scholarly journals BRAIN-BASED AUTHENTICATION: TOWARDS A SCALABLE, COMMERCIAL GRADE SOLUTION USING NONINVASIVE BRAIN SIGNALS.

2021 ◽  
Author(s):  
Ronen Kopito ◽  
Aia Haruvi ◽  
Noa Brande-Eilat ◽  
Shai Kalev ◽  
Eitan Kay ◽  
...  
Keyword(s):  
The United States ◽  
Biometric Authentication ◽  
Ideal Candidate ◽  
Commercial Grade ◽  
Brain Signals ◽  
Authentication System ◽  
Signal Complexity ◽  
Grade Performance ◽  
The Brain ◽  
Rapid Presentation

In this study we report on a field test where we asked if it is feasible to deliver a scalable, commercial-grade solution for brain-based authentication currently given available head wearables. Sixty-two (62) participants living across the United States in autumn 2020 completed four (4) at-home sessions over a single (1) week. In each session there were six (6) authentication events consisting of rapid presentation of images (10Hz) that participants watched for 10 seconds while recording their brain signal with an off-the-shelf brain signal measuring headband. The non-stationary nature of the brain signal, and the fact that the signal results from a superposition of hundreds of simultaneous processes in the brain that respond to context makes the data unique in time, unrepeatable, and unpredictable. Even when a participant watched identical stimuli, we find no two periods of time to be alike (Fig. 4B) and furthermore, no two combinations of time periods are alike. Differences within people (intra-) and across people (inter- participant) from session to session were found to be significant, however stable processes do appear to be underlying the signal complexity and non-stationarity. We show a simplified brain-based authentication system that captures distinguishable information with reliable, commercial-grade performance from participants at their own homes. We conclude that noninvasively measured brain signals are an ideal candidate for biometric authentication, especially for head wearables such as headphones and AR/VR devices.

scholarly journals Performance Analysis Between Feature Extraction and Fusion in Familiar and Unfamiliar Typing Biometric Authentication

2021 ◽  
Vol 2071 (1) ◽  
pp. 012041
Author(s):  
I Amalina ◽  
A Saidatul ◽  
C Y Fook ◽  
R F Navea
Keyword(s):  
Feature Extraction ◽  
Feature Fusion ◽  
Biometric Authentication ◽  
Feature Extraction Method ◽  
Parietal Lobes ◽  
Brain Signals ◽  
Performance Accuracy ◽  
The Mean ◽  
Nonlinear Feature ◽  
The Brain

Abstract The brain signals recorded by EEG devices are largely developed in for biometric authentication purposes. Those signals are very informative and reliable to be classified using signal processing. In this paper, the feature extraction and feature fusion are further studied to observe their performance towards the typing tasks. The signals are pre-processed to eliminate the unwanted noise present in the signals. The feature extraction method such as Welch’s method, Burg’s method and Yule Walk’s method are applied to extract the mean, median, standard deviation and variance in the data. Nonlinear feature such as fuzzy entropy is also been extracted. The extracted features are further classified by using k-Nearest Neighbour (k-NN), Random Forest (RF) and Ensemble Bagged Tree (EBT). The performance of feature extraction and feature fusion through concatenation are recorded and compared. For comparison, the feature fusion shows a better performance accuracy rather than feature extraction. The highest percentage accuracy was produced by Burg’s method for frontal-parietal lobes feature fusion which is 95.94% using Ensemble Bagged Tree (EBT).

scholarly journals Complexity matching: brain signals mirror environment information patterns during music listening and reward

2019 ◽  
Author(s):  
Sarah M. Carpentier ◽  
Andrea R. McCulloch ◽  
Tanya M. Brown ◽  
Petra Ritter ◽  
Zhang Wang ◽  
...  
Keyword(s):  
Emotional Experience ◽  
Essential Element ◽  
Human Mind ◽  
Multiscale Entropy ◽  
Complex Signal ◽  
Perceptual Task ◽  
Additional Information ◽  
Brain Signals ◽  
Signal Complexity ◽  
The Brain

