Conditional Entropy: A Potential Digital Marker for Stress

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

Download Full-text

Classification of Enchepalo Graph (EEG) Signals for Epilepsy Using Discrete Wavelet Transform and K-Nearest Neighbor Methods

Procedia of Engineering and Life Science ◽

10.21070/pels.v1i1.750 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Maulana Angga Pribadi

Keyword(s):

Signal Analysis ◽

Nearest Neighbor ◽

Brain Activity ◽

Discrete Wavelet ◽

K Nearest Neighbor ◽

Eeg Signals ◽

Epilepsy Classification ◽

Non Stationary Signal ◽

The Brain

Epileps is a disorder of the contents of the nervous system of the human brain resulting in the presence of abnormal activity that is the excessive activity of neuron cells in the brain. In Indonesia there are more than 1,400,000 cases of Epilepsy each year with 70,000 additional cases each year. About 4050% occurs in children. A widely used method for assessing brain activity is through a sephalogram (EEG) Electrone signal. The Epilepsy classification system is built with extraction and identifikas stages. Wavelet exctraction is suitable for non-stationary signal analysis such as EEG signals. Wavelet tranformation can extract signal components only at the required frequency. So that it can also reduce the amount of data but without losing meaningful information. But to make it work and can be used on a system needs to be done classification in order to be able to distinguish between commands from each other. So it is used K-Nearest Neighbour (K-NN) classification method so that the signal that has been eliminated buzz can be directly entered into the classification to determine the correct wrongness of a data. In this study obtained the results of data accuracy value that K = 1 has the largest percent of 100% and the smallest percent is found in K = 7 and K = 11 namely 14.2% and 18.2% it is caused by the presence of classes that do not match the test data so as to reduce the percentage of accuracy in the K.

Download Full-text

Wrist and finger motor representations embedded in the cerebral and cerebellar resting-state activation

Brain Structure and Function ◽

10.1007/s00429-021-02330-8 ◽

2021 ◽

Author(s):

Toshiki Kusano ◽

Hiroki Kurashige ◽

Isao Nambu ◽

Yoshiya Moriguchi ◽

Takashi Hanakawa ◽

...

Keyword(s):

Resting State ◽

Brain Activity ◽

Body Part ◽

Resting State Fmri ◽

Body Parts ◽

Finger Movements ◽

Multiple Components ◽

Brain Activity Pattern ◽

Motor Representations ◽

The Brain

AbstractSeveral functional magnetic resonance imaging (fMRI) studies have demonstrated that resting-state brain activity consists of multiple components, each corresponding to the spatial pattern of brain activity induced by performing a task. Especially in a movement task, such components have been shown to correspond to the brain activity pattern of the relevant anatomical region, meaning that the voxels of pattern that are cooperatively activated while using a body part (e.g., foot, hand, and tongue) also behave cooperatively in the resting state. However, it is unclear whether the components involved in resting-state brain activity correspond to those induced by the movement of discrete body parts. To address this issue, in the present study, we focused on wrist and finger movements in the hand, and a cross-decoding technique trained to discriminate between the multi-voxel patterns induced by wrist and finger movement was applied to the resting-state fMRI. We found that the multi-voxel pattern in resting-state brain activity corresponds to either wrist or finger movements in the motor-related areas of each hemisphere of the cerebrum and cerebellum. These results suggest that resting-state brain activity in the motor-related areas consists of the components corresponding to the elementary movements of individual body parts. Therefore, the resting-state brain activity possibly has a finer structure than considered previously.

Download Full-text

Scale-Free Coupled Dynamics in Brain Networks Captured by Bivariate Focus-Based Multifractal Analysis

Frontiers in Physiology ◽

10.3389/fphys.2020.615961 ◽

2021 ◽

Vol 11 ◽

Author(s):

Orestis Stylianou ◽

Frigyes Samuel Racz ◽

Andras Eke ◽

Peter Mukli

Keyword(s):

