This document summarizes a seminar presentation on brain fingerprinting through digital electroencephalography signal technique. It discusses how brain fingerprinting works by measuring the brain's electrical activity through EEG to detect the P300 brain wave, which indicates when the brain recognizes familiar information. The document outlines the equipment used, including EEG sensors and a computer system to present stimuli. It also describes the basic process of how a suspect is tested by measuring their brain waves in response to crime-relevant and irrelevant probes to determine if they have knowledge of the crime. Finally, it discusses applications in national security, advertising and criminal investigations, as well as advantages and limitations of the technique.
This slide is about the basic theories of Neurotechnology.
It shows
1. An overview of this area
- Market value, etc
2. Basic knowledge
- Types of neurotechnologies
- Basics of neuroscience
- software engineering.
3. Use cases with neurotechnologies.
This webinar is part of a 2-hour monthly series hosted by the Neurotechnology Innovation Network: https://ktn-uk.org/health/neurotechnology/
Each webinar features expert speakers and focusses on a new development in a different technology area.
The third topic in this series is Dementia treatment using a biodesign approach. Dementia can have enormous effects, not only to those suffering but also family members and others
caring for them, but there are currently no effective therapies available. Neurotechnology offers a new way of treating dementia.
There is growing evidence that technologies such as deep brain stimulation and transcranial magnetic stimulation could help treat some of the effects of dementia and brain-computer interfaces are now able to detect the first signs of dementia years before symptoms appear.
In collaboration with UK Dementia Research Institute this webinar explores novel neurotechnologies to treat dementia, discuss barriers to adoption and new opportunities in the field.
This slide is about the basic theories of Neurotechnology.
It shows
1. An overview of this area
- Market value, etc
2. Basic knowledge
- Types of neurotechnologies
- Basics of neuroscience
- software engineering.
3. Use cases with neurotechnologies.
This webinar is part of a 2-hour monthly series hosted by the Neurotechnology Innovation Network: https://ktn-uk.org/health/neurotechnology/
Each webinar features expert speakers and focusses on a new development in a different technology area.
The third topic in this series is Dementia treatment using a biodesign approach. Dementia can have enormous effects, not only to those suffering but also family members and others
caring for them, but there are currently no effective therapies available. Neurotechnology offers a new way of treating dementia.
There is growing evidence that technologies such as deep brain stimulation and transcranial magnetic stimulation could help treat some of the effects of dementia and brain-computer interfaces are now able to detect the first signs of dementia years before symptoms appear.
In collaboration with UK Dementia Research Institute this webinar explores novel neurotechnologies to treat dementia, discuss barriers to adoption and new opportunities in the field.
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
Teaching Techniques: Neurotechnologies the way of the future (Stotler, 2019)Jacob Stotler
Presenting alternative to drugs from nuerotechnologies and teaching about clinical use of neurothreapy and therapeutic effectiveness of biological aspects of the use of clinical technologies.
Using Brain Waves as New Biometric Feature for Authenticating a Computer User...CSCJournals
In this paper we propose an Electroencephalogram based Brain Computer Interface as a new modality for Person Authentication and develop a screen lock application that will lock and unlock the computer screen at the users will. The brain waves of the person, recorded in real time are used as password to unlock the screen. Data fusion from 14 sensors of the Emotiv headset is done to enhance the signal features. The power spectral density of the intermingle signals is computed. The channel spectral power in the frequency band of alpha, beta and gamma is used in the classification task. A two stage checking is done to authenticate the user. A proximity value of 0.78 and above is considered a good match. The percentage of accuracy in classification is found to be good. The essence of this work is that the authentication is done in real time based on the meditation task and no external stimulus is used.
