Deception detection has important legal and medical applications, but the reliability of methods for the differentiation between truthful and deceptive responses is still limited. Deception detection can be more accurately achieved by measuring the brain correlates of lying in an individual. For the evaluation of the method, several participants were gone through the designed concealed information test paradigm and their respective brain signals were recorded. The electroencephalogram (EEG) signals were recorded and separated into many single trials. To enhance signal noise ratio (SNR) of P3 components, the independent component analysis (ICA) method was adopted to separate non-P3 (i.e. artifacts) and P3 components from every single trial. Then the P3 waveforms with high SNR were reconstructed. And then group of features based on time, frequency, and amplitude were extracted from the reconstructed P3 waveforms. Finally, two different class of feature samples were used to train a support vector machine (SVM) classifier because it has higher performance compared with several other classifiers. The method presented in this paper improves the efficiency of CIT and deception detection in comparison with previous reported methods.
Abstract:
Brain fingerprinting is based on finding that the brain generates a unique brain wave pattern when a person encounters a familiar stimulus Use of functional magnetic resonance imaging in lie detection derives from studies suggesting that persons asked to lie show different patterns of brain activity than they do when being truthful. Issues related to the use of such evidence in courts are discussed. The author concludes that neither approach is currently supported by enough data regarding its accuracy in detecting deception to warrant use in court.
In the field of criminology, a new lie detector has been developed in the United States of America. This is called “brain fingerprinting”. This invention is supposed to be the best lie detector available as on date and is said to detect even smooth criminals who pass the polygraph test (the conventional lie detector test) with ease. The new method employs brain waves, which are useful in detecting whether the person subjected to the test, remembers finer details of the crime. Even if the person willingly suppresses the necessary information, the brain wave is sure to trap him, according to the experts, who are very excited about the new kid on the block.
Introduction:
Brain Fingerprinting is a controversial proposed investigative technique that measures recognition of familiar stimuli by measuring electrical brain wave responses to words, phrases, or pictures that are presented on a computer screen. Brain fingerprinting was invented by Lawrence Farwell. The theory is that the suspect's reaction to the details of an event or activity will reflect if the suspect had prior knowledge of the event or activity. This test uses what Farwell calls the MERMER ("Memory and Encoding Related Multifaceted Electroencephalographic Response") response to detect familiarity reaction. One of the applications is lie detection. Dr. Lawrence A. Farwell has invented, developed, proven, and patented 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 or pictures presented on a computer screen. 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..
Abstract:
Brain fingerprinting is based on finding that the brain generates a unique brain wave pattern when a person encounters a familiar stimulus Use of functional magnetic resonance imaging in lie detection derives from studies suggesting that persons asked to lie show different patterns of brain activity than they do when being truthful. Issues related to the use of such evidence in courts are discussed. The author concludes that neither approach is currently supported by enough data regarding its accuracy in detecting deception to warrant use in court.
In the field of criminology, a new lie detector has been developed in the United States of America. This is called “brain fingerprinting”. This invention is supposed to be the best lie detector available as on date and is said to detect even smooth criminals who pass the polygraph test (the conventional lie detector test) with ease. The new method employs brain waves, which are useful in detecting whether the person subjected to the test, remembers finer details of the crime. Even if the person willingly suppresses the necessary information, the brain wave is sure to trap him, according to the experts, who are very excited about the new kid on the block.
Introduction:
Brain Fingerprinting is a controversial proposed investigative technique that measures recognition of familiar stimuli by measuring electrical brain wave responses to words, phrases, or pictures that are presented on a computer screen. Brain fingerprinting was invented by Lawrence Farwell. The theory is that the suspect's reaction to the details of an event or activity will reflect if the suspect had prior knowledge of the event or activity. This test uses what Farwell calls the MERMER ("Memory and Encoding Related Multifaceted Electroencephalographic Response") response to detect familiarity reaction. One of the applications is lie detection. Dr. Lawrence A. Farwell has invented, developed, proven, and patented 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 or pictures presented on a computer screen. 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..
