scholarly journals Processing of Affective Pictures: A Study Based on Functional Connectivity Network in the Cerebral Cortex

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhongyang He ◽  
Kai Yang ◽  
Ning Zhuang ◽  
Ying Zeng
Keyword(s):  
Cerebral Cortex ◽  
Brain Function ◽  
Middle Temporal Gyrus ◽  
Emotional States ◽  
Eeg Signals ◽  
Brain Areas ◽  
Middle Temporal ◽  
Network Connections ◽  
The Difference ◽  
Function Network

Emotion plays an important role in people’s life. However, the existing researches do not give a unified conclusion on the brain function network under different emotional states. In this study, pictures from the international affective picture system (IAPS) of different valences were presented to subjects with a fixed frequency blinking frequency to induce stable state visual evoked potential (SSVEP). With the source location method, the cerebral cortex source signal was reconstructed based on EEG signals, and then the difference in SSVEP amplitudes in key brain areas under different emotional states and the difference in brain function network connections among different brain areas were analysed in cortical space. The results of the study show that positive and negative emotions evoked greater activation intensities in the prefrontal, temporal, and parietal lobes compared with those of neutral emotion. The network connections with a significant difference between emotional states mainly appear in the alpha and gamma bands, and the network connections with significant differences between positive emotion and negative emotion mainly exist in the right middle temporal gyrus and the superior frontal gyrus on both sides. In addition, the long-range connections play an important role in the process of emotional processing, especially the connections among frontal gyrus, middle temporal gyrus, and middle occipital gyrus. The results of this study provide a reliable scientific basis for revealing and elucidating the neural mechanism of emotion processing and the selection of brain regions and frequency bands in emotion recognition based on EEG signals.

Electroencephalagram Analysis in the State of Attention

2013 ◽  
Vol 321-324 ◽  
pp. 716-719
Author(s):  
Jun Chang Zhao ◽  
Zheng Zhong Zheng ◽  
Xiao Lin Huang ◽  
Jun Wang
Keyword(s):  
Correlation Analysis ◽  
Working Conditions ◽  
Brain Function ◽  
Cross Correlation ◽  
Brain State ◽  
Eeg Signals ◽  
Number State ◽  
Cross Correlation Analysis ◽  
The Difference ◽  
First Time

Assessment the distinction of different brain working conditions is very important for brain function study. For the first time, detrended cross-correlation analysis (DCCA) was applied to analyze different brain working conditions. It were compared the difference of DCCA values for EEG signals under count number state and close eyes state. It was found that the DCCA values of count number state EEG signals decreased compared with close eyes state EEG signals which can be helpful for studying different brain state.

scholarly journals Lexical Influences on Categorical Speech Perception Are Driven by a Temporoparietal Circuit

2021 ◽  
pp. 1-13
Author(s):  
Gavin M. Bidelman ◽  
Claire Pearson ◽  
Ashleigh Harrison
Keyword(s):  
Parietal Lobe ◽  
Middle Temporal Gyrus ◽  
Brain Areas ◽  
Lexical Status ◽  
Inferior Parietal Lobe ◽  
Middle Temporal ◽  
Phonetic Perception ◽  
Speech Categorization ◽  
Left Middle Temporal Gyrus ◽  
Left Middle

Categorical judgments of otherwise identical phonemes are biased toward hearing words (i.e., “Ganong effect”) suggesting lexical context influences perception of even basic speech primitives. Lexical biasing could manifest via late stage postperceptual mechanisms related to decision or, alternatively, top–down linguistic inference that acts on early perceptual coding. Here, we exploited the temporal sensitivity of EEG to resolve the spatiotemporal dynamics of these context-related influences on speech categorization. Listeners rapidly classified sounds from a /gɪ/-/kɪ/ gradient presented in opposing word–nonword contexts ( GIFT–kift vs. giss–KISS), designed to bias perception toward lexical items. Phonetic perception shifted toward the direction of words, establishing a robust Ganong effect behaviorally. ERPs revealed a neural analog of lexical biasing emerging within ~200 msec. Source analyses uncovered a distributed neural network supporting the Ganong including middle temporal gyrus, inferior parietal lobe, and middle frontal cortex. Yet, among Ganong-sensitive regions, only left middle temporal gyrus and inferior parietal lobe predicted behavioral susceptibility to lexical influence. Our findings confirm lexical status rapidly constrains sublexical categorical representations for speech within several hundred milliseconds but likely does so outside the purview of canonical auditory-sensory brain areas.

scholarly journals Theta-band Oscillations in the Middle Temporal Gyrus Reflect Novel Word Consolidation

2018 ◽  
Vol 30 (5) ◽  
pp. 621-633 ◽  
Author(s):  
Iske Bakker-Marshall ◽  
Atsuko Takashima ◽  
Jan-Mathijs Schoffelen ◽  
Janet G. van Hell ◽  
Gabriele Janzen ◽  
...  
Keyword(s):  
Brain Activity ◽  
Functional Integration ◽  
Middle Temporal Gyrus ◽  
Theta Band ◽  
Middle Temporal ◽  
Lexical Representations ◽  
Simultaneous Activation ◽  
The Difference ◽  
Novel Word Learning ◽  
Novel Words