Amyotrophic Lateral Sclerosis (ALS) is the most common progressive neurodegenerative disorder reflecting
the degeneration of upper and lower motor neurons. Motor neurons controls the communication between nervous
system and muscles of the body. ALS results in the loss of voluntary control over muscular activities along with the
inability to breathe and the maximum life expectancy of affected individual will be 3-5 years from the onset of
symptoms. But the lifetime of affected people can be extended by early detection of disease. The usual methods for
diagnosis are Electromyography (EMG), Nerve Conduction Study (NCS), Magnetic Resonance Imaging (MRI) and
Magneto-encephalography (MEG). But some of these methods may erroneously result in neuropathy or myopathy
instead of ALS and some do not provide any biomarker. EEG is comparatively least expensive method and it
provides biomarker for ALS detection. ALS is always associated with fronto-temporal dementia (FTD). The spectral
analysis of EEG will reveal the structural and functional connectivity alterations of the underlying neural network
that occurs due to FTD and it can generate potential biomarkers for the early detection of ALS. A novel algorithm
has been developed by exploiting the Dual Tree Complex Wavelet Transform (DTCWT) technique and it can
overcome the short comes of existing methods for the analysis and feature extraction of EEG. Deterministic
biomarkers were obtained from spectral analysis of EEG and the proposed algorithm provided 100% accuracy for all
the test datasets.
Enhancing extreme learning machine: Novel extentions and applications to opti...Apdullah YAYIK, Ph.D.
As a single-hidden layer feed forward neural network (SLFN), conventional extreme learning machine (ELM) reaches high performance rates in extremely rapid training pace on benchmark datasets. However, when it is applied to real life large datasets, decline in training pace and performance rates related to low convergence of singular value decomposition (SVD) method occurs. This thesis proposes new approaches in conventional ELM to overcome this problem with lower upper (LU) triangularization, Hessenberg decomposition, Schur decomposition, modified Gram Schmidt (MGS) process and Householder reflection methods. Experiments with conventional and proposed ELMs, have been conducted on visual stimuli optimization problem in brain computer interface (BCI). And, multi-layer perceptron (MLP), k-nearest neighbour (k-NN) and Bayesian network (BayesNET) are applied for compartments. 19 subjects participated in this experiment and results show that if priority is given to training pace, Hessenberg decomposition method, and if priority is given to performance measures Householder reflection method can replace SVD. Also, other proposed methods give comparable results. Besides, this thesis shows that visual stimuli that is smaller and has orange coloured concentric background has statistically positive effect on performing BCI application. In real-time BCI application proposed algorithms can decide just in 17 seconds with selected electroencephalography (EEG) channels and it has an accuracy rate of 90.83%.
System Architecture for Brain-Computer Interface based on Machine Learning an...ShahanawajAhamad1
Brain functions are required to be read for curing
neurological illness. Brain-Computer Interface (BCI) connects
the brain to the digital world for brain signals receiving,
recording, processing, and comprehending. With a BrainComputer Interface (BCI), the information from the user’s brain
is fed into actuation devices, which then carry out the actions
programmed into them. The Internet of Things (IoT) has made it
possible to connect a wide range of everyday devices.
Asynchronous BCIs can benefit from an improved system
architecture proposed in this paper. Individuals with severe
motor impairments will particularly get benefit from this feature.
Control commands were translated using a rule-based
translation algorithm in traditional BCI systems, which relied
only on EEG recordings of brain signals. Examining BCI
technology’s various and cross-disciplinary applications, this
argument produces speculative conclusions about how BCI
instruments combined with machine learning algorithms could
affect the forthcoming procedures and practices. Compressive
sensing and neural networks are used to compress and
reconstruct ECoG data presented in this article. The neural
networks are used to combine the classifier outputs adaptively
based on the feedback. A stochastic gradient descent solver is
employed to generate a multi-layer perceptron regressor. An
example network is shown to take a 50% compression ratio and
89% reconstruction accuracy after training with real-world,
medium-sized datasets as shown in this paper
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
Teaching Techniques: Neurotechnologies the way of the future (Stotler, 2019)Jacob Stotler
Presenting alternative to drugs from nuerotechnologies and teaching about clinical use of neurothreapy and therapeutic effectiveness of biological aspects of the use of clinical technologies.