Resting State ◽

Functional Coupling ◽

Dependent Manner ◽

Coupled Dynamics ◽

Scale Free ◽

Neural Processes ◽

Eeg Recordings ◽

Cortical Regions ◽

The Brain

While most connectivity studies investigate functional connectivity (FC) in a scale-dependent manner, coupled neural processes may also exhibit broadband dynamics, manifesting as power-law scaling of their measures of interdependence. Here we introduce the bivariate focus-based multifractal (BFMF) analysis as a robust tool for capturing such scale-free relations and use resting-state electroencephalography (EEG) recordings of 12 subjects to demonstrate its performance in reconstructing physiological networks. BFMF was employed to characterize broadband FC between 62 cortical regions in a pairwise manner, with all investigated connections being tested for true bivariate multifractality. EEG channels were also grouped to represent the activity of six resting-state networks (RSNs) in the brain, thus allowing for the analysis of within- and between- RSNs connectivity, separately. Most connections featured true bivariate multifractality, which could be attributed to the genuine scale-free coupling of neural dynamics. Bivariate multifractality showed a characteristic topology over the cortex that was highly concordant among subjects. Long-term autocorrelation was higher in within-RSNs, while the degree of multifractality was generally found stronger in between-RSNs connections. These results offer statistical evidence of the bivariate multifractal nature of functional coupling in the brain and validate BFMF as a robust method to capture such scale-independent coupled dynamics.

Download Full-text

Computerized physical and cognitive training improves the functional architecture of the brain in adults with Down syndrome: A network science EEG study

Network Neuroscience ◽

10.1162/netn_a_00177 ◽

2020 ◽

pp. 1-21

Author(s):

Alexandra Anagnostopoulou ◽

Charis Styliadis ◽

Panagiotis Kartsidis ◽

Evangelia Romanopoulou ◽

Vasiliki Zilidou ◽

...

Keyword(s):

Down Syndrome ◽

Functional Connectivity ◽

Cognitive Training ◽

Resting State ◽

Network Architecture ◽

Right Hemisphere ◽

Random Network ◽

Brain Activity ◽

Hierarchical Organization ◽

The Brain

Understanding the neuroplastic capacity of people with Down syndrome (PwDS) can potentially reveal the causal relationship between aberrant brain organization and phenotypic characteristics. We used resting-state EEG recordings to identify how a neuroplasticity-triggering training protocol relates to changes in the functional connectivity of the brain’s intrinsic cortical networks. Brain activity of 12 PwDS before and after a 10-week protocol of combined physical and cognitive training was statistically compared to quantify changes in directed functional connectivity in conjunction with psychosomatometric assessments. PwDS showed increased connectivity within the left hemisphere and from left-to-right hemisphere, as well as increased physical and cognitive performance. Our findings reveal a strong adaptive neuroplastic reorganization as a result of the training that leads to a less-random network with a more pronounced hierarchical organization. Our results go beyond previous findings by indicating a transition to a healthier, more efficient, and flexible network architecture, with improved integration and segregation abilities in the brain of PwDS. Resting-state electrophysiological brain activity is used here for the first time to display meaningful relationships to underlying Down syndrome processes and outcomes of importance in a translational inquiry. This trial is registered with ClinicalTrials.gov Identifier NCT04390321.

Download Full-text

K-NN Classification of Brain Dominance

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v8i4.pp2494-2502 ◽

2018 ◽

Vol 8 (4) ◽

pp. 2494

Author(s):

Khairul Amrizal Abu Nawas ◽

Mahfuzah Mustafa ◽

Rosdiyana Samad ◽

Dwi Pebrianti ◽

Nor Rul Hasma Abdullah

Keyword(s):

Wavelet Analysis ◽

Nearest Neighbor ◽

Gabor Wavelet ◽

Image Feature ◽

Band Pass Filter ◽

K Nearest Neighbor ◽

Brain Dominance ◽

Pass Filter ◽

Band Frequency ◽

The Brain

<span>The brain dominance is referred to right brain and left brain. The brain dominance can be observed with an Electroencephalogram (EEG) signal to identify different types of electrical pattern in the brain and will form the foundation of one’s personality. The objective of this project is to analyze brain dominance by using Wavelet analysis. The Wavelet analysis is done in 2-D Gabor Wavelet and the result of 2-D Gabor Wavelet is validated with an establish brain dominance questionnaire. Twenty-one samples from University Malaysia Pahang (UMP) student are required to answer the establish brain dominance questionnaire has been collected in this experiment. Then, brainwave signal will record using Emotiv device. The threshold value is used to remove the artifact and noise from data collected to acquire a smoother signal. Next, the Band-pass filter is applied to the signal to extract the sub-band frequency components from Delta, Theta, Alpha, and Beta. After that, it will extract the energy of the signal from image feature extraction process. Next the features were classified by using K-Nearest Neighbor (K-NN) in two ratios which 70:30 and 80:20 that are training set and testing set (training: testing). The ratio of 70:30 gave the highest percentage of 83% accuracy while a ratio of 80:20 gave 100% accuracy. The result shows that 2-D Gabor Wavelet was able to classify brain dominance with accuracy 83% to 100%.</span>

Download Full-text

Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

10.1101/2021.02.08.430334 ◽

2021 ◽

Author(s):

Takashi Nakano ◽

Masahiro Takamura ◽

Haruki Nishimura ◽

Maro Machizawa ◽

Naho Ichikawa ◽

...