Brain Fingerprinting is a new computer-based technology to identify the perpetrator of a crime accurately and scientifically by measuring brain-wave responses to crime-relevant words or pictures presented on a computer screen. 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 felony crimes. Brain fingerprinting is based on finding that the brain generates a unique brain wave pattern when a person encounters a familiar stimulus Use of functional magnetic resonance imaging in lie detection derives from studies suggesting that persons asked to lie show different patterns of brain activity than they do when being truthful. Issues related to the use of such evidence in courts are discussed. The author concludes that neither approach is currently supported by enough data regarding its accuracy in detecting deception to warrant use in court. In the field of criminology, a new lie detector has been developed in the United States of America. This is called “brain fingerprinting”. This invention is supposed to be the best lie detector available as on date and is said to detect even smooth criminals who pass the polygraph test (the conventional lie detector test) with ease. The new method employs brain waves, which are useful in detecting whether the person subjected to the test, remembers finer details of the crime. Even if the person willingly suppresses the necessary information, the brain wave is sure to trap him, according to the experts, who are very excited about the new kid on the block.
Brain Fingerprinting is a controversial forensic science technique that uses electroencephalography (EEG) to determine whether specific information is stored in a subject's brain. It does this by measuring electrical brainwave responses to words, phrases, or pictures that are presented on a computer screen (Farwell & Smith 2001, Farwell, Richardson, and Richardson 2012).
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Recognition of emotional states using EEG signals based on time-frequency ana...IJECEIAES
The recognition of emotions is a vast significance and a high developing field of research in the recent years. The applications of emotion recognition have left an exceptional mark in various fields including education and research. Traditional approaches used facial expressions or voice intonation to detect emotions, however, facial gestures and spoken language can lead to biased and ambiguous results. This is why, researchers have started to use electroencephalogram (EEG) technique which is well defined method for emotion recognition. Some approaches used standard and pre-defined methods of the signal processing area and some worked with either fewer channels or fewer subjects to record EEG signals for their research. This paper proposed an emotion detection method based on time-frequency domain statistical features. Box-and-whisker plot is used to select the optimal features, which are later feed to SVM classifier for training and testing the DEAP dataset, where 32 participants with different gender and age groups are considered. The experimental results show that the proposed method exhibits 92.36% accuracy for our tested dataset. In addition, the proposed method outperforms than the state-of-art methods by exhibiting higher accuracy.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
A New Model of H-Bridge Multilevel Inverter for Reduced Harmonics DistortionIJERA Editor
In this paper the cascaded H Bridge multilevel inverter (CHB-MLI) is discussed and mainly focuses on the
modified H-Bridge multilevel inverter in which the number of power devices is reduced. The analysis of fivelevel,
seven-level and nine-level MLI are also done. The various control strategies are also introduced which
effectively reduce the harmonics. The THD of five-level multilevel inverter is reduced to 16.91% which is much
lower than the nine-level MLI.
Brain Fingerprinting is a new computer-based technology to identify the perpetrator of a crime accurately and scientifically by measuring brain-wave responses to crime-relevant words or pictures presented on a computer screen. 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 felony crimes. Brain fingerprinting is based on finding that the brain generates a unique brain wave pattern when a person encounters a familiar stimulus Use of functional magnetic resonance imaging in lie detection derives from studies suggesting that persons asked to lie show different patterns of brain activity than they do when being truthful. Issues related to the use of such evidence in courts are discussed. The author concludes that neither approach is currently supported by enough data regarding its accuracy in detecting deception to warrant use in court. In the field of criminology, a new lie detector has been developed in the United States of America. This is called “brain fingerprinting”. This invention is supposed to be the best lie detector available as on date and is said to detect even smooth criminals who pass the polygraph test (the conventional lie detector test) with ease. The new method employs brain waves, which are useful in detecting whether the person subjected to the test, remembers finer details of the crime. Even if the person willingly suppresses the necessary information, the brain wave is sure to trap him, according to the experts, who are very excited about the new kid on the block.
Brain Fingerprinting is a controversial forensic science technique that uses electroencephalography (EEG) to determine whether specific information is stored in a subject's brain. It does this by measuring electrical brainwave responses to words, phrases, or pictures that are presented on a computer screen (Farwell & Smith 2001, Farwell, Richardson, and Richardson 2012).