Using Brain Waves as New Biometric Feature for Authenticating a Computer User...CSCJournals
In this paper we propose an Electroencephalogram based Brain Computer Interface as a new modality for Person Authentication and develop a screen lock application that will lock and unlock the computer screen at the users will. The brain waves of the person, recorded in real time are used as password to unlock the screen. Data fusion from 14 sensors of the Emotiv headset is done to enhance the signal features. The power spectral density of the intermingle signals is computed. The channel spectral power in the frequency band of alpha, beta and gamma is used in the classification task. A two stage checking is done to authenticate the user. A proximity value of 0.78 and above is considered a good match. The percentage of accuracy in classification is found to be good. The essence of this work is that the authentication is done in real time based on the meditation task and no external stimulus is used.
Amyotrophic Lateral Sclerosis (ALS) is the most common progressive neurodegenerative disorder reflecting
the degeneration of upper and lower motor neurons. Motor neurons controls the communication between nervous
system and muscles of the body. ALS results in the loss of voluntary control over muscular activities along with the
inability to breathe and the maximum life expectancy of affected individual will be 3-5 years from the onset of
symptoms. But the lifetime of affected people can be extended by early detection of disease. The usual methods for
diagnosis are Electromyography (EMG), Nerve Conduction Study (NCS), Magnetic Resonance Imaging (MRI) and
Magneto-encephalography (MEG). But some of these methods may erroneously result in neuropathy or myopathy
instead of ALS and some do not provide any biomarker. EEG is comparatively least expensive method and it
provides biomarker for ALS detection. ALS is always associated with fronto-temporal dementia (FTD). The spectral
analysis of EEG will reveal the structural and functional connectivity alterations of the underlying neural network
that occurs due to FTD and it can generate potential biomarkers for the early detection of ALS. A novel algorithm
has been developed by exploiting the Dual Tree Complex Wavelet Transform (DTCWT) technique and it can
overcome the short comes of existing methods for the analysis and feature extraction of EEG. Deterministic
biomarkers were obtained from spectral analysis of EEG and the proposed algorithm provided 100% accuracy for all
the test datasets.
Enhancing extreme learning machine: Novel extentions and applications to opti...Apdullah YAYIK, Ph.D.
As a single-hidden layer feed forward neural network (SLFN), conventional extreme learning machine (ELM) reaches high performance rates in extremely rapid training pace on benchmark datasets. However, when it is applied to real life large datasets, decline in training pace and performance rates related to low convergence of singular value decomposition (SVD) method occurs. This thesis proposes new approaches in conventional ELM to overcome this problem with lower upper (LU) triangularization, Hessenberg decomposition, Schur decomposition, modified Gram Schmidt (MGS) process and Householder reflection methods. Experiments with conventional and proposed ELMs, have been conducted on visual stimuli optimization problem in brain computer interface (BCI). And, multi-layer perceptron (MLP), k-nearest neighbour (k-NN) and Bayesian network (BayesNET) are applied for compartments. 19 subjects participated in this experiment and results show that if priority is given to training pace, Hessenberg decomposition method, and if priority is given to performance measures Householder reflection method can replace SVD. Also, other proposed methods give comparable results. Besides, this thesis shows that visual stimuli that is smaller and has orange coloured concentric background has statistically positive effect on performing BCI application. In real-time BCI application proposed algorithms can decide just in 17 seconds with selected electroencephalography (EEG) channels and it has an accuracy rate of 90.83%.
System Architecture for Brain-Computer Interface based on Machine Learning an...ShahanawajAhamad1
Brain functions are required to be read for curing
neurological illness. Brain-Computer Interface (BCI) connects
the brain to the digital world for brain signals receiving,
recording, processing, and comprehending. With a BrainComputer Interface (BCI), the information from the user’s brain
is fed into actuation devices, which then carry out the actions
programmed into them. The Internet of Things (IoT) has made it
possible to connect a wide range of everyday devices.
Asynchronous BCIs can benefit from an improved system
architecture proposed in this paper. Individuals with severe
motor impairments will particularly get benefit from this feature.
