scholarly journals COMPLEXITY-BASED EVALUATION OF THE CORRELATION BETWEEN HEART AND BRAIN RESPONSES TO MUSIC

Fractals ◽  
2021 ◽  
pp. 2150238
Author(s):  
TISARA KUMARASINGHE ◽  
ONDREJ KREJCAR ◽  
ALI SELAMAT ◽  
NORAZRYANA MAT DAWI ◽  
ENRIQUE HERRERA-VIEDMA ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Sample Entropy ◽  
Auditory Stimuli ◽  
Strong Correlations ◽  
Pink Noise ◽  
Eeg Signals ◽  
Brain Responses ◽  
Brown Noise ◽  
The Difference ◽  
The Brain

The evaluation of the correlation between the activations of various organs has great importance. This work investigated the synchronization of the brain and heart responses to different auditory stimuli using complexity-based analysis. We selected three pieces of music based on the difference in the complexity of embedded noise (including white noise, brown noise, and pink noise) in them. We played these pieces of music for 11 subjects (7 M and 4 F) and computed the fractal dimension and sample entropy of EEG signals and R–R time series [as heart rate variability (HRV)]. We found strong correlations ([Formula: see text] in the case of fractal dimension and [Formula: see text] in the case of sample entropy) among the complexities of EEG signals and HRV. This finding demonstrates the synchronization of the brain and heart responses and auditory stimuli from the complexity perspective.

scholarly journals EVALUATION OF THE COUPLING BETWEEN ELECTROENCEPHALOGRAM (EEG) AND GALVANIC SKIN RESPONSE (GSR) SIGNALS VERSUS THE COMPLEX STRUCTURE OF MUSIC

Fractals ◽  
2021 ◽  
pp. 2150175
Author(s):  
HAMIDREZA NAMAZI ◽  
SHAFIUL OMAM ◽  
KAMIL KUCA ◽  
ONDREJ KREJCAR
Keyword(s):  
Fractal Dimension ◽  
Complex Structure ◽  
Correlation Coefficients ◽  
Sample Entropy ◽  
Brain Responses ◽  
Skin Response ◽  
Skin Responses ◽  
The Difference ◽  
The Relationship ◽  
The Brain

Since skin activity, like other organs, is controlled by the brain, we decoded the correlation among the brain and skin responses in auditory stimulation by complexity-based analysis of EEG and GSR signals. Three pieces of music were selected according to the difference in the fractal exponent and sample entropy of embedded noises in them. We calculated the fractal dimension and sample entropy of EEG and GSR signals for 11 subjects in rest and response to these music pieces. The correlation coefficients of 0.9525 and 0.9822 in the case of fractal dimension and sample entropy demonstrated a strong correlation between the complexities of the GSR and EEG signals. Therefore, we can state that the skin and brain responses are coupled. This method can be applied to evaluate the relationship between the human brain and other organs.

scholarly journals EVALUATION OF THE CORRELATION BETWEEN FACIAL MUSCLE AND BRAIN ACTIVITIES IN AUDITORY STIMULATION

Fractals ◽  
2021 ◽  
Vol 29 (01) ◽  
pp. 2150100
Author(s):  
MIRRA SOUNDIRARAJAN ◽  
MARTIN AUGUSTYNEK ◽  
ONDREJ KREJCAR ◽  
HAMIDREZA NAMAZI
Keyword(s):  
Human Body ◽  
Strong Correlation ◽  
Rock Music ◽  
Sample Entropy ◽  
Auditory Stimulation ◽  
Auditory Stimuli ◽  
Facial Muscle ◽  
Eeg Signals ◽  
Facial Muscles ◽  
The Brain

Evaluation of the correlation of the activities of various organs is an important area of research in physiology. In this paper, we evaluated the correlation among the brain and facial muscles’ reactions to various auditory stimuli. We played three different music (relaxing, pop, and rock music) to 13 subjects and accordingly analyzed the changes in complexities of EEG and EMG signals by calculating their fractal exponent and sample entropy. Based on the results, EEG and EMG signals experienced more significant changes by presenting relaxing, pop, and rock music, respectively. A strong correlation was observed among the alterations of the complexities of EMG and EEG signals, which indicates the coupling of the activities of facial muscles and brain. This method could be further applied to investigate the coupling of the activities of the brain and other organs of the human body.

scholarly journals ANALYSIS OF THE CORRELATION BETWEEN EYES AND BRAIN ACTIVITIES IN RESPONSE TO MOVING VISUAL STIMULI

Fractals ◽  
2021 ◽  
Author(s):  
RAMESH RAMAMOORTHY ◽  
AVINASH MENON ◽  
KARTHIKEYAN RAJAGOPAL ◽  
ROBERT FRISCHER ◽  
HAMIDREZA NAMAZI
Keyword(s):  
Fractal Dimension ◽  
Eye Movements ◽  
Fractal Theory ◽  
Visual Stimuli ◽  
Sample Entropy ◽  
Eeg Signals ◽  
Complex Patterns ◽  
The Brain