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Recognition of emotional states using EEG signals based on time-frequency ana...IJECEIAES
The recognition of emotions is a vast significance and a high developing field of research in the recent years. The applications of emotion recognition have left an exceptional mark in various fields including education and research. Traditional approaches used facial expressions or voice intonation to detect emotions, however, facial gestures and spoken language can lead to biased and ambiguous results. This is why, researchers have started to use electroencephalogram (EEG) technique which is well defined method for emotion recognition. Some approaches used standard and pre-defined methods of the signal processing area and some worked with either fewer channels or fewer subjects to record EEG signals for their research. This paper proposed an emotion detection method based on time-frequency domain statistical features. Box-and-whisker plot is used to select the optimal features, which are later feed to SVM classifier for training and testing the DEAP dataset, where 32 participants with different gender and age groups are considered. The experimental results show that the proposed method exhibits 92.36% accuracy for our tested dataset. In addition, the proposed method outperforms than the state-of-art methods by exhibiting higher accuracy.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
A New Model of H-Bridge Multilevel Inverter for Reduced Harmonics DistortionIJERA Editor
In this paper the cascaded H Bridge multilevel inverter (CHB-MLI) is discussed and mainly focuses on the
modified H-Bridge multilevel inverter in which the number of power devices is reduced. The analysis of fivelevel,
seven-level and nine-level MLI are also done. The various control strategies are also introduced which
effectively reduce the harmonics. The THD of five-level multilevel inverter is reduced to 16.91% which is much
lower than the nine-level MLI.
Non-life claims reserves using Dirichlet random environmentIJERA Editor
The purpose of this paper is to propose a stochastic extension of the Chain-Ladder model in a Dirichlet random
environment to calculate the provesions for disaster payement. We study Dirichlet processes centered around the
distribution of continuous-time stochastic processes such as a Brownian motion or a continuous time Markov
chain. We then consider the problem of parameter estimation for a Markov-switched geometric Brownian
motion (GBM) model. We assume that the prior distribution of the unobserved Markov chain driving by the
drift and volatility parameters of the GBM is a Dirichlet process. We propose an estimation method based on
Gibbs sampling.
A Wind driven PV- FC Hybrid System and its Power Management Strategies in a GridIJERA Editor
This paper shows the work done on the method to operate a Wind driven grid connected hybrid system which is composed of a Photovoltaic (PV) array and a Proton exchange membrane fuel cell . A wind system provides with an opportunity to harness the abundantly available renewable resource. With the proton exchange membrane the hybrid system output power becomes controllable. Here the system uses two operation modes, the unit-power control (UPC) mode and the feeder-flow control (FFC) mode. This papers discusses the coordination of two control modes, the coordination of the PV array and the proton exchange membrane fuel cell in hybrid system and the way in which the reference parameters are determined.
Nowadays, cyber-attacks from botnets are increasing at a faster rate than any other malware spread. Detecting the botmaster who commands the tasks has become more difficult. Most of the detecting methods are based on the features of any communication protocol or the history of the network traffic. In this paper, a rational approach is brought for the live detection of the botmaster in the internal network. The victim machine monitors its packets and compromises the bots in the network and finds the traces to the botmaster. This approach works independent of the structure of the botnet, and will be a better option for online detection of the botmaster.
Optimization of “T”-Shaped Fins Geometry Using Constructal Theory and “FEA” C...IJERA Editor
This paper reports the geometric (constructal) optimization of T-shaped fin assemblies, where the objective is to maximize the global thermal conductance of the assembly, subject to total volume and fin-material constraints. Assemblies of plate fins are considered. It is shown that every geometric feature of the assembly is delivered by the optimization principle and the constraints. These optimal features are reported in dimensionless terms for this entire class of fin assemblies. Based on the constructal theory by Dr. A Bejan, T-shaped fins are developed for better heat conductance as compared to conventional fins. Now the geometry of this T type of fin contains many geometry parameters which affect the overall conductance of the fin. With the same material constraint and volume constraints optimal geometry ratios has been calculated so as to design the fin for its best performance. With focus to the practical situations and heat flow patterns, it is quite complex to calculate the temperatures on a T-shaped fin. It requires the help of FEA concepts and CAE software to optimize the geometry.
