Seminar on BRAIN-COMPUTER

INTERFACEto use Windows

applications by directly controlling mouse and

keyboardBYDIGIL VINOY4SH1CS032

AGENDAAGENDAABSTRACTINTRODUCTIONEXISTING SYSTEMPROPOSED SYSTEM FUTURE WORKCONCLUSIONREFERENCES

ABSTRACT A Brain-Computer Interface (BCI) allows to control a computer by brain activity only,

without the need for muscle control. Here an EEG-based BCI system based on code-modulated visual evoked potentials (c-

VEPs) that enables the user to work with arbitrary Windows applications. Other BCI systems, like the P300 speller or BCI based browsers, allow control of one

dedicated application designed for use with a BCI. In contrast, the system presented in this paper does not consist of one dedicated

application, but enables the user to control mouse cursor and keyboard input on the level of the operating system, thereby making it possible to use arbitrary applications.

As the c-VEP BCI method was shown to enable very fast communication speeds (writing more than 20 error-free characters per minute), the presented system is the next step in replacing the traditional mouse and keyboard and enabling complete brain-based control of a computer.

INTRODUCTION

HOW DOES BCI WORK?

Human Brain Parts and It’s Working

1.Brain Stem 2.Limbic System

3.Cerebellum3.Cerebellum

4.Cerebrum 4.Cerebrum

4.Cerebrum

FUNCTIONS:-1.BRAIN STEM

• Reptilian brain• Medulla, pons, midbrain• Heart beat, breathing, bladder functions, sense of equilibrium

2.LIMBIC SYSTEM• Emotional brain• Thalamus, hypothalamus, amygdala• Flight or fight situations

3.CEREBELLUM• Little brain, 50% of all neurons• Regulation & control of movements, body posture, balance• Integrates sensory signals to fine tune motor movements

4.CEREBRUM• Higher brain functions, conscious thoughts,action selection,control• 2 hemispheres connected via corpus collosum

6.Temporal Lobe

5.Occipital lobe

8.Frontal Lobe7.Parietal Lobe

THE CEREBRAL CORTEX

FUNCTIONS:-

5.OCCIPITAL LOBE• Vision processing center of the brain• Spatial orientation, color differentiation, motor perception

6.TEMPORAL LOBE• Visual memories, languages, emotional association, • Long term memory• Left temporal lobe – language synthesis

7.PARIETAL LOBE• Merge info from external source and internal senses• Representation of how our body relates to the environment, and how all

the things (objects, people) in the environment spatially relate to us. • Hand eye coordination

8.FRONTAL LOBE• Conscious thoughts, voluntary movements of limbs• Reward, attention, reward, short term memory,planning,motivation

THE NEURON

• 100 billion neurons in the brain• Ion pumps and ion channels helps carry electrical signal.• The synaptic transmission is triggered by the release of neurotransmitters which causes a voltage change across the cell membrane.

BCI

INVASIVE

Neuro surgery

PARTIALLY

INVASIVE

E-CoG

NON INVASIVE

F-MRIEEG MRI

MEG

HOW IS BCI ACHIEVED???

Brain Implant &Electro CorticoGraphy

Brain implant Brain-Gate(E-CoG)

MRI-Magnetic Resonance Imagingf-MRI:-Functional MRI

MRI scan results F-MRI scan results

MEG:-Magneto EncephaloGraphyEEG:-Electro EncephaloGraphy

MEG scanner and result EEG headset and result

ELECTRO-ENCEPHALOGRAPHY

Measures the post synaptic potential from the scalp Non Invasive Size Portable High Temporal Resolution Cheap Doesn’t require the user to lay still

Emotiv EPOC+ NeuroSky MindWave

InteraXon muse OPENEEG

F:-Frontal lobe O:-occipital lobeT:-Temporal lobeP:-Parietal lobeFp:-fronto polar

Odd numbers:-Left HemisphereEven numbers:-Right Hemisphere

C:-Centerz-:-ZeroA:- Reference electrodes

10-20 SYSTEM OF ELECTRODE POSITIONS ON AN EEG HEADSET

PRE-PROCESSING SIGNALS

THE RECORDED EEG SIGNAL MAY CONTAIN MANY ARTIFACTS DUE TO

Muscle activity(ECG,EMG) Eye Movements Eye blinks Headset movements Line noise

CLASSIFICATION OF BRAIN WAVES FROM EEG

EXISTING SYSTEM

Feature extraction from EEG signals Most EEG headsets provide an associated SDK for developers.

