An Conventional Methodology for Brain Finger Printing

2017 ◽  
Vol 7 (8) ◽  
pp. 301
Author(s):  
M. Ramasubramanian ◽  
TKS. Rathish Babu ◽  
VRS. Rajesh Kumar
Keyword(s):  
Brain Activity ◽  
Lie Detection ◽  
The United States ◽  
Wave Pattern ◽  
Familiar Stimulus ◽  
Functional Magnetic Resonance ◽  
Brain Wave ◽  
Finger Printing ◽  
The Brain ◽  
Brain Fingerprinting

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. In the field of criminology, a new lie detector has been developed in the United States of America.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 crime. According to the experts, even if the person willingly suppresses the necessary information, the brain wave is sure to trap him.

scholarly journals The Options and Limitations of the Brain Fingerprinting Lie Detection Method in the Criminal Proceeding1

2019 ◽  
Vol 18 (5) ◽  
Author(s):  
Árpád Budaházi ◽  
Zsanett Fantoly ◽  
Brigitta Kakuszi ◽  
István Bitter ◽  
Pál Czobor
Keyword(s):  
United States ◽  
Human Brain ◽  
United States Of America ◽  
Detection Method ◽  
Lie Detection ◽  
The United States ◽  
The Brain ◽  
Brain Fingerprinting

The aim of this study is to introduce the new lie detection method of brain fingerprinting already introduced in the United States of America. According to some scholars, the method of a brain-focused instrumental credibility examination of testimonies still unknown in Hungary is highly reliable, establishing their concept on their belief that the human brain does not lie. First of all, we shall examine the possibilities lying in the measure, and second of all, we shall introduce the doubts causing the delay of its admission in Hungary.

scholarly journals Adaptive brain activity changes during tongue movement with palatal coverage from fMRI data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuka Inamochi ◽  
Kenji Fueki ◽  
Nobuo Usui ◽  
Masato Taira ◽  
Noriyuki Wakabayashi
Keyword(s):  
Spatial Information ◽  
Brain Activity ◽  
Motor Impairment ◽  
Angular Gyrus ◽  
Tongue Movement ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Oral Environment ◽  
Motor Dexterity ◽  
The Brain

AbstractSuccessful adaptation to wearing dentures with palatal coverage may be associated with cortical activity changes related to tongue motor control. The purpose was to investigate the brain activity changes during tongue movement in response to a new oral environment. Twenty-eight fully dentate subjects (mean age: 28.6-years-old) who had no experience with removable dentures wore experimental palatal plates for 7 days. We measured tongue motor dexterity, difficulty with tongue movement, and brain activity using functional magnetic resonance imaging during tongue movement at pre-insertion (Day 0), as well as immediately (Day 1), 3 days (Day 3), and 7 days (Day 7) post-insertion. Difficulty with tongue movement was significantly higher on Day 1 than on Days 0, 3, and 7. In the subtraction analysis of brain activity across each day, activations in the angular gyrus and right precuneus on Day 1 were significantly higher than on Day 7. Tongue motor impairment induced activation of the angular gyrus, which was associated with monitoring of the tongue’s spatial information, as well as the activation of the precuneus, which was associated with constructing the tongue motor imagery. As the tongue regained the smoothness in its motor functions, the activation of the angular gyrus and precuneus decreased.

Methods of Predicting the Brain Activity Based on Noun

2013 ◽  
Vol 347-350 ◽  
pp. 2516-2520
Author(s):  
Jian Hua Jiang ◽  
Xu Yu ◽  
Zhi Xing Huang
Keyword(s):  
Brain Activity ◽  
Past Research ◽  
Neural Representation ◽  
Fmri Data ◽  
Semantic Features ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
The Past ◽  
Machine Learning Methods ◽  
The Brain

Over the last decade, functional magnetic resonance imaging (fMRI) has become a primary tool to predict the brain activity.During the past research, researchers transfer the focus from the picture to the word.The results of these researches are relatively successful. In this paper, several typical methods which are machine learning methods are introduced. And most of the methods are by using fMRI data associated with words features. The semantic features (properties or factors) support words neural representation, and have a certain commonality in the people.The purpose of the application of these methods is used for prediction or classification.

scholarly journals Distributed affective space represents multiple emotion categories across the brain

2017 ◽  
Author(s):  
Heini Saarimäki ◽  
Lara Farzaneh Ejtehadian ◽  
Enrico Glerean ◽  
liro P. Jääskeläinen ◽  
Patrik Vuilleumier ◽  
...  
Keyword(s):  
Functional Organization ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Subjective Feeling ◽  
Neural Activation ◽  
Functional Magnetic Resonance ◽  
Activation Patterns ◽  
Discrete Emotion ◽  
Subcortical Regions ◽  
The Brain