FPGA based Real-time Automatic Number Plate Recognition System for Modern Lic...IJERA Editor
This paper proposes a real-time number plate recognition technique which involves localizing the number plate from a rear view image of a vehicle, processing it using image processing and image enhancement techniques to segment and extract the characters in the number plate which are in turn matched against a set of stored templates for an accurate character recognition process. The entire system is developed on a FPGA to achieve real-time processing.
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Brain Fingerprinting is scientific technique to determine whether or not specific information is stored in an individual's brain.
Ruled Admissible in one US Court as scientific evidence.
It has a record of 100% Accuracy.
Brain fingerprinting is based on finding that the brain generates a unique brain wave pattern when a person encounters a familiar stimulus Use of functional magnetic resonance imaging in lie detection derives from studies suggesting that persons asked to lie show different patterns of brain activity than they do when being truthful. Issues related to the use of such evidence in courts are discussed. The author concludes that neither approach is currently supported by enough data regarding its accuracy in detecting deception to warrant use in court.
In the field of criminology, a new lie detector has been developed in the United States of America. This is called “brain fingerprinting”. This invention is supposed to be the best lie detector available as on date and is said to detect even smooth criminals who pass the polygraph test (the conventional lie detector test) with ease. The new method employs brain waves, which are useful in detecting whether the person subjected to the test, remembers finer details of the crime. Even if the person willingly suppresses the necessary information, the brain wave is sure to trap him, according to the experts, who are very excited about the new kid on the block.
Fingerprinting is a controversial proposed investigative technique that measures recognition of familiar stimuli by measuring electrical brain wave responses to words, phrases, or pictures that are presented on a computer screen. Brain fingerprinting was invented by Lawrence Farwell. The theory is that the suspect's reaction to the details of an event or activity will reflect if the suspect had prior knowledge of the event or activity. This test uses what Farwell calls the MERMER ("Memory and Encoding Related Multifaceted Electroencephalographic Response") response to detect familiarity reaction. One of the applications is lie detection. Dr. Lawrence A. Farwell has invented, developed, proven, and patented 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 or pictures presented on a computer screen. 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.
Brain Fingerprinting is a technique used to determine scientifically what information is, or is not stored in a particular brain.
Brain Finger Printing was invented by Dr B .S. Farwell chief scientist and president of human brain research and laboratory , USA
Brain fingerprinting is based on finding that the brain generates a unique brain wave pattern when a person encounters a familiar stimulus use of functional magnetic resonance imaging in lie detection derives from studies suggesting that persons asked to lie show different patterns of brain activity than they do being truthful. Issue related to the use of such evidence in courtsare discussed.The author concludes that neither approach is currently supported by enough data regarding its accuracy in detecting deception to warrant use in court. In the field of criminology a new lie detector has been developed in USA. This is called “BRAIN FINGERPRINTING”.The invention is supposed to be the best lie detector even smooth criminals who paas the polygraph Test with ease.The new method employs brainwaves ,which are useful in detecting whether the person is subjected to test remember finer details of crime,even if the person willingly suppressesthe necessary information,the brain wave is sure to trap him ,according to the experts who are very excited about the new kid on the block.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Mission to Decommission: Importance of Decommissioning Products to Increase E...
REVIEW: Previous Deception detection methods and New proposed method using independent component analysis of EEG signals.
1. Roshni D. Tale Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 12( Part 6), December 2014, pp.178-182
www.ijera.com 178|P a g e
REVIEW: Previous Deception detection methods and New
proposed method using independent component analysis of EEG
signals.
Roshni D. Tale*, Bhavana P.Harne**
* (Department of Electronics and Telecommunication engineering, S.S.G. M. College of Engineering, Shegaon,
India, 444203.) **
(Department of Electronics and Telecommunication Engineering , .S.S.G. M. College of Engineering, Shegaon,
India, 444203)
ABSTRACT
Deception detection has important legal and medical applications, but the reliability of methods for the
differentiation between truthful and deceptive responses is still limited. Deception detection can be more
accurately achieved by measuring the brain correlates of lying in an individual. For the evaluation of the
method, several participants were gone through the designed concealed information test paradigm and their
respective brain signals were recorded. The electroencephalogram (EEG) signals were recorded and separated
into many single trials. To enhance signal noise ratio (SNR) of P3 components, the independent component
analysis (ICA) method was adopted to separate non-P3 (i.e. artifacts) and P3 components from every single trial.