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Brain Activity ◽

Resting State Fmri ◽

Brain Regions ◽

Posterior Cingulate Cortex ◽

Fmri Data ◽

Independent Test ◽

The Individual ◽

The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

Download Full-text

Resting state and personality component (BIS/BAS) predict the brain activity (EEG and fNIRS measure) in response to emotional cues

Brain and Behavior ◽

10.1002/brb3.686 ◽

2017 ◽

Vol 7 (5) ◽

pp. e00686 ◽

Cited By ~ 9

Author(s):

Michela Balconi ◽

Maria E. Vanutelli ◽

Elisabetta Grippa

Keyword(s):

Resting State ◽

Brain Activity ◽

Emotional Cues ◽

The Brain

Download Full-text

The ecological cocktail party: Measuring brain activity during an auditory oddball task with background noise

10.1101/371435 ◽

2018 ◽

Author(s):

Joanna E. M. Scanlon ◽

Danielle L. Cormier ◽

Kimberley A. Townsend ◽

Jonathan W.P. Kuziek ◽

Kyle E. Mathewson

Keyword(s):

White Noise ◽

New Technologies ◽

Brain Activity ◽

Real Life ◽

Auditory Oddball ◽

Oddball Task ◽

Eeg Recordings ◽

Auditory Oddball Task ◽

The Brain ◽

Sound Condition

AbstractMost experiments using EEG recordings take place in highly isolated and restricted environments, limiting their applicability to real-life scenarios. New technologies for mobile EEG are changing this by allowing EEG recording to take place outside of the laboratory. However, before results from experiments performed outside the laboratory can be fully understood, the effects of ecological stimuli on brain activity during cognitive tasks must be examined. In this experiment, participants performed an auditory oddball task while also listening to concurrent background noises of silence, white noise and outdoor ecological sounds, as well as a condition in which the tones themselves were at a low volume. We found a significantly increased N1 and decreased P2 when participants performed the task with outdoor sounds and white noise in the background, with the largest differences in the outdoor sound condition. This modulation in the N1 and P2 replicates what we have previously found outside while people ride bicycles (Scanlon et al., 2017b). No behavioural differences were found in response to the target tones. We interpret these modulations in early ERPs as indicative of sensory filtering of background sounds, and that ecologically valid sounds require more filtering than synthetic sounds. Our results reveal that much of what we understand about the brain will need to be updated as we step outside the lab.

Download Full-text

Classification of Concentration Levels in Adult-Early Phase using Brainwave Signals by Applying K-Nearest Neighbor

Signal and Image Processing Letters ◽

10.31763/simple.v1i1.170 ◽

2019 ◽

Vol 1 (1) ◽

pp. 14-24

Author(s):

Ahmad Azhari ◽

Fathia Irbati Ammatulloh

Keyword(s):

Early Phase ◽

Nearest Neighbor ◽

Concentration Level ◽

Cognitive Activity ◽

Statistical Characteristics ◽

K Nearest Neighbor ◽

Mental Action ◽

The Brain ◽

Concentration Levels

The brain controls the center of human life. Through the brain, all activities of living can be done. One of them is cognitive activity. Brain performance is influenced by mental conditions, lifestyle, and age. Cognitive activity is an observation of mental action, so it includes psychological symptoms that involve memory in the brain's memory, information processing, and future planning. In this study, the concentration level was measured at the age of the adult-early phase (18-30 years) because in this phase, the brain thinks more abstractly and mental conditions influence it. The purpose of this study was to see the level of concentration in the adult-early phase with a stimulus in the form of cognitive activity using IQ tests with the type of Standard Progressive Matrices (SPM) tests. To find out the IQ test results require a long time, so in this study, a recording was done to get brain waves so that the results of the concentration level can be obtained quickly.EEG data was taken using an Electroencephalogram (EEG) by applying the SPM test as a stimulus. The acquisition takes three times for each respondent, with a total of 10 respondents. The method implemented in this study is a classification with the k-Nearest Neighbor (kNN) algorithm. Before using this method, preprocessing is done first by reducing the signal and filtering the beta signal (13-30 Hz).The results of the data taken will be extracted first to get the right features, feature extraction in this study using first-order statistical characteristics that aim to find out the typical information from the signals obtained. The results of this study are the classification of concentration levels in the categories of high, medium, and low. Finally, the results of this study show an accuracy rate of 70%.

Download Full-text

Altered resting-state network dynamics in Schizophrenia

10.1101/2020.07.21.20157347 ◽

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.

Download Full-text