Like many other types of memory formation, novel word learning benefits from an offline consolidation period after the initial encoding phase. A previous EEG study has shown that retrieval of novel words elicited more word-like-induced electrophysiological brain activity in the theta band after consolidation [Bakker, I., Takashima, A., van Hell, J. G., Janzen, G., & McQueen, J. M. Changes in theta and beta oscillations as signatures of novel word consolidation. Journal of Cognitive Neuroscience, 27, 1286–1297, 2015]. This suggests that theta-band oscillations play a role in lexicalization, but it has not been demonstrated that this effect is directly caused by the formation of lexical representations. This study used magnetoencephalography to localize the theta consolidation effect to the left posterior middle temporal gyrus (pMTG), a region known to be involved in lexical storage. Both untrained novel words and words learned immediately before test elicited lower theta power during retrieval than existing words in this region. After a 24-hr consolidation period, the difference between novel and existing words decreased significantly, most strongly in the left pMTG. The magnitude of the decrease after consolidation correlated with an increase in behavioral competition effects between novel words and existing words with similar spelling, reflecting functional integration into the mental lexicon. These results thus provide new evidence that consolidation aids the development of lexical representations mediated by the left pMTG. Theta synchronization may enable lexical access by facilitating the simultaneous activation of distributed semantic, phonological, and orthographic representations that are bound together in the pMTG.

scholarly journals III. A comparison of the latency periods of the ocular muscles on excitation of the frontal and occipito-temporal regions of the brain

1888 ◽  
Vol 43 (258-265) ◽  
pp. 411-412 ◽  
Keyword(s):  
Cerebral Cortex ◽  
Occipital Lobe ◽  
Superior Temporal Gyrus ◽  
Frontal Region ◽  
Angular Gyrus ◽  
Middle Temporal Gyrus ◽  
Middle Temporal ◽  
Posterior Limb ◽  
The Brain

Conjugate deviation of the eyes to the opposite side is produced by excitation of entirely different regions of the cerebral cortex. The parts which when electrically excited produce this movement are: (1) An area in the frontal region of the hemisphere which is included in the motor or psychomotor zone of authors; (2) the superior temporal gyrus; (3) the upper end of the middle temporal gyrus; (4) the posterior limb of the angular gyrus; (5) the whole cortex of the occipital lobe including its mesial and under surfaces; (6) the quadrate lobule.

scholarly journals Lexical influences on categorical speech perception are driven by a temporoparietal circuit

2020 ◽  
Author(s):  
Gavin M. Bidelman ◽  
Claire Pearson ◽  
Ashleigh Harrison
Keyword(s):  
Parietal Lobe ◽  
Middle Temporal Gyrus ◽  
Top Down ◽  
Perceptual Coding ◽  
Brain Areas ◽  
Lexical Status ◽  
Inferior Parietal Lobe ◽  
Middle Temporal ◽  
Phonetic Perception ◽  
Speech Categorization

AbstractCategorical judgments of otherwise identical phonemes are biased toward hearing words (i.e., “Ganong effect”) suggesting lexical context influences perception of even basic speech primitives. Lexical biasing could manifest via late stage post-perceptual mechanisms related to decision or alternatively, top-down linguistic inference which acts on early perceptual coding. Here, we exploited the temporal sensitivity of EEG to resolve the spatiotemporal dynamics of these context-related influences on speech categorization. Listeners rapidly classified sounds from a /gi/ - /ki/ gradient presented in opposing word-nonword contexts (GIFT-kift vs. giss-KISS), designed to bias perception toward lexical items. Phonetic perception shifted toward the direction of words, establishing a robust Ganong effect behaviorally. ERPs revealed a neural analog of lexical biasing emerging within ∼200 ms. Source analyses uncovered a distributed neural network supporting the Ganong including middle temporal gyrus (MTG), inferior parietal lobe (IPL), and middle frontal cortex. Yet, among Ganong-sensitive regions, only left MTG and IPL predicted behavioral susceptibility to lexical influence. Our findings confirm lexical status rapidly constrains sub-lexical categorical representations for speech within several hundred milliseconds but likely does so outside the purview of canonical “auditory-linguistic” brain areas.

scholarly journals Identification of Breathing Patterns through EEG Signal Analysis Using Machine Learning

2021 ◽  
Vol 11 (3) ◽  
pp. 293
Author(s):  
Yong-Gi Hong ◽  
Hang-Keun Kim ◽  
Young-Don Son ◽  
Chang-Ki Kang
Keyword(s):  
Machine Learning ◽  
Side Effects ◽  
Brain Function ◽  
Memory Task ◽  
Mouth Breathing ◽  
Breathing Patterns ◽  
Eeg Signals ◽  
The Difference ◽  
O2 Supply ◽  
The Brain

This study was to investigate the changes in brain function due to lack of oxygen (O2) caused by mouth breathing, and to suggest a method to alleviate the side effects of mouth breathing on brain function through an additional O2 supply. For this purpose, we classified the breathing patterns according to EEG signals using a machine learning technique and proposed a method to reduce the side effects of mouth breathing on brain function. Twenty subjects participated in this study, and each subject performed three different breathings: nose and mouth breathing and mouth breathing with O2 supply during a working memory task. The results showed that nose breathing guarantees normal O2 supply to the brain, but mouth breathing interrupts the O2 supply to the brain. Therefore, this comparative study of EEG signals using machine learning showed that one of the most important elements distinguishing the effects of mouth and nose breathing on brain function was the difference in O2 supply. These findings have important implications for the workplace environment, suggesting that special care is required for employees who work long hours in confined spaces such as public transport, and that a sufficient O2 supply is needed in the workplace for working efficiency.

Multiscale Mutual Mode Entropy Based Coupling Analysis of Alpha Rhythm Electroencephalogram

2014 ◽  
Vol 574 ◽  
pp. 718