AbstractUnderstanding how the human brain integrates information from the environment with ongoing, internal brain signals in order to produce individual perspective is an essential element of understanding the human mind. Brain signal complexity, measured with multiscale entropy, has been employed as a measure of information processing in the brain (Carpentier et al., 2016), and we propose that it can also be used to measure the information available from a stimulus. We can directly assess the correspondence, or functional isomorphism, between brain signal complexity and stimulus complexity as an indication of how well the brain reflects the content of the environment in an analysis that we termed complexity matching. Music makes an ideal stimulus input because it is a multidimensional, complex signal, and because of its emotion and reward-inducing potential. We found that electroencephalography (EEG) complexity was lower and more closely resembled the musical complexity when participants performed a perceptual task that required them to closely track the acoustics, compared to an emotional task that asked them to think about how the music made them feel. Music-derived reward scores on the Barcelona Music Reward Questionnaire (Mas-Herrero et al., 2013) correlated with worse complexity matching and higher EEG complexity. Compared to perceptual-level processing, emotional and reward responses are associated with additional internal information processes above and beyond those in the external stimulus.Significance StatementExperience of our world is combination of the input from the environment, our expectations, and individual responses. For example, the same piece of music can elict happiness in one person and sadness in another. We researched this by measuring the information in pieces of music and whether listener’s brain more closely followed that, or whether additional information was added by the brain. We noted when listener’s were reacting to how music made them feel, their brains added more information and the degree to which this occurred related to how much they find music rewarding. Thus, we were able to provide clues as to how the brain integrates incoming information, adding to it to provide a richer perceptual and emotional experience.

scholarly journals Complexity Matching: Brain Signals Mirror Environment Information Patterns during Music Listening and Reward

2020 ◽  
Vol 32 (4) ◽  
pp. 734-745 ◽  
Author(s):  
Sarah M. Carpentier ◽  
Andrea R. McCulloch ◽  
Tanya M. Brown ◽  
Sarah E. M. Faber ◽  
Petra Ritter ◽  
...  
Keyword(s):  
Essential Element ◽  
Emotional Valence ◽  
Human Mind ◽  
Multiscale Entropy ◽  
Music Listening ◽  
Brain Signals ◽  
Multidimensional Signal ◽  
Information Patterns ◽  
Signal Complexity ◽  
The Brain

Understanding how the human brain integrates information from the environment with intrinsic brain signals to produce individual perspectives is an essential element of understanding the human mind. Brain signal complexity, measured with multiscale entropy, has been employed as a measure of information processing in the brain, and we propose that it can also be used to measure the information available from a stimulus. We can directly assess the correspondence between brain signal complexity and stimulus complexity as an indication of how well the brain reflects the content of the environment in an analysis that we term “complexity matching.” Music is an ideal stimulus because it is a multidimensional signal with a rich temporal evolution and because of its emotion- and reward-inducing potential. When participants focused on acoustic features of music, we found that EEG complexity was lower and more closely resembled the musical complexity compared to an emotional task that asked them to monitor how the music made them feel. Music-derived reward scores on the Barcelona Music Reward Questionnaire correlated with less complexity matching but higher EEG complexity. Compared with perceptual-level processing, emotional and reward responses are associated with additional internal information processes above and beyond those linked to the external stimulus. In other words, the brain adds something when judging the emotional valence of music.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

Biometric authentication system using EEG biometric trait – A review

2021 ◽  
Author(s):  
Fatin Atiqah Rosli ◽  
Saidatul Ardeenawatie Awang ◽  
Azian Azamimi Abdullah ◽  
Mohammad Shahril Salim
Keyword(s):  
Biometric Authentication ◽  
Authentication System ◽  
Biometric Trait

Psychiatry’s “Chemical Imbalance” Fraud: Who Killed Rebecca Riley?