Then the P3 waveforms with high SNR were reconstructed. And then group of features based on time,
frequency, and amplitude were extracted from the reconstructed P3 waveforms. Finally, two different class of
feature samples were used to train a support vector machine (SVM) classifier because it has higher performance
compared with several other classifiers. The method presented in this paper improves the efficiency of CIT and
deception detection in comparison with previous reported methods.
Keywords- Electroencephalogram, Independent component analysis, Support vector machine, Concealed
information test
I. INTRODUCTION
Detection is one of the most emotive and hotly
debated of all human technological endeavors. The
ability to detect deception has important legal, moral
and clinical implications, and has recently received
revived interest from the scientific community.
Deception is ubiquitous in human societies and is
essential for proper social interactions. Lying is a
complex process requiring suppression of the truth,
communication of a coherent falsehood, contextual
knowledge of that false situation, and modifications
and changes of behaviors to convince the receiver of
one‟s actions.[1] This complex and universal process
would seem amenable to detection by brain imaging.
The ability to measure noninvasively the Correlates
of lying in the brain within an individual could offer a
significant improvement over currently available
tools to detect deception. Currently, the most widely
used technique for the quantitative discrimination
between deceptive and truthful responses is polygraph
test, which relies on measures of autonomic nervous
system response, i.e. emotional efferents. Traditional
polygraphy, however, has been criticized for having
unacceptable level of reliability. Consequently, a
number of other recording modalities have recently
been investigated for the possible application to
deception detection; other method includes functional
magnetic resonance (fMRI) and
electroencephalography (EEG). Until recently, the
reported EEG studies in deception detection have
primarily focused on the topography and time-domain
analyses of the P300 component. However, there is
still disagreement about the features that may best
discriminate between deceptive and truthful
responses.[2]
In this paper we will discuss about the new
method of deception detection using
electroencephalograph (EEG) analysis of guilty and
truthful suspect. Here the most powerful technique
Independent component analysis is used to separate
the eeg signal as eeg signal is superposition of many
signal arising from brain and different noise such as
Electroculogram EOG) and EMG. Buried in the EEG
are signals that reveal information about brain
processes. These signals are detected by changes in
timing in the EEG after events such as listening to a
sound or seeing a picture. The resulting signal is
called an event related potential (ERP), which clearly
stands above the background brain activity. The ERP
can be divided into several basic components
represented as positive or negative fluctuations in the
ERP waveform at different delays after the
stimulating event.ERP component contains signals
such as P1, P2, N1, N2, N400 and P300. The signals
RESEARCH ARTICLE OPEN ACCESS
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generally arising after 250 milliseconds are thought to
reflect higher level cognitive processes such as
memory or language. The P300 is a specific electrical
brain wave that is triggered whenever a person sees a
object familiar to him. The P300 waves have been
understood in electrophysiology to mean that the
subject is able to consciously identify and categorize
a stimulus. For instance, if a subject has been
listening to trombone noises and a flute tone is
played, a P300 wave will appear 300 ms later on the
EEG machine. The P300 event-related potential can
be used to determine concealed knowledge that only a
criminal would know. By placing details of the crime
randomly among a list of non-relevant items, one can
distinguish criminal from citizen. If an individual
recognizes a detail of the crime, it produce a P300
EPR and is likely guilty of, or at least familiar with,
the crime.
II. THE DEVELOPMENT OF DECEPTION
DETECTION TECHNOLOGY
The U.S. judicial system places great weight in
the belief that juries are effective and reliable in
determining the credibility of the witness. Yet,
behavioral and social research has shown that humans
are good at lying and quite poor at lie detection. For
example, an average person‟s ability to detect
deception in a face-to-face interaction with another
individual is only modestly better than chance. Thus,
the critical importance of truthful testimony and the
inadequacy of human lie detectors have prompted the
perennial search for a technology-based, objective
method of lie detection or truth verification; this
search continues today. Let us take a review on
previous work and research regarding deception
detection.