The functional organization of human emotion systems as well as their neuroanatomical basis and segregation in the brain remains unresolved. Here we used pattern classification and hierarchical clustering to reveal and characterize the organization of discrete emotion categories in the human brain. We induced 14 emotions (6 “basic”, such as fear and anger; and 8 “non-basic”, such as shame and gratitude) and a neutral state in participants using guided mental imagery while their brain activity was measured with functional magnetic resonance imaging (fMRI). Twelve out of 14 emotions could be reliably classified from the fMRI signals. All emotions engaged a multitude of brain areas, primarily in midline cortices including anterior and posterior cingulate and precuneus, in subcortical regions, and in motor regions including cerebellum and premotor cortex. Similarity of subjective emotional experiences was associated with similarity of the corresponding neural activation patterns. We conclude that the emotions included in the study have discrete neural bases characterized by specific, distributed activation patterns in widespread cortical and subcortical circuits, and highlight both overlaps and differences in the locations of these for each emotion. Locally differentiated engagement of these globally shared circuits defines the unique neural fingerprint activity pattern and the corresponding subjective feeling associated with each emotion.

scholarly journals GPT-2’s activations predict the degree of semantic comprehension in the human brain

2021 ◽  
Author(s):  
Charlotte Caucheteux ◽  
Alexandre Gramfort ◽  
Jean-Rémi King
Keyword(s):  
Brain Activity ◽  
Planum Temporale ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Brain Responses ◽  
The Brain ◽  
Semantic Comprehension ◽  
Empirical Framework ◽  
Superior Frontal Cortex ◽  
Language Network

Language transformers, like GPT-2, have demonstrated remarkable abilities to process text, and now constitute the backbone of deep translation, summarization and dialogue algorithms. However, whether these models actually understand language is highly controversial. Here, we show that the representations of GPT-2 not only map onto the brain responses to spoken stories, but also predict the extent to which subjects understand the narratives. To this end, we analyze 101 subjects recorded with functional Magnetic Resonance Imaging while listening to 70 min of short stories. We then fit a linear model to predict brain activity from GPT-2 activations, and correlate this mapping with subjects’ comprehension scores as assessed for each story. The results show that GPT-2’s brain predictions significantly correlate with semantic comprehension. These effects are bilaterally distributed in the language network and peak with a correlation above 30% in the infero-frontal and medio-temporal gyri as well as in the superior frontal cortex, the planum temporale and the precuneus. Overall, this study provides an empirical framework to probe and dissect semantic comprehension in brains and deep learning algorithms.

scholarly journals ARM Controller and EEG based Drowsiness Tracking and Controlling during Driving

2018 ◽  
Vol 6 (3) ◽  
pp. 127
Author(s):  
B. Naresh ◽  
S. Rambabu ◽  
D. Khalandar Basha
Keyword(s):  
Brain Activity ◽  
Traffic Signals ◽  
Electrical Signal ◽  
Emotional States ◽  
Brain Wave ◽  
Computer Interfaces ◽  
Medium Level ◽  
And Control ◽  
To Receive ◽  
The Brain

<span>This paper discussed about EEG-Based Drowsiness Tracking during Distracted Driving based on Brain computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity commands through controller device in real time. With these signals from brain in mat lab signals spectrum analyzed and estimates driver concentration and meditation conditions. If there is any nearest vehicles to this vehicle a voice alert given to driver for alert. And driver going to sleep gives voice alert for driver using voice chip. And give the information about traffic signal indication using RFID. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human feelings, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) is used to receive the raw data from brain wave sensor and it is used to extract and process the signal using Mat lab platform. The nearest vehicles information is information is taken through ultrasonic sensors and gives voice alert. And traffic signals condition is detected through RF technology.</span>

scholarly journals Altered resting-state network dynamics in Schizophrenia

2020 ◽  
Author(s):  
Ana Rita Lopes ◽  
Anna Sardinha Letournel ◽  
Joana Cabral
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Phase Locking ◽  
Bold Signal ◽  
Functional Magnetic Resonance ◽  
Probability Of Occurrence ◽  
Magnetic Resonance Data ◽  
Resonance Data ◽  
The Brain ◽  
Time Point