This paper analyzed the coupling among the reactions of eyes and brain in response to visual stimuli. Since eye movements and electroencephalography (EEG) signals as the features of eye and brain activities have complex patterns, we utilized fractal theory and sample entropy to decode the correlation between them. In the experiment, subjects looked at a dot that moved on different random paths (dynamic visual stimuli) on the screen of a computer in front of them while we recorded their EEG signals and eye movements simultaneously. The results indicated that the changes in the complexity of eye movements and EEG signals are coupled ([Formula: see text] in case of fractal dimension and [Formula: see text] in case of sample entropy), which reflects the coupling between the brain and eye activities. This analysis could be extended to evaluate the correlation between the activities of other organs versus the brain.

scholarly journals Complexity-Based Analysis of the Variations of Brain and Muscle Reactions in Walking and Standing Balance While Receiving Different Perturbations

2021 ◽  
Vol 15 ◽  
Author(s):  
Najmeh Pakniyat ◽  
Hamidreza Namazi
Keyword(s):  
Virtual Reality ◽  
Fractal Dimension ◽  
Sample Entropy ◽  
Standing Balance ◽  
Physiological Signals ◽  
Complex Structures ◽  
Eeg Signals ◽  
Muscle Activations ◽  
Electroencephalogram Eeg ◽  
The Brain

In this article, we evaluated the variations of the brain and muscle activations while subjects are exposed to different perturbations to walking and standing balance. Since EEG and EMG signals have complex structures, we utilized the complexity-based analysis. Specifically, we analyzed the fractal dimension and sample entropy of Electroencephalogram (EEG) and Electromyogram (EMG) signals while subjects walked and stood, and received different perturbations in the form of pulling and rotation (via virtual reality). The results showed that the complexity of EEG signals was higher in walking than standing as the result of different perturbations. However, the complexity of EMG signals was higher in standing than walking as the result of different perturbations. Therefore, the alterations in the complexity of EEG and EMG signals are inversely correlated. This analysis could be extended to investigate simultaneous variations of rhythmic patterns of other physiological signals while subjects perform different activities.

Information-Based Analysis of the Coupling Between the Alterations of Heart and Brain Activities in Response to Auditory Stimuli

2021 ◽  
pp. 2150049
Author(s):  
Hamidreza Namazi ◽  
Tisara Kumarasinghe ◽  
Ondrej Krejcar
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Shannon Entropy ◽  
Auditory Stimuli ◽  
Eeg Signals ◽  
Information Contents ◽  
The Difference ◽  
The Brain

In this work, we investigated the coupling among the activities of the brain and heart versus the changes in auditory stimuli using information-based analysis. Three music were selected based on the difference in their complexity. We applied these auditory stimuli on 11 subjects, and accordingly, computed and compared the Shannon entropy of electroencephalography (EEG) signals and heart rate variability (R–R time series). The results demonstrated a correlation among the alterations of the information contents of EEG signals and R–R time series. This finding shows the coupling between the activities of the brain and heart. This analysis could be expanded to analyze the activities of other organs versus the brain’s reaction in various conditions.

scholarly journals Rapid Temporal Recalibration to Audiovisual Asynchrony Occurs Across the Difference in Neural Processing Speed Based on Spatial Frequency

i-Perception ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 204166952096661
Author(s):  
Yasuhiro Takeshima
Keyword(s):  
Spatial Frequency ◽  
Processing Speed ◽  
Audiovisual Integration ◽  
Auditory Stimuli ◽  
Neural Processing ◽  
Temporal Recalibration ◽  
Synchrony Perception ◽  
The Difference ◽  
The One ◽  
The Brain

Audiovisual integration relies on temporal synchrony between visual and auditory stimuli. The brain rapidly adapts to audiovisual asynchronous events by shifting the timing of subjective synchrony in the direction of the leading modality of the most recent event, a process called rapid temporal recalibration. This phenomenon is the flexible function of audiovisual synchrony perception. Previous studies found that neural processing speed based on spatial frequency (SF) affects the timing of subjective synchrony. This study examined the effects of SF on the rapid temporal recalibration process by discriminating whether the presentation of the visual and auditory stimuli was simultaneous. I compared the magnitudes of the recalibration effect between low and high SF visual stimuli using two techniques. First, I randomly presented each SF accompanied by a tone during one session, then in a second experiment, only a single SF was paired with the tone throughout the one session. The results indicated that rapid recalibration occurred regardless of difference in presented SF between preceding and test trials. The recalibration magnitude did not significantly differ between the SF conditions. These findings confirm that intersensory temporal process is important to produce rapid recalibration and suggest that rapid recalibration can be induced by the simultaneity judgment criterion changes attributed to the low-level temporal information of audiovisual events.

scholarly journals Fractal dimension of EEG signal senses complexity of fractal animations

2021 ◽  
Author(s):  
Sarshar Dorosti ◽  
Reza Khosrowabadi
Keyword(s):  
Fractal Dimension ◽  
Information Processing ◽  
Visual Stimuli ◽  
Fractal Dimensions ◽  
Neural Mechanism ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Spatial And Temporal Scales ◽  
Box Counting Method ◽  
The Brain