II.1. DETECTING DECEPTION VIA
ANALYSIS OF VERBAL AND NON
VERBAL BEHAVIOUR
In the early ages technique called deception
detection via analysis of verbal and nonverbal
behavior had been developed. In this they examined
the hypotheses that a systematic analysis of nonverbal
behavior could be useful in deception detection and
that lie detection would be most accurate if both
verbal and nonverbal indicators of deception to be
consider. Seventy three nursing students participated
in a study about “telling lies” and either told the truth
or lied about a film they had just seen. The interviews
were audio and video recorded and the nonverbal
behavior (NVB) and speech content of the liars and
truth tellers were analyzed the later with the criteria
based content analysis technique and reality
monitoring technique. Results revealed several
nonverbal and verbal indicators of deception. If only
nonverbal behavior is considered 78% of the lies and
truths could be correctly classified. An even the
percentage of correct classification can be higher
when all three detection techniques were taken into
account. But this is completely hypothetical results
based so is not liable. There are the basic ways to
catch liars (A) By observing them how they behave
their movements, whether they smile or steal gazes,
pitch of their voice, rate of speech likewise. (B) By
listening to them what they says analyzing there
speech content, (C) By measuring physiological
responses.[4][5] Deception detection method bases on
verbal nonverbal behavior based on content-based
criteria analysis (CBCA) and Reality
monitoring(RM). Use of nonverbal cues for deceit
detection accuracy rate is usually 45 and 60 percent.
This method is not reliable because this is based on
hypothetical assumptions. Some liars may act
innocent during the test if they are skilled enough to
deceive and some innocent may found guilty because
of fear and nervousness.[1][6] This is not reliable
method.
II.2. POLYGRAPH TEST
Anaother most widely used method lately is
Polygraph Teste.The polygraph, which measures
activity of the peripheral nervous system to detect
deception .This method measure nonspecific
peripheral emotional/autonomic arousal that might or
might not be associated with lying. By their very
nature, polygraph measurements provide an
extremely limited and indirect view of complex
underlying brain processes.
II.3. FIELD MAGNETIC RESPONSE
IMAGING
Another emerging technique is FMRI. MRI is a
medical imaging technique using high magnetic fields
and non-ionizing electromagnetic radiation to
produce high resolution, three-dimensional (3D)
tomographic images of the body is distinguished from
regular (structural) MRI by the speed of acquisition of
each 3D image.[3] In fMRI, continuous images of the
entire brain are acquired after every few seconds,
which is fast enough to observe changes in the
regional blood volume and flow that are associated
with cognitive activity. Blood oxygenation level
dependent (BOLD) imaging is now being used in the
fMRI technique most commonly used in cognitive
neuroscience. BOLD depends on the difference in the
magnetic properties of the contents of the blood
vessels and the surrounding brain tissue as well as the
magnetic difference between oxygenated and
deoxygenated hemoglobin. BOLD fMRI does not
show absolute regional brain activity; rather, it
indicates relative changes in regional activity over
time. To make conclusions about the nature of the
regional brain activity, BOLD FMRI task designs rely
on a principle of “cognitive subtraction”. This
principle assumes that the fMRI signal difference
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between two behavioral conditions that are identical
in all but a single variable is due to this variable. We
have presented a method for parallel spatial and
temporal independent component analysis for
concurrent multi-subject single-trial EEG-fMRI
recordings that addresses the mixing problem in both
modalities. The data are integrated via correlation of
the trial-to-trial modulation of the recovered FMRI
maps with EEG time-courses. The method afforded
identification of an additional spatiotemporal process
corresponding to the auditory onset response and
subsequent low-level orienting/ change
detection.[6][7]The discussion details the area where
this method is applicable, and provides an account for
the potential functional role of the reported
component.