Schizophrenia remains a poorly understood disease, hence the interest in assessing and indirectly characterizing brain activity and connectivity. This paper aims to search for potential biomarkers in schizophrenia with functional magnetic resonance data, between subjects in the resting state. Firstly, we used fMRI from an open database, SchizConnect, of 48 subjects, in which 27 were control subjects, with no apparent disease and the others 21 were patients with schizophrenia. With the SPM tool, we proceeded to manually pre-process the images obtained, at the risk of having influenced the final results. Then, with the AAL atlas as a reference, we divided the brain into 116 areas. Then, brain activity in these areas were analysed, using the LEiDA method, which aims to characterize brain activity at each time point t by phase locking patterns of the BOLD signal. After the application of LEiDA, brain activity was evaluated based on trajectories and bar graphs of functional connectivity states in which the probability of occurrence and their dwell time were calculated for each state. It was also found that the visual cortex was the subsystem that showed significantly more probability of occurrence in schizophrenia patients to be assessed, and may correspond to symptoms of hallucinations by the patients with schizophrenia.

scholarly journals Deep Learning on Game Addiction Detection Based on Electroencephalogram

2021 ◽  
Vol 5 (3) ◽  
pp. 963
Author(s):  
Lalu Arfi Maulana Pangistu ◽  
Ahmad Azhari
Keyword(s):  
Brain Activity ◽  
Late Adolescence ◽  
Wave Activity ◽  
Test Results ◽  
Brain Wave ◽  
Brain Waves ◽  
Game Addiction ◽  
Brain Wave Activity ◽  
Electroencephalogram Eeg ◽  
The Brain

Playing games for too long can be addictive. Based on a recent study by Brand et al, adolescents are considered more vulnerable than adults to game addiction. The activity of playing games produces a wave in the brain, namely beta waves where the person is in a focused state. Brain wave activity can be measured and captured using an Electroencephalogram (EEG). Recording brain wave activity naturally requires a prominent and constant brain activity such as when concentrating while playing a game. This study aims to detect game addiction in late adolescence by applying Convolutional Neural Network (CNN). Recording of brain waves was carried out three times for each respondent with a stimulus to play three different games, namely games included in the easy, medium, and hard categories with a consecutive taking time of 10 minutes, 15 minutes, and 30 minutes. Data acquisition results are feature extraction using Fast Fourier Transform to get the average signal for each respondent. Based on the research conducted, obtained an accuracy of 86% with a loss of 0.2771 where the smaller the loss value, the better the CNN model built. The test results on the model produce an overall accuracy of 88% with misclassification in 1 data. The CNN model built is good enough for the detection of game addiction in late adolescence. 

scholarly journals Boosting Brain Waves Improves Memory

2021 ◽  
Vol 9 ◽  
Author(s):  
Richard J. Addante ◽  
Mairy Yousif ◽  
Rosemarie Valencia ◽  
Constance Greenwood ◽  
Raechel Marino
Keyword(s):  
Wave Pattern ◽  
Brain Wave ◽  
Brain Waves ◽  
Wave Patterns ◽  
The Brain ◽  
With Memory

Have you ever wanted to improve your memory? Or have you struggled to remember what you studied? Memory uses special patterns of activity in the brain. This experiment tested a new way to create brain wave patterns that help with memory. We wanted to see if we could improve memory by using lights and sounds that teach the brain waves to be in sync. People wore special goggles that made flashes of light and headphones that made beeping noises. This trained the brain through a process called entrainment. The entrainment put the brain in sync at a specific brain wave pattern called theta. People whose brains were trained to be in theta had better memory compared to people whose brains did not get trained. We learned that entrainment is a cool new way to make memory better.

scholarly journals Regions and Connections: Complementary Approaches to Characterize Brain Organization and Function

2019 ◽  
Vol 26 (2) ◽  
pp. 117-133 ◽  
Author(s):  
Corey Horien ◽  
Abigail S. Greene ◽  
R. Todd Constable ◽  
Dustin Scheinost
Keyword(s):  
Functional Organization ◽  
Brain Activity ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Brain Organization ◽  
Neural Processes ◽  
Relative Utility ◽  
And Function ◽  
Brain Behavior ◽  
The Brain

Functional magnetic resonance imaging has proved to be a powerful tool to characterize spatiotemporal patterns of human brain activity. Analysis methods broadly fall into two camps: those summarizing properties of a region and those measuring interactions among regions. Here we pose an unappreciated question in the field: What are the strengths and limitations of each approach to study fundamental neural processes? We explore the relative utility of region- and connection-based measures in the context of three topics of interest: neurobiological relevance, brain-behavior relationships, and individual differences in brain organization. In each section, we offer illustrative examples. We hope that this discussion offers a novel and useful framework to support efforts to better understand the macroscale functional organization of the brain and how it relates to behavior.

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