scholarly journals Análise e reconhecimento de padrões cognitivos em escutas musicais e sonoros em áudios

2020 ◽  
Author(s):  
◽  
E. Ribeiro
Keyword(s):  
Classification Problem ◽  
Cortical Activation ◽  
Auditory Task ◽  
Neural Activation ◽  
Acoustic Features ◽  
Audio Signals ◽  
Eeg Signals ◽  
Activation Patterns ◽  
Similar Cluster ◽  
Cluster Behavior

We are involved in an environment full of sounds around us. Studying and analyzing the impacts that musical practice causes and showing mathematically that this practice provides significant cognitive effects on the human brain are the main motivations of this thesis. In more detail, the aim of this thesis was to develop a methodology capable of characterizing the cortical activation patterns generated during the register of Electroencephalogram (EEG) signals through pattern recognition techniques in statistics, in addition to analyzing the acoustic features commonly employed in this context, in order to reveal whether they are statistically relevant. A computational framework was initially developed to address a 2 group classification problem based on data from EEG signals extracted from volunteer musicians and non-musicians during an auditory task, to predict whether a particular person is a musician or not. The results showed that it is possible to classify the sampled groups with accuracy ranging from 69.2% to 93.8%, allowing not only a better description of the neural activation patterns that characterize the musician and non-musician volunteers, but also highlighting how these patterns they change in the transition regions and decision boundaries that separate the sampled groups, indicating a plausible linear separation between these groups. Additionally, as another original contribution of this thesis, the audio signals from a public and internationally referenced database containing 1000 musical excerpts with 10 different genres were analyzed to investigate numerical similarities between the short-term acoustic features extracted from the audios and commonly explored in related literature. The results obtained show a similar cluster behavior among these features for all analyzed music, regardless of the musical genre. It was then possible to discuss in an unprecedented way the relationship between the way the acoustic features of songs are described in the literature and how they are grouped statistically, revealing that the information we use to cognitively process these sound features is implicitly statistical. Although all the methods described and implemented in this thesis are based on EEG signals, it is believed that they can be extended to other types of multivariate cognitive signals, such as, for example, functional Magnetic Resonance Imaging (fMRI), allowing a greater cortical and sub-cortical understanding of the functioning of our brain during listening

A Whole Brain EEG Analysis of Musicianship

2019 ◽  
Vol 37 (1) ◽  
pp. 42-56
Author(s):  
Estela Ribeiro ◽  
Carlos Eduardo Thomaz
Keyword(s):  
Classical Music ◽  
Brain Activity ◽  
Activity Patterns ◽  
Classification Problem ◽  
Time Instant ◽  
Neural Activation ◽  
Features Selection ◽  
Acoustic Features ◽  
Acoustic Feature ◽  
Whole Brain

The neural activation patterns provoked in response to music listening can reveal whether a subject did or did not receive music training. In the current exploratory study, we have approached this two-group (musicians and nonmusicians) classification problem through a computational framework composed of the following steps: Acoustic features extraction; Acoustic features selection; Trigger selection; EEG signal processing; and Multivariate statistical analysis. We are particularly interested in analyzing the brain data on a global level, considering its activity registered in electroencephalogram (EEG) signals on a given time instant. Our experiment's results—with 26 volunteers (13 musicians and 13 nonmusicians) who listened the classical music Hungarian Dance No. 5 from Johannes Brahms—have shown that is possible to linearly differentiate musicians and nonmusicians with classification accuracies that range from 69.2% (test set) to 93.8% (training set), despite the limited sample sizes available. Additionally, given the whole brain vector navigation method described and implemented here, our results suggest that it is possible to highlight the most expressive and discriminant changes in the participants brain activity patterns depending on the acoustic feature extracted from the audio.

scholarly journals A multivariate statistical analysis of EEG signals for differentiation of musicians and non-musicians

2018 ◽  
Author(s):  
Estela Ribeiro ◽  
Carlos Eduardo Thomaz
Keyword(s):  
Musical Training ◽  
Johannes Brahms ◽  
Music Listening ◽  
Neural Activation ◽  
Acoustic Features ◽  
Multivariate Statistical ◽  
Activation Patterns ◽  
Statistical Framework ◽  
Brain Data ◽  
The Brain

