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.

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 Signal complexity indicators of health status in clinical EEG

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kelly Shen ◽  
Alison McFadden ◽  
Anthony R. McIntosh
Keyword(s):  
Health Status ◽  
Neurological Disorders ◽  
Spectral Power ◽  
Spectral Power Density ◽  
Multiscale Entropy ◽  
Brain Signals ◽  
Clinical Biomarker ◽  
Patient Groups ◽  
Signal Complexity ◽  
Clinical Eeg

AbstractBrain signal variability changes across the lifespan in both health and disease, likely reflecting changes in information processing capacity related to development, aging and neurological disorders. While signal complexity, and multiscale entropy (MSE) in particular, has been proposed as a biomarker for neurological disorders, most observations of altered signal complexity have come from studies comparing patients with few to no comorbidities against healthy controls. In this study, we examined whether MSE of brain signals was distinguishable across patient groups in a large and heterogeneous set of clinical-EEG data. Using a multivariate analysis, we found unique timescale-dependent differences in MSE across various neurological disorders. We also found MSE to differentiate individuals with non-brain comorbidities, suggesting that MSE is sensitive to brain signal changes brought about by metabolic and other non-brain disorders. Such changes were not detectable in the spectral power density of brain signals. Our findings suggest that brain signal complexity may offer complementary information to spectral power about an individual’s health status and is a promising avenue for clinical biomarker development.

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 Signal complexity indicators of health status in clinical-EEG

2021 ◽  
Author(s):  
Kelly Shen ◽  
Alison McFadden ◽  
Anthony R. McIntosh
Keyword(s):  
Health Status ◽  
Neurological Disorders ◽  
Spectral Power ◽  
Spectral Power Density ◽  
Multiscale Entropy ◽  
Brain Signals ◽  
Clinical Biomarker ◽  
Health And Disease ◽  
Signal Complexity ◽  
Clinical Eeg

Brain signal variability changes across the lifespan in both health and disease, likely reflecting changes in information processing capacity related to development, aging and neurological disorders. While signal complexity, and multiscale entropy (MSE) in particular, has been proposed as a biomarker for neurological disorders, most observations of altered signal complexity have come from studies comparing patients with few to no comorbidities against healthy controls. In this study, we examined whether MSE of brain signals was distinguishable across individuals in a large and heterogeneous set of clinical-EEG data. Using a multivariate analysis, we found unique timescale-dependent differences in MSE across various neurological disorders. We also found MSE to differentiate individuals with non-brain comorbidities, suggesting that MSE is sensitive to brain signal changes brought about by metabolic and other non-brain disorders. Such changes were not detectable in the spectral power density of brain signals. Our findings suggest that brain signal complexity may offer complementary information to spectral power about an individual's health status and is a promising avenue for clinical biomarker development.

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.

Differentiation of Healthy Individuals from Those with Autism Spectrum Disorders using Information Graph of Complementary Opposites

2020 ◽  
Keyword(s):  
Children With Autism ◽  
Autism Spectrum ◽  
Brain Signals ◽  
Three Stages ◽  
The Mean ◽  
Spectrum Disorders ◽  
Pediatric Psychiatry ◽  
The Brain ◽  
Optimal Feature ◽  
Information Graph

This study aimed to examine the brain signals of children with Autism Spectrum Disorder (ASD) and use a method according to the concept of complementary opposites to obtain the prominent features or a pattern of EEG signal that represents the biological characteristic of such children. In this study, 20 children with the mean±SD age of 8±5 years were divided into two groups of normal control (NC) and ASD. The diagnosis and approval of individuals in both groups were conducted by two experts in the field of pediatric psychiatry and neurology. The recording protocol was designed with the most accuracy; therefore, the brain signals were recorded with the least noise in the awake state of the individuals in both groups. Moreover, the recording was conducted in three stages from two channels (C3-C4) of EEG ( referred to as the central part of the brain) which were symmetrical in function. In this study, the Mandala method was adopted based on the concept of complementary opposites to investigate the features extracted from Mandala pattern topology and obtain new features and pseudo-patterns for the screening and early diagnosis of ASD. The optimal feature here was based on different stages of processing and statistical analysis of Pattern Detection Capability (PDC). The PDC is a biomarker derived from the Mandala pattern for differentiating the NC from ASD groups.