Control commands were translated using a rule-based
translation algorithm in traditional BCI systems, which relied
only on EEG recordings of brain signals. Examining BCI
technology’s various and cross-disciplinary applications, this
argument produces speculative conclusions about how BCI
instruments combined with machine learning algorithms could
affect the forthcoming procedures and practices. Compressive
sensing and neural networks are used to compress and
reconstruct ECoG data presented in this article. The neural
networks are used to combine the classifier outputs adaptively
based on the feedback. A stochastic gradient descent solver is
employed to generate a multi-layer perceptron regressor. An
example network is shown to take a 50% compression ratio and
89% reconstruction accuracy after training with real-world,
medium-sized datasets as shown in this paper
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
Brain computer interface based smart keyboard using neurosky mindwave headsetTELKOMNIKA JOURNAL
In the last decade, numerous researches in the field of electro-encephalo-graphy (EEG) and brain-computer-interface (BCI) have been accomplished. BCI has been developed to aid disabled/partially disabled people to efficiently communicate with the community. This paper presents a control tool using the Neurosky Mindwave headset, which detects brainwaves (voluntary blinks and attention) to form a brain-computer interface (BCI) by receiving the system signals from the frontal lobe. This paper proposed an alternative computer input device for those disabled people (who are physically challenged) rather than the conventional one. The work suggested to use two virtual keyboard designs. The conducted experiment revealed a significant result in developing user printing skills on PCs. Encouraging results (1.55-1.8 word per minute (WPM)) were obtained in this research in comparison to other studies.
Modelling and Analysis of EEG Signals Based on Real Time Control for Wheel ChairIJTET Journal
Free versatility is center to having the capacity to perform exercises of day by day living without anyone else's input. In this proposed framework introduce an imparted control construction modeling that couples the knowledge and cravings of the client with the exactness of a controlled wheelchair. Outspread Basis Function system was utilized to characterize the predefined developments, for example, rest, forward, regressive, left and right of the wheelchair. This EEG-based cerebrum controlled wheelchair has been produced for utilization by totally incapacitated patients. The proposed outline incorporates a novel methodology for selecting ideal terminal positions, a progression of sign transforming and an interface to a controlled wheelchair.The Brain Controlled Wheelchair (BCW) is a basic automated framework intended for individuals, for example, bolted in individuals, who are not ready to utilize physical interfaces like joysticks or catches. The objective is to add to a framework usable in healing centers and homes with insignificant base alterations, which can help these individuals recover some portability. Also, it is explored whether execution in the STOP interface would be influenced amid movement, and discovered no modification with respect to the static performance.Finally, the general procedure was assessed and contrasted with other cerebrum controlled wheelchair ventures. Notwithstanding the overhead needed to choose the destination on the interface, the wheelchair is quicker than others .It permits to explore in a commonplace indoor environment inside a sensible time. Accentuation was put on client's security and comfort,the movement direction procedure guarantees smooth, protected and unsurprising route, while mental exertion and exhaustion are minimized by lessening control to destination determination.
One-day system authentication could be widely achieved through brainwaves. One doesn’t need to remember that 8 or more character long strange password. Simply thinking of certain things, such as a person face, or a rotating displayed cube, or line of song would be enough to unlock a device. Electro-encephalography (EEC) sensors are behind the technique. That is where electrical activity in certain parts of the brain is recorded. These sensors are used to generate the graphical lines on charts created from wired electrodes placed on the scalp, as seen in hospitals and TV shows. They are used in hospital to diagnose epilepsy, among other things. In this case, though, one wouldn’t need to be fitted with wired electrodes —or even a headset, which is used already in some current non-muscular EEC computer controls. An ear bud will collect the signals (mental gesture) and perform secure authentication. This research could provide hands-free and wireless interaction, authentication, and user experience, all in the form-factor of a typical ear bud.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
1. Seminar
On
The Brain Fingerprinting Through Digital
Electroencephalography Signal Technique
Presented By
STUDENT NAME :-Priyanka Rohidas Parkhande
Exam No :T150714241
Guided By
PROF. A.S.SONDKAR
3. CONTENTS
INTRODUCTION
THE INVENTION
WORKING
MERMER
P300
OPERATING MECHANNISM
EQUPMENT AND TECHNIQUES
EEG
STAGES OF BRAIN FINGERPRINTING
FEATURES AND APPLICATION
ROLE IN CRIMINAL PROCEEDINGS AND THE ROLE IN LEGAL
PROCEEDINGS
ADVANTAGES AND DISADVANTAGES
LIMITITION
CONCLUSION
REFERENCE
THANK YOU
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
3
4. Introduction
• Brain Fingerprinting scientifically
Identifies whether information is
stored in The brain by precisely
measurig brainwaves.