It is possible to reveal whether a subject received musical training through the neural activation patterns induced in response to music listening. We are particularly interested in analyzing the brain data on a global level, considering its activity registered in electroencephalogram electrodes signals. Our experiments results, with 13 musicians and 12 non-musicians who listened the song Hungarian Dance No 5 from Johannes Brahms, have shown that is possible to differentiate musicians and non-musicians with high classification accuracy (88%). Given this multivariate statistical framework, it has also been possible to highlight the most expressive and discriminant changes in the participants brain according to the acoustic features extracted from the audio.

scholarly journals Shared Numerosity Representations Across Formats and Tasks Revealed with 7 Tesla fMRI: Decoding, Generalization, and Individual Differences in Behavior

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Eric D Wilkey ◽  
Benjamin N Conrad ◽  
Darren J Yeo ◽  
Gavin R Price
Keyword(s):  
Individual Differences ◽  
Inferior Frontal Gyrus ◽  
Cortical Activation ◽  
7 Tesla ◽  
Neural Activation ◽  
Math Skills ◽  
Comparison Task ◽  
Activation Patterns ◽  
Parietal Lobes ◽  
Numerical Magnitudes

Abstract Debate continues on whether encoding of symbolic number is grounded in nonsymbolic numerical magnitudes. Nevertheless, fluency of perceiving both number formats, and translating between them, predicts math skills across the life span. Therefore, this study asked if numbers share cortical activation patterns across formats and tasks, and whether neural response to number predicts math-related behaviors. We analyzed patterns of neural activation using 7 Tesla functional magnetic resonance imaging in a sample of 39 healthy adults. Discrimination was successful between numerosities 2, 4, 6, and 8 dots and generalized to activation patterns of the same numerosities represented as Arabic digits in the bilateral parietal lobes and left inferior frontal gyrus (IFG) (and vice versa). This indicates that numerosity-specific neural resources are shared between formats. Generalization was also successful across tasks where participants either identified or compared numerosities in bilateral parietal lobes and IFG. Individual differences in decoding did not relate to performance on a number comparison task completed outside of the scanner, but generalization between formats and across tasks negatively related to math achievement in the parietal lobes. Together, these findings suggest that individual differences in representational specificity within format and task contexts relate to mathematical expertise.

scholarly journals Functional disruption of cortical cingulate activity attenuates visceral hypersensitivity and anxiety induced by acute experimental colitis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lukas Brenner ◽  
Leah Zerlin ◽  
Linette Liqi Tan
Keyword(s):  
Visceral Pain ◽  
Experimental Colitis ◽  
Visceral Hypersensitivity ◽  
Cortical Activation ◽  
Anterior Cingulate ◽  
Pathological Feature ◽  
Activation Patterns ◽  
Bowel Disorders ◽  
Noxious Mechanical Stimulation ◽  
Underlying Mechanisms

AbstractVisceral pain is a highly complex experience and is the most common pathological feature in patients suffering from inflammatory gastrointestinal disorders. Whilst it is increasingly recognized that aberrant neural processing within the gut-brain axis plays a key role in development of neurological symptoms, the underlying mechanisms remain largely unknown. Here, we investigated the cortical activation patterns and effects of non-invasive chemogenetic suppression of cortical activity on visceral hypersensitivity and anxiety-related phenotypes in a well-characterized mouse model of acute colitis induced by dextran sulfate sodium (DSS). We found that within the widespread cortical network, the mid-cingulate cortex (MCC) was consistently highly activated in response to innocuous and noxious mechanical stimulation of the colon. Furthermore, during acute experimental colitis, impairing the activity of the MCC successfully alleviated visceral hypersensitivity, anxiety-like behaviors and visceromotor responses to colorectal distensions (CRDs) via downregulating the excitability of the posterior insula (PI), somatosensory and the rostral anterior cingulate cortices (rACC), but not the prefrontal or anterior insula cortices. These results provide a mechanistic insight into the central cortical circuits underlying painful visceral manifestations and implicate MCC plasticity as a putative target in cingulate-mediated therapies for bowel disorders.

scholarly journals Shared space, separate processes: Neural activation patterns for auditory description and visual object naming in healthy adults

