Technical Seminar on“Emotiv Epoc/EEG/BCI”

By Suhail Ahmed Khan1RN07EC096

Introduction

• Emotiv Epoc is a brain computer interfacing device

• It is basically a headset that can read and interpret brain waves

• Emotiv Epoc is based on two working principles which are- Electroencephalography and Brain-Computer Interfacing

• Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by firing of neurons within the brain.

• 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.

Working Principles• 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.

•This Block Diagram gives us the general idea of brain computer interface•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)

• Electroencephalography belongs to the Non Invasive BCI category.

• 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.

• It works using the most common Artificial Neural Networks Learning and Training techniques, namely the Back Propagation algorithm and McCulloch-Pitts model.

Biological NeuronBiological Terminology

ANN terminology

Neuron Node/unit/cell

Synapse Connection/link

Synaptic Efficiency Connection Strength/weight

Firing frequency Node output

McCulloch-Pitts Model•Simplest form of Artificial Neural Model.•Here different inputs, X1 to Xn are fed to a summer along with some known weights (W0 to Wn)•These are given to an activation function, which determines the output in terms of avtivation value or induced local field.•Some common activation functions are the threshold function and sigmoid functions.•This is classified under supervised learning

Back Propagation Algorithm• It is a supervised learning method, and is a

generalization of the delta rule.• There are two phases in BP: Propagation

and weight update using delta rule.• Error at the output can be calculated

directly at the output neurons using d j(n)-yi(n) but this corresponds to hidden neurons.

• Hence the hidden neurons need to be penalized or awarded.

• This is done by back propagating the error backwards.

• The delta rule is a gradient descent learning rule for updating the weights of the artificial neurons in a single-layer perceptron.

• For a neuron j with activation function g(x), the delta rule for j’s ith weight wji is given by,

Hardware Components

• The main physical components are the 14 electrodes, the gyroscope, sensors and a Lithium-poly battery.

• The device connects wirelessly with the computer or the embedded system.

SpecificationsNumber 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

Operation

Training • Just like

any other ANN device, the EPOC has to be trained under supervised learning to give the desired outputs.

• 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.

• There are four different mental states or facial gesture and predefined Actions that can be triggered when they occur.

Software• ABI is a simple software

for the Modular EEG that implements an experimental Brain Computer Interface (BCI).

• Multichannel Human ComputerInterface allows traditional input ofconscious control while providing afeedback loop based on userperception

• The programming language used in the API (application program interface) is known as Python C.

• Example code is as shown below:

consumer_key='\x31\x00\x35\x54\x38\x10\x37\x42\x31\x00\x35\x48\x38\x00\x37\x50'research_key='\x31\x00\x39\x54\x38\x10\x37\x42\x31\x00\x39\x48\x38\x00\x37\x50' 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==0xe9self.gyroX=ord(data[29]) -102self.gyroY=ord(data[30]) -104#assert ord(data[15]) == 0

Applications

• The lists of applications for this device are numerous ranging from medical applications to electro-mechanical to entertainment applications.

• The applications can be divided into two types- The ones currently in use and those that are in beta (development) stages.

Present Applications

• Currently this system is being used in Virtual 3D navigation and in gaming applications.

• Here a person can control a character on the screen just through his or her facial expressions.

• No physical movements of arms is required as in traditional gaming.

• Currently this system is being used in Virtual 3D navigation and in gaming applications.

• Here a person can control a character on the screen just through his or her facial expressions.

• No physical movements of arms is required as in traditional gaming.

Developer Applications

• Two of the best known developer applications of the Emotiv Epoc are: The brain driver car and the mind controlled wheel chair

• In the brain driver car the can be made to accelerate/decelerate or turn just by bio-electric signals from the 16 channel EPOC

• An electric wheel chair can be made to navigate just by simple actions such as blinking and smiling.

Brain Driver Car Mind Controlled Wheel Chair

Advantages/Disadvantages

Advantages• New method of interacting

with machines/computer, where no physical touch is required.

• Relatively cheap compared to other EEG devices.

• No conduction gel required for the electrodes.

Disadvantages• Current version limits to

only four different actions(i.e., inputs)

• Many applications still in beta stages

• Hard to market since not many people are known to the idea of BCI.

Current Status• 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.

• There is stiff competition from other BCI products such as OCZ’s neural impulse actuators (NIA), shown in figure. It uses only 3 electrodes.

Conclusion• 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.

• The neural activity used in BCI can be recorded using invasive or non-invasive techniques.

• 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.

• Electroencephalography is the process of recording the electrical activity in the brain.

• Emotiv Epoc is a neural headset that works on the principle of Electroencephalography or EEG.

Questions

Video