• Brain Fingerprinting only
Detects whether the information
Exist within the brain or not.
6/8/2021
The Brain Fingerprinting Through Digital Electroencephalography Signal Technique
Department of computer science and engineering 4
5. Continue….
• Farwell Brain Fingerprinting has proven 100%
accurate in over 120 tests.
• Including tests on FBI agents, tests for a US
intelligence agency and for the US Navy, and tests
on real-life situations including actual crimes.
• The technique of Farwell Brain Fingerprinting, a
new computer-based technology to identify the
perpetrator of a crime accurately and scientifically
by measuring brain wave responses to crime-
relevant words
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
5
6. THE INVENTION
• Brain fingerprinting was invented by Lawrence
Farewell.
• Brain fingerprinting technology is based on an
electric signal known as MERMER.
• Farwell’s Brain Fingerpriinting originally used the
well known P300 brain response to detect the
brain’s recognition of the known information
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
6
7. How Does it Work?
measurements are recorded in fractions of a second after the
stimulus is presented, before the subject is able to formulate or
control a response
Dr. Farwell discovered that the P300 was one aspect of a larger
brain-wave response that he named and patented, a MERMER (
encoding related multifaceted memory and electroencephalographic
response)
Brain responses were recorded from the midline frontal, central, and
parietal scalp locations, referenced to linked mastoids (behind the
ear), and from a location on the forehead to track eye movements
At the end of each test, subjects were given a written list of all
stimulus items and asked to mark each item as noteworthy,
somewhat noteworthy, or irrelevant - those marked were thrown out
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
7
8. TO CHECK INFORMATION IS PRESENT OR NOT
Information Absent Information Present
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
8
9. WORKING PROCEDURE
• First of all the suspect wears a special headset
on which many sensors are fitted to capture the
brain wave signals. He is seated before a
comp. system on which stimulies are
presented.
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
9
10. MERMER
• Farwell discovered the P300 –MERMER(“Memory and
Encoding Related Multifaced Electroencephalographic
Response”)
• A MERMER is an electric signal which is part of brainwave
observed in response to familiar information .
• When the brain recognizes something , then there is increase in
neurons activity,so elicit some changes in brain wave signals.
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
10
11. P300
The P300(P3) wave is an event related
potential(ERP) which can be recorded via EEG
as a positive defination in voltage at a latency
of roughly 300 ms in EEG.
The P300 signal is an aggregate recording
from a great many neurons .
P300 wave form must be evoked using a
stimulus delivered one of the sensory
modilities .
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
11
12. Block Diagram of Technology
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
12
13. Equipment and technology:
The brain fingerprinting system comprises:
1. A personal computer.
2. A data acquisition board.
3. Two monitors.
4 A EEG amplifier.
5. Software for data acquisition Some electrodes.
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
13
14. TECHNIQUE USED
P300-MERMER
Memory and Encoding Related Multifaceted
Electroencephalographic Response
• Electrical signal known as P300 is emitted from an individual's brain
beginning approximately 300 milliseconds after it is confronted with
a stimulus of special significance.
• The P300 wave is an event related potential
• (ERP) which can be recorded via electroencephalography (EEG) as
a deflection in voltage at a latency of roughly 300 ms in the EEG
15. TECHNIQUES
• Electrical signal known as P300 is emitted from an individual's
brain beginning approximately 300 milliseconds after it is
confronted with a stimulus of special significance.
• The application of this in brain fingerprinting is to detect the
P300 as a response to stimuli.
• The system does not require the subject to issue verbal
responses to questions or stimuli.
• brain fingerprinting uses cognitive brain responses, brain
fingerprinting does not depend on the emotions of the subject,
nor is it affected by emotional responses.
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
15
16. BASIC PRINCIPLE
• It is based on the theory that throughout any action, the
brain analyze, executes and records that action.