AbstractWe are surrounded with many fractal and self-similar patterns which has been area of many researches in the recent years. We can perceive self-similarities in various spatial and temporal scales; however, the underlying neural mechanism needs to be well understood. In this study, we hypothesized that complexity of visual stimuli directly influence complexity of information processing in the brain. Therefore, changes in fractal pattern of EEG signal must follow change in fractal dimension of animation. To investigate this hypothesis, we recorded EEG signal of fifteen healthy participants while they were exposed to several 2D fractal animations. Fractal dimension of each frame of the animation was estimated by box counting method. Subsequently, fractal dimensions of 32 EEG channels were estimated in a frequency specific manner. Then, association between pattern of fractal dimensions of the animations and pattern of fractal dimensions of EEG signals were calculated using the Pearson’s correlation algorithm. The results indicated that fractal animation complexity is mainly sensed by changes in fractal dimension of EEG signals at the centro-parietal and parietal regions. It may indicate that when the complexity of visual stimuli increases the mechanism of information processing in the brain also enhances its complexity to better attend and comprehend the stimuli.

scholarly journals Multivariate Multiscale Entropy: An Approach to Estimating Vigilance of Driver

2021 ◽  
Author(s):  
Kawser Ahammed ◽  
Mosabber Uddin Ahmed
Keyword(s):  
Detection System ◽  
Sample Entropy ◽  
Multiscale Entropy ◽  
Support Vector ◽  
Eeg Signals ◽  
Cognitive States ◽  
The Difference ◽  
New Feature ◽  
Multivariate Multiscale Entropy ◽  
Multivariate Sample

Abstract Various driver’s vigilance estimation techniques currently exist in literature. But none of them detects the vigilance of driver in complexity domain. As a result, we have proposed the recently introduced multivariate multiscale entropy (MMSE) method to fill this research gap. In this research, we have applied the MMSE technique to differential entropy features of electroencephalogram (EEG) and electrooculogram (EOG) signals for detecting vigilance of driver in complexity domain. The MMSE has also been employed to PERCLOS (Percentage of Eye Closure) values to analyse cognitive states (awake, tired and drowsy) in complexity domain. The contribution of this research is to show how a new feature called MMSE can efficiently classify the awake, tired and drowsy state of the driver in complexity domain. Another contribution is to demonstrate the distinguishing ability of the MMSE by validating it with applying multivariate sample entropy feature of cognitive states to support vector machine (SVM). The experimental MMSE analysis curves show statistically significant differences (p < 0.01) in terms of complexity among brain EEG signals, forehead EEG signals and EOG signals. Moreover, the difference in the multivariate sample entropy across all scales in awake (1.0828 ± 0.4664), tired (0.7841 ± 0.3183) and drowsy (0.2938 ± 0.1664) states are statistically significant (p <0.01). Also, the SVM, a machine learning technique, has discriminated the cognitive states with the promising classification accuracy of 76.2%. As a result, the MMSE analysis of cognitive states can be implemented practically for vigilance detection by building a programmable vigilance detection system.

scholarly journals The brain tunes to unfamiliar voices during sleeping

2021 ◽  
Author(s):  
Mohamed Ameen ◽  
Dominik Philipp Johannes Heib ◽  
Christine Blume ◽  
Manuel Schabus
Keyword(s):  
Phase Synchronization ◽  
Sensory Information ◽  
Event Related Potentials ◽  
Nrem Sleep ◽  
Auditory Stimuli ◽  
Brain Responses ◽  
Oscillatory Power ◽  
Related Potentials ◽  
The Many ◽  
The Brain

The brain continues to respond selectively to environmental stimuli even during sleep. However, the functional role of such responses, and whether they reflect information processing or rather sensory inhibition is not fully understood. Here, we presented 17 human sleepers (14 females) with their own name and two unfamiliar first names, spoken by either a familiar voice (FV) or an unfamiliar voice (UFV), while recording polysomnography during a full night of sleep. We detected K-complexes, sleep spindles, and micro-arousals, and then assessed event-related potentials, oscillatory power as well as inter-trial phase synchronization in response to the different stimuli presented during non-rapid eye movement (NREM) sleep. We show that UFVs evoke more K-complexes and micro-arousals than FVs. When both stimuli evoke a K-complex, we observed larger evoked potentials, higher oscillatory power in the high beta (>16Hz) frequency range, and stronger time-locking in the delta band (1-4 Hz) in response to UFVs relative to FVs. Crucially, these differences in brain responses disappear when no K-complexes are evoked by the auditory stimuli. Our findings highlight discrepancies in brain responses to auditory stimuli based on their relevance to the sleeper and propose a key role for K-complexes in the modulation of sensory processing during sleep. We argue that such content-specific, dynamic reactivity to external sensory information enables the brain to enter a sentinel processing mode in which it engages in the many important processes that are ongoing during sleep while still maintaining the ability to process vital information in the surrounding.

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