A researchable hypothesis is that by looking at
brain function more directly, it might be possible to
understand and ultimately detect deception. Based on
this supposition a number of neurophysiologic signals
have recently been investigated for the possible
application to deception detection, including
Functional Magnetic Resonance Imaging (fMRI) and
event related potentials (ERP). ERPs are recorded
from the central nervous system and are considered to
be affected by the recognition of important events,
which is more cognitively determined activity than
autonomic responses. An endogenous ERP, which has
been extensively studied, is the P300 (P3) wave. It is
seen in response to rare, meaningful stimuli often
called “oddball” stimuli.[8][9][10] The EEG is
composed of electrical potentials arising from several
sources. Each source (including separate neural
clusters, blink artifact, or pulse artifact) projects a
unique topography onto the scalp-'scalp maps'. These
maps are mixed according to the principle of linear
superposition. Independent component analysis (ICA)
attempts to reverse the superposition by separating
the EEG into mutually independent scalp maps, or
components so that the method used is the study is
independent component analysis (ICA).[11][12] After
applying ICA and extracting required response
features are extracted and then by using SVM
classifier we can see the difference between truthful
and deceptive responses.
III. PRPOSED METHOD
The brief idea about the method is that first
acquiring the EEG recording of the person under test
with the help of Concealed information test, then after
signal acquisition performing some basic filtering
operation on the acquired data so as to increase the
SNR of signal and reduce some percent of noise. We
know that EEG consist of many superimposed signal
so by applying powerful Independent Component
Analysis algorithm we will get refined and
independent signals from mixed EEG signal. After
applying ICA we can get the clear view of signal and
then feature extraction can be performed. Various
features can be extracted from the EEG signal such as
Frequency , time, amplitude, Eigen values etc. After
extracting the feature these features are applied to
SVM classifier. SVM classifier will classify the
features into two sets of value SVM classifier is one
of the powerful classifier to classify two sets of value
exactly.
III.1. CONCEALED INFORMATION
TEST
An alternative technique, the concealed
information test (CIT), also known as the guilty
knowledge test, has recently drawn considerable
attention among researchers. This test presents a set
of question items to an examiner, which include one
crime related item (critical item) and several control
items (noncritical items). Items are selected so that an
innocent examinee (i.e., one who does not possess the
information) would be unable to distinguish the
critical item from the noncritical items. In this study,
we used the CIT technique which relied on
contrasting brain waves evoked by relevant and
control stimuli, and developed a novel efficient (i.e.,
accuracy/time) EEG-based CIT using machine
learning algorithms. Through EEG signal processing,
we automatically detected brain waves corresponding
to different mental activity patterns to uncover the
critical item from noncritical items. Indeed, numerous
studies have previously demonstrated that CIT based
on brain signals can be very accurate. Besides
detection accuracy, it is important to develop fast
algorithms that can be used in real-life CIT
investigations, for a number of reasons.
Participants: The participants in this test are 5 healthy
right handed person of age between. They had no
previous history of neurological or mental
abnormalities.
Protocol: Participants will be given a total 3 card out
of which two will be face up on the computer screen
Participants will be informed that the identities of
these two face-up cards, as in some forms of poker,
were known by the participants, as well as the
researchers. Participants will then asked to choose a
third card from among two sealed envelope, each of
which contained a playing card which they kept in
their hand („target‟ card) and Rs50. Participants were
informed that only they knew the identity of this card,
and the experimenter would be attempting to learn the
identity of this card by alternately presenting a series
of cards, asking the question “Do you have this
card?”, and examining their brain responses. They
were told that if they were successful in concealing
the identity of the card, that would be able to keep Rs
50. Three categories of cards were presented to
participants. The „target‟ card was presented 10 times.
The “correct” response to the question “Do you have
this card?” for this card was “no.” The „truth‟ card
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was also presented 10 times with a correct response
of “no.” „Control‟ cards consisted of 2 presentations
each of the 2 „hand‟ cards, with a correct response of
“yes.” In this way the test is performed and EEG
recording of 5 persons can be recorded.
III.2. EEG SYSTEM AND
PREPROCESSING
The EEG recording will be made using Ag-AgCl
electrode placed on the scalp .The EPR is mainly
elicits in prefrontal lobe of brain. So the recording
from the electrode placed in this area is taken that is
recordings of electrode F3 F4 F8 F7 will be taken into
consideration. Then the recorded EEG is to be
processed for artifact correction and noise
suppression. EOG and EMG removal is possible with
the help of stem software. Then the principal of
independent component analysis should be applied.