• And when the brain recognizes something then there are
some changes in the neurons activity due to which there is
changes in brainwave signals .On the basis of these changes
in brain wave signals scientists determine that a particular
information is present in the subjest mind or not.
• The brain wave signal which is used in this technique is
well-known brain wave p 300-MERMER.
• P300-MERMER
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
16
17. Brain Waves are used to detect
the crime
A suspect is tested by looking at three kinds of
information represented by different colored lines :
• RED: Information the suspect is expected to know. It
arises due to target type stimulus.
• GREEN Information not to suspect. The irrelevant
stimuli is responsible for this type of brain waves.
• BLUE Information of the crime that only perpetrator
would know. This occurs due to probes.
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
17
19. ONE OF THE TEST CASES OF A
SUSPECT
The Following figure shows the RED and BLUE
lines are closely correlated. This indicates the
suspect or the criminal has the knowledge of the
CRIME.
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
19
20. Electroencephalography
• Electroencephalography (EEG) is the
measurement of electrical activity produced by
the brain as recorded from electrodes placed on
the scalp.
• Just as the activity in a computer can be
understood on multiple levels, from the activity of
individual transistors to the function of
applications, so can the electrical activity of the
brain be described on relatively small to relatively
large scales.
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
20
21. Continue……..
• Electroencephalography (EEG) is the
measurement of electrical activity produced by
the brain as recorded from electrodes placed
on the scalp.
• Special sensors (electrodes camera) are
attached to your head and hooked by wires to a
computer.
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
21
22. Continue…
• Special sensors (electrodes camera) are
attached to your head and hooked by wires to a
computer.
• The computer records your brain's electrical
activity on the screen or on paper as wavy
lines.
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
22
23. CONTINUE……
• EEG signals in the range of milli-volts
• The data measured by the scalp EEG are used for clinical
and research purposes.
There are several types of brain waves:
1)Beta waves
2)Delta waves
3)Theta waves
4)Alpha waves
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
23
24. CONTINUE….
• Electroencephalogram has two types:
1)Normal &
2)Abnormal
Normal:
The two sides of the brain show similar
patterns of electrical activity
Abnormal:
The two sides of the brain show different
patterns of electrical activity
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
24
25. EEG MEASUREMENT
EEG-ELECRTOENCEPHALOGRAPHY
• When a stimulus appears, the EEG breaks into
a series of larger peaks and troughs which
constitutes the ERP.
• Voltage difference between a pair of electrodes
are measured, filtered, amplified and recorded
for analysis.
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
25
26. EEG VS FMRI AND PET
• In EEG time resolution is very high (on the level
of a single millisecond).
• Other methods of looking at brain activity, such as
PET and FMRI have time resolution between
seconds and minutes.
• EEG measures the brain's electrical activity
directly
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
26
27. STAGES OF BRAIN FINGERPRINTING
• Crime Scene Evidence Collection
• Brain Evidence Collections
• Computer Evidence Analysis
• Scientific Result
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
27
28. TYPES OF STIMULIES
Target: The target stimuli are made relevant
and noteworthy to all subjects.
Irrelevant: These have no relation to the
situation under investigation.
Probes: Probes are the stimuli that are
relevant to the situation under investigation.
6/8/2021
Project Title Department of
Computer Engineering, SCSCOE
28
30. Phases of Brain Fingerprinting
There are four stages to Brain Fingerprinting:
• 1. Crime Scene Evidence Collection:
Gathering evidences from crimescenes.
• 2. Brain Evidence Collection:
A specialist checks whether the crime scene evidence
matches evidence stored in brain.
• 3. Computer Evidence Analysis:
Computerized analysis is done on the brain evidences and
statistical methods are applied to move to the next phase.
• 4. Scientific Result:
Finding whether the person is guilty or not guilty.
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31. Application of Technology
• 1) National security: To Identify terrorists , fugitives,
criminals and accomplices prior to attack by determining
whether specific information is embedded into brain
memory of the subject.
• 2) Medical diagnosis : a) Alzheimer’s Disease : detects
p300 brainwave, symptoms reversible through dietary and
medicinal changes.b) Pharmaceutical companies: to see
effects of new medication, doctors can monitor treatments
and adjust them.