These SDKs employ various machine learning algorithms to recognize a pattern from the EEG data. Artificial neural networks are used to classify signals into various categories and make sense out of

them.

EEG BASED BCI IMPLIMENTATION TECHNIQUES

Event related potentials like p300 Motor imagination Oscillations in alpha-beta rhythms Mental state classification approach Slow cortical potentials Sensory evoked potentials

Auditory evoked potentials Visual evoked potentials

Steady-state visual evoked potentials(SSVEP) OR Frequency modulated visual evoked potentials(f-VEP)

Time modulated visual evoked potentials(t-VEP) Pseudo-random code modulated evoked potentials(c-VEP)

VEP

• VEPs are caused by sensory stimulation of a subject’s visual field, and reflect visual information processing mechanisms in the brain.

• In a VEP based BCI, each target is coded by a unique stimulus sequence which in turn evokes a unique VEP pattern.

• A fixation target can thus be identified by analyzing the characteristics of the VEP.• Classified on the modulation of stimulus sequence as

1. Time modulated VEP2. Frequency modulated VEP or SSVEP3. Code modulated VEP

t –VEP & f-VEP

t - VEP f- VEP

c-VEP In a c-VEP BCI, pseudorandom sequences are used. The m-

sequence is the most widely used pseudorandom Sequence. A binary m-sequence is generated using maximal linear

feedback shift registers. An m-sequence has an autocorrelation function which is nearly

orthogonal to its time lag sequence. Thus an m-sequence and its time lag sequence with equivalent neighbors can be used for a c-VEP BCI.

At the beginning of each stimulation cycle, a synchronous signal providing a trigger for target identification should be given to the EEG amplifier.

PROPOSED SYSTEM

c-VEP IN MOUSE MODE

c-VEP IN KEYBOARD MODE

CONCLUSION

The c-VEP BCI system is currently the fastest non-invasive BCI

enabling the user to write > 20 error-free characters per minute.

Here an extended version of the c-VEP BCI software that allows to control mouse cursor and keyboard input on the level of the Windows operating system, thereby enabling control of arbitrary Windows applications.

Thereby, the presented system serves as an important step towards complete brain based control of a computer.

IN THE FUTURE???

FUTURE WORK

Better interface Focus based education Telekinesis Telepathy Brain-to-brain communication Mind expansion

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communication and control,” Clinical neurophysiology, vol. 113, no. 6, pp. 767–791, 2002. [2] L. A. Farwell and E. Donchin, “Talking off the top of your head: toward a mental prosthesis utilizing event-

related brain potentials,” Electroencephalography and clinical Neurophysiology, vol. 70, no. 6, pp. 510–523, 1988. [3] E. E. Sutter, “The visual evoked response as a communication channel,” in Proceedings: IEEE Symposium on

Biosensors, 1984, pp. 85– 100. [4] G. Bin, X. Gao, Y. Wang, B. Hong, and S. Gao, “VEP-based brain-computer interfaces: time, frequency, and code

modulations,” Computational Intelligence Magazine, IEEE, vol. 4, no. 4, pp. 22– 26, 2009. [5] M. Sp¨uler, A. Walter, W. Rosenstiel, and M. Bogdan, “Spatial Filtering Based on Canonical Correlation Analysis

for Classification of Evoked or Event-Related Potentials in EEG Data,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 6, pp. 1097–1103, 2014.

[6]. Ray Kurzweil “How to create a Mind” [7]. Michio Kaku “The future of the mind” [8]. Openeeg,youtube,Wikipedia and other websites