48x38x00x37x50' research_key = 'x31x00x39x54x38x10x37x42x31x00x39x48x38x00x37x50'   sensorBits = { 'F3': [10, 11, 12, 13, 14, 15, 0, 1, 2, 3, 4, 5, 6, 7], 'FC6': [214, 215, 200, 201, 202, 203, 204, 205, 206, 207, 192, 193, 194, 195], 'P7': [84, 85, 86, 87, 72, 73, 74, 75, 76, 77, 78, 79, 64, 65], 'T8': [160, 161, 162, 163, 164, 165, 166, 167, 152, 153, 154, 155, 156, 157], 'F7': [48, 49, 50, 51, 52, 53, 54, 55, 40, 41, 42, 43, 44, 45], 'F8': [178, 179, 180, 181, 182, 183, 168, 169, 170, 171, 172, 173, 174, 175] } classEmotivPacket (object): def__init__ (self, data): self . counter = ord(data[0]) self . sync = self . counter == 0xe9 self . gyroX = ord(data[29]) - 102 self . gyroY = ord(data[30]) - 104 #assert ord(data[15]) == 0  
  18. 19. Applications <ul><li>The lists of applications for this device are numerous ranging from medical applications to electro-mechanical to entertainment applications. </li></ul><ul><li>The applications can be divided into two types- The ones currently in use and those that are in beta (development) stages. </li></ul>
  19. 20. Present Applications <ul><li>Currently this system is being used in Virtual 3D navigation and in gaming applications. </li></ul><ul><li>Here a person can control a character on the screen just through his or her facial expressions. </li></ul><ul><li>No physical movements of arms is required as in traditional gaming. </li></ul>
  20. 21. Developer Applications <ul><li>Two of the best known developer applications of the Emotiv Epoc are: The brain driver car and the mind controlled wheel chair </li></ul><ul><li>In the brain driver car the can be made to accelerate/decelerate or turn just by bio-electric signals from the 16 channel EPOC </li></ul><ul><li>An electric wheel chair can be made to navigate just by simple actions such as blinking and smiling. </li></ul>
  21. 22. <ul><li>Brain Driver Car </li></ul><ul><li>Mind Controlled Wheel Chair </li></ul>
  22. 23. Advantages/Disadvantages <ul><li>Advantages </li></ul><ul><li>New method of interacting with machines/computer, where no physical touch is required. </li></ul><ul><li>Relatively cheap compared to other EEG devices. </li></ul><ul><li>No conduction gel required for the electrodes. </li></ul><ul><li>Disadvantages </li></ul><ul><li>Current version limits to only four different actions(i.e., inputs) </li></ul><ul><li>Many applications still in beta stages </li></ul><ul><li>Hard to market since not many people are known to the idea of BCI. </li></ul>
  23. 24. Current Status <ul><li>This system is being currently extensively being used in the medical and gaming fields. But it is still in the developer (Beta) stages for most of its Electro-Mechanical Applications. </li></ul><ul><li>There is stiff competition from other BCI products such as OCZ’s neural impulse actuators (NIA), shown in figure. It uses only 3 electrodes. </li></ul>
  24. 25. Conclusion <ul><li>Brain-Computer Interface (BCI) is a method of communication based on voluntary neural activity generated by the brain and independent of its normal output pathways of peripheral nerves and muscles. </li></ul><ul><li>The neural activity used in BCI can be recorded using invasive or non-invasive techniques. </li></ul><ul><li>We can say as detection techniques and experimental designs improve, the BCI will improve as well and would provide wealth alternatives for individuals to interact with their environment. </li></ul><ul><li>Electroencephalography is the process of recording the electrical activity in the brain. </li></ul><ul><li>Emotiv Epoc is a neural headset that works on the principle of Electroencephalography or EEG. </li></ul>
  25. 26. Questions
  26. 27. Video

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