III.3. INDEPENDENT COMPONENT
ANALYSIS
The EEG is composed of electrical potentials
arising from several sources. Each source (including
separate neural clusters, blink artifact, or pulse
artifact) projects a unique topography onto the scalp-
'scalp maps'. These maps are mixed according to the
principle of linear superposition. Independent
component analysis (ICA) attempts to reverse the
superposition by separating the EEG into mutually
independent scalp maps, or components so that the
method used is the study is independent component
analysis (ICA). [][]Independent component analysis
(ICA) was used for the processing of the filtered EEG
recordings. ICA is a signal processing technique that
models a set of input data in terms of statistically
independent variables: it is able to separate
independent components produced by distinct sources
from linearly mixed signals. The basic ICA model
can be described as.
X A ts
Where s(t)= [s1(t)…….. sm(t)]T
is a source signal
vector , x(t)=[x1(t),…,xn(t)] stands for the vector of
mixtures, and A denotes the [n×m] mixing matrix .
The minimal required apriori information is the
independence of the source signals and the fact that at
most one of the signals can have Gaussian
distribution. The mutual independence of the sources
is defined as:
1, 2 m
1
s s , .s
m
i i
i
f f s
where s1, s2,…, sm are the source signals, fi is the
probability density function of Si, and f is the joint
probability density
function of s1, s2,…, sm.
A solution for the ICA problem is possible if two
additional conditions are met: the mixing matrix is
full column rank and the number of recordings is at
least equal to the number of source signals. In this
case, the independent components (ICs) can be
retrieved by determining a [m×n] matrix W, named
unmixing matrix. Then, the m-dimensional vector
( ) ( )y t Wx t
is the best estimate of the source vector s(t). In this
way by applying ICA we will get different EEG
signal sources.
The study proves that ICA is a powerful tool
when the biomedical analysis involved more
channels, which is the case of electroencephalogram
and polysomnogram. In this case the important
information can be obtained considering only the
relevant signals, obtained after applying the
Independent Component Analysis.
III.4. FEATURE EXTRACTION AND
SVM CLASSIFIRE
A vast variety of approaches to the extraction of
quantitative features from an EEG signal was
introduced during more than 70 years of
electroencephalography. As for any signal, it seems
promising to elaborate a mathematical model of the
EEG signal. However, mathematical models and
physiological findings linking the EEG to electrical
activities of single nerve cells remain problematic,
and no single model of EEG dynamics has yet
achieved the goal of integrating the wide variety of
properties of an observed EEG and single-cell
activities.
Successful attempts were limited to
autoregressive modeling of short EEG segments.
Further significant progress in this direction can
hardly be expected, because the dynamics of EEG
depends on brain activities related to a very complex
dynamics of various types of information processing,
which is related to repeatedly renewed internal and
external information; thus stationary dynamic
equations evidently cannot adequately describe an
EEG signal. Support Vector Machines (SVM‟s) have
become extremely successful discriminative
approaches to pattern classification and regression
problems. Excellent results have been reported in
applying SVM‟s in multiple domains. However, the
application of SVM‟s to data sets where each element
has variable length remains problematic.
Furthermore, for those data sets where the elements
are represented by large sequences of vectors, such as
speech, video or image recordings, the direct
application of SVM‟s to the original vector space is
typically unsuccessful. While most research in the
SVM community has focused on the underlying
learning algorithms the study of kernels has also
gained importance recently. Standard kernels such as
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linear, Gaussian, or polynomial do not take full
advantage of the nuances of specific data sets.
IV. CONCLUSION
The aim of this paper is to focus on all the
previous researches took place in deception detection
and there liability. Deception detection can play very
important role in investigating crime and criminals.
Real, fast decision making technique is required so
the method can be developed using brain signals. The
proposed method can be implemented successfully
with high paradigm concealed information test and
highly accurate discrimination and classification
techniques for best results. The present study suggests
that further development is worthwhile, and would
provide assistance to forensic investigations in the
future.
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