• 3) Advertising :It allow advertisers to determine what
information from an ad is retained in memory as (i) what
elements dopeople pay attention to .(ii) what type of media
is most effective . (iii) how to people all over the world.
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32. Advertising Applications
• What specific information do people retain from
advertising?
• What specific elements in an ad campaign have
the most impact?
• Which type of media is most effective?
• What commercial is the most effective for a
single product?
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33. Comparison with other
technology
• v The novel interpretation in brain fingerprinting is to look for P300
as response to stimuli related to the crime in question e.g., a murder
weapon or a victims face. Be-cause it is based on EEG signals, the
system does not require the testee to issue ver-bal responses to
questions or stimuli.
• v Brain fingerprinting uses cognitive brain responses; brain finger
printing does not depend on the emotions of the subject, nor is it
affected by emotional responses.
• v Brain fingerprinting is fundamentally different from the polygraph
(lie-detector), which measures emotion-based physiological signals
such as heart rate, sweating, and blood pressure. Also, unlike
polygraph testing, it does not attempt to determine whether or not
the subject is lying or telling the truth
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34. National Security
• Identify terrorists and accomplices prior to
attacks by determining whether specific
information is embedded in the memory of the
subject or not.
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35. ROLE IN CRIMINAL PROCEEDINGS
The application of Brain Fingerprinting testing in a
criminal case involves four phases: investigation,
interview, scientific testing, and adjudication. Of
these four phases, only the third one is in the
domain of science. The first phase is undertaken
by a skilled investigator, the second by an
interviewer who may be an investigator or a
scientist, the third by a scientist,and the fourth by
a judge and jury. This is similar to the forensic
application of other sciences.
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36. THE ROLE IN LEGAL
PROCEEDINGS
• In legal proceedings, the scope of the science
of Brain Fingerprinting - and all other sciences
- is limited.
• The role of Brain Fingerprinting is to take the
output of investigations and interviews
regarding What information is relevant.
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37. Advantages
The various advantages are as follows:
1. The rate of error is extremely low virtually nonexistent and
clear standards governing.
2. Record of 100% accuracy.
3. Identifies the criminal quickly and scientifically.
4. Provides immediate scientific result.
5. Reduced costs and complexities.
6. Support the right to a speedy and fair trail.
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38. Disadvantages
The various disadvantages are as follows:
1. Not applicable for general screening.
2. It does not indicate intent of the crime.
3. Takes a fair amount of time to set up and conduct
properly.
4. Difficult to distinguish the criminal and a witness who
saw all the criminal activity happen.
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39. CONCLUSION
Brain Fingerprinting technology is a advanced brain computer interface
technology for solving the criminals case and also identify the perpetrators,
and exonerating innocent suspects. This technology provide the 99.9%
accurate result towards crime victims, falsely accused innocent suspects.
Brain Fingerprinting is a revolutionary new scientific technology
for solving crimes, identifying perpetrators, and exonerating innocent suspects,
with a record of 100% accuracy in research with US government agencies,
actual criminal cases, and other applications. The technology fulfills an urgent
need for governments, law enforcement agencies, corporations, investigators,
crime victims, and falsely accused, innocent suspects.
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40. References
• 1) Farwell LA, Donchin E. The brain detector: P300 in the detection of deception.
Psychophysiology 1986; 24:434.
• 2) Farwell LA, Donchin E. The truth will out: interrogative polygraphy ("lie detection") with
event-related brain potentials. Psychophysiology 1991;28:531-541.
• 3)Farwell LA, inventor. Method and apparatus for multifaceted electroencephalographic
response analysis (MERA). US patent 5,363,858. 1994 Nov 15.
• 4) Farwell LA. Two new twists on the truth detector: brain-wave detection of occupational
information. Psychophysiology 1992;29(4A):S3.
• 5) Farwell LA, inventor. Method and apparatus for truth detection. US patent 5,406,956. 1995
Apr 18.
• 6)Picton TW. Handbook of electroencephalography and clinical neurophysiology: human
event-related potentials. Amsterdam:
• 7)Goggle.com
• 8)Youtube.com
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