2013 ◽  
Vol 35 (6) ◽  
pp. 2507-2520 ◽  
Author(s):  
Marla J. Hamberger ◽  
Christian G. Habeck ◽  
Spiro P. Pantazatos ◽  
Alicia C. Williams ◽  
Joy Hirsch
Keyword(s):  
Healthy Adults ◽  
Visual Object ◽  
Object Naming ◽  
Neural Activation ◽  
Activation Patterns ◽  
Shared Space ◽  
Visual Object Naming

scholarly journals The Panoramic ECAP Method: estimating patient-specific patterns of current spread and neural health in cochlear implant users

2020 ◽  
Author(s):  
Charlotte Garcia ◽  
Tobias Goehring ◽  
Stefano Cosentino ◽  
Richard E Turner ◽  
John M. Deeks ◽  
...  
Keyword(s):  
Cochlear Implant ◽  
Electrode Array ◽  
Significant Negative Correlation ◽  
Patient Specific ◽  
Clinical Settings ◽  
Neural Activation ◽  
Activation Patterns ◽  
Current Spread ◽  
Compound Action Potentials ◽  
In Cis

The knowledge of patient-specific neural excitation patterns from cochlear implants can provide important information for optimising efficacy and improving speech perception outcomes. The Panoramic ECAP (or ‘PECAP’) method (Cosentino, et al., 2015) uses forward-masked electrically evoked compound action potentials (ECAPs) to estimate neural activation patterns of cochlear implant (CI) stimulation. The algorithm requires ECAPs be measured for loudness-balanced stimuli from all combinations of probe and masker electrodes, and takes advantage of ECAP amplitudes being a result of the overlapping excitatory areas of both probes and maskers. Here we present an improved version of the PECAP algorithm that imposes biologically realistic constraints on the solution and produces separate estimates of current spread and neural health along the length of the electrode array. The algorithm was evaluated for reliability and accuracy in three ways: (1) computer-simulated current-spread and neural-health scenarios, (2) comparisons to psychophysical correlates of neural health and electrode-modiolus distances in human CI users, and (3) detection of simulated neural ‘dead’ regions (using forward masking) in human CI users. The PECAP algorithm reliably estimated the computer simulated scenarios. A moderate but significant negative correlation between focused thresholds and PECAP’s neural health estimates was found, consistent with previous literature. It also correctly identified simulated dead regions in seven CI users. The revised PECAP algorithm provides an estimate of the electrode-to-neuron interface in CIs that could be used to inform and optimize CI stimulation strategies for individual patients in clinical settings.

scholarly journals Decoding the Information Structure Underlying the Neural Representation of Concepts

2021 ◽  
Author(s):  
Leonardo Fernandino ◽  
Lisa L. Conant ◽  
Colin J. Humphries ◽  
Jeffrey R. Binder
Keyword(s):  
Information Structure ◽  
Conceptual Knowledge ◽  
Neural Representation ◽  
Neural Activation ◽  
Activation Patterns ◽  
Concept Representation ◽  
Native Space ◽  
Distributional Information ◽  
Similarity Structure ◽  
Types Of Information

The nature of the neural code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. Three main types of information have been proposed as candidates for the neural representations of lexical concepts: taxonomic (i.e., information about category membership and inter-category relations), distributional (i.e., information about patterns of word co-occurrence in natural language use), and experiential (i.e., information about sensory-motor, affective, and other features of phenomenal experience engaged during concept acquisition). In two experiments, we investigated the extent to which these three types of information are encoded in the neural activation patterns associated with hundreds of English nouns from a wide variety of conceptual categories. Participants made familiarity judgments on the meaning of written nouns while undergoing functional MRI. A high-resolution, whole-brain activation map was generated for each noun in each participant′s native space. These word-specific activation maps were used to evaluate different representational spaces corresponding to the three types of information described above. In both studies, we found a striking advantage for experience-based models in most brain areas previously associated with concept representation. Partial correlation analyses revealed that only experiential information successfully predicted concept similarity structure when inter-model correlations were taken into account. This pattern of results was found independently for object concepts and event concepts. Our findings indicate that the neural representation of conceptual knowledge primarily encodes information about features of experience, and that - to the extent that it is represented in the brain - taxonomic and distributional information may rely on such an experience-based code.

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