Human Mind and Its Carriers: Is It Possible to Transform Ourselves into Robots?

2021 ◽  
pp. 105-117
Author(s):  
Vadim V. Vasilyev ◽  
Keyword(s):  
Personal Identity ◽  
Human Person ◽  
Human Mind ◽  
John Searle ◽  
David Chalmers ◽  
Main Emphasis ◽  
Theories Of Mind ◽  
Continuous Stream ◽  
Previous State ◽  
The Brain

In this paper I discuss some aspects of the problem of carriers of human mind and person. The main emphasis is placed on the origin of our idea of the identi­cal self in the stream of perceptions, the need for a physical carrier of our self and person, and on possibility of replacing the biological carriers of self and per­son with artificial analogues. I argue that the idea of identical self is constructed by reflection on memories, that its truth is guaranteed by continuous stream of perceptions kept in memories, and that the stream of perceptions presupposes the presence of a normally functioning brain, which can be considered as a car­rier of our mind and person. Therefore, personal identity turns out to be depen­dent on the identity of the brain in time. An attempt to copy the structures of mind and person onto other possible carriers can thus only lead to creation of duplicates of the original person, but not to the continuation of its existence on another carrier. I argue that the gradual replacement of their components with artificial analogues is a more promising way of transforming the biological carri­ers of human person. To access the possible consequences of such a replacement I analyze arguments of John Searle and David Chalmers, designed to show, re­spectively, the disappearance of consciousness and person with such a replace­ment and, on the contrary, their preservation in a previous state. I explain why Searle’s arguments are unconvincing, and demonstrate that Chalmers’ arguments are based on a hidden premise, the confirmation of which is possible in the con­text of dubious theories of mind-body identity, epiphenomenalism or panpsy­chism only. I conclude that in the current situation it is impossible to predict which consequences for our person would follow such a replacement.

scholarly journals Neurophysiological Correlates of Comprehending Emotional Meaning in Context

2009 ◽  
Vol 21 (11) ◽  
pp. 2245-2262 ◽  
Author(s):  
Daphne J. Holt ◽  
Spencer K. Lynn ◽  
Gina R. Kuperberg
Keyword(s):  
Emotional Processing ◽  
Semantic Analysis ◽  
Emotional Valence ◽  
N400 Effect ◽  
Late Positivity ◽  
Electrophysiological Responses ◽  
Emotional Meaning ◽  
Reading Experiment ◽  
The Brain ◽  
Modulated Process

Although the neurocognitive mechanisms of nonaffective language comprehension have been studied extensively, relatively less is known about how the emotional meaning of language is processed. In this study, electrophysiological responses to affectively positive, negative, and neutral words, presented within nonconstraining, neutral contexts, were evaluated under conditions of explicit evaluation of emotional content (Experiment 1) and passive reading (Experiment 2). In both experiments, a widely distributed Late Positivity was found to be larger to negative than to positive words (a “negativity bias”). In addition, in Experiment 2, a small, posterior N400 effect to negative and positive (relative to neutral) words was detected, with no differences found between N400 magnitudes to negative and positive words. Taken together, these results suggest that comprehending the emotional meaning of words following a neutral context requires an initial semantic analysis that is relatively more engaged for emotional than for nonemotional words, whereas a later, more extended, attention-modulated process distinguishes the specific emotional valence (positive vs. negative) of words. Thus, emotional processing networks within the brain appear to exert a continuous influence, evident at several stages, on the construction of the emotional meaning of language.

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