2008 ◽  
Vol 10 (2) ◽  
pp. 96-108 ◽  
Author(s):  
Fred A. Baughman
Keyword(s):  
The United States ◽  
Diagnosis And Treatment ◽  
Normal Person ◽  
Organic Disease ◽  
Health Care Fraud ◽  
Department Of Health ◽  
Congressional Hearing ◽  
The Difference ◽  
Chemical Imbalance ◽  
The Brain

All physicians attend medical school and learn of (a) all things physically normal; anatomy, physiology, and chemistry, (b) all things physically abnormal; pathology, disease, and (c) how to tell the difference. Diagnosis is the first obligation of every physician to every patient, and must precede treatment. Diagnosis first asks, “Is there a physical abnormality (physical abnormality = disorder = disease), yes or no?” Patients with no abnormality (no physical abnormality = no disorder = no disease = normal) are referred to as having “no evidence or disease” (NED) or “no organic disease” (NOD). Their problems may be psychological or psychiatric, but they are not medical or surgical. In patients found to have an abnormality, diagnosis now asks, “Which disease?” Psychiatrists are the only physicians who do not perform physical diagnosis. The absence of disease is determined for them by other physicians, usually referring physicians. In 1948 the previously conjoint specialty of neuropsychiatry was divided into neurology—responsible for the diagnosis and treatment or physical/organic disease of the nervous system—and psychiatry—responsible for the treatment of emotional and psychological problems, none of them due to organic diseases. Nor did psychiatry object to this scientific division of labor at the time. However, in the 1950s, with the advent of psychotropic drugs, psychiatry, increasingly in league with the pharmaceutical industry, began referring to psychological diagnoses as disorders/diseases/chemical imbalances of the brain, albeit with no proof or science. In a congressional hearing in 1970, psychiatrists and federal officials, including the Food and Drug Administration and the Department of Health, Education, and Welfare, represented hyperkinetic disorder (HKD) to be a disorder/disease of the brain leading to the appropriation of millions of dollars for research, diagnosis and treatment into the drug treatment of school children said to have the new disease HKD. HKD became ADD, then ADHD, a disorder/disease/chemical imbalance always in need of a “chemical balancer”—a pill. Without proof of an abnormality/disorder/disease, the ADHD epidemic grew from 150,000 in 1970 to 6 million to 7 million today, the most common childhood diagnosis in the United States, a multi-billion dollar industry, and a model for all 374 DSM–IV psychological/psychiatric diagnoses—none of them actual diseases. As such, psychiatry is not a legitimate branch of medicine deserving scientific-fiscal parity; rather, collectively, it is the greatest health care fraud in history. Every time a so-called chemical imbalance is diagnosed, a patient’s right to informed consent has been abrogated. Every time a medically normal person is treated with a psychotropic chemical balancer—a pill—their first and only abnormality is the iatrogenic intoxication: poisoning.

Depression and Inflammatory Markers in Veterans With Multiple Sclerosis

2021 ◽  
pp. 109980042110500
Author(s):  
Pamela Newland ◽  
Yelyzaveta Basan ◽  
Ling Chen ◽  
Gregory Wu
Keyword(s):  
Multiple Sclerosis ◽  
Inflammatory Markers ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
The United States ◽  
Biological Mechanisms ◽  
The Central Nervous System ◽  
Brain And Spinal Cord ◽  
The Brain

Multiple sclerosis (MS), an inflammatory neurodegenerative disease of the central nervous system (CNS), afflicts over one per thousand people in the United States. The pathology of MS typically involves lesions in several regions, including the brain and spinal cord. The manifestation of MS is variable and carries great potential to negatively impact quality of life (QOL). Evidence that inflammatory markers are related to depression in MS is accumulating. However, there are barriers in precisely identifying the biological mechanisms underlying depression and inflammation. Analysis of cytokines provides one promising approach for understanding the mechanisms that may contribute to MS symptoms. Methods: In this pilot study, we measured salivary levels of interleukin (IL)-6, IL-1beta (β), and IL-10 in 24 veterans with MS. Descriptive statistics were reported and Pearson correlation coefficients were obtained between cytokines and depression. Results: The anti-inflammatory cytokine IL-10 was significantly negatively associated with depression in veterans with MS (r = −0.47, p = .024). Conclusion: Cytokines may be useful for elucidating biological mechanisms associated with the depression and a measure for nurses caring for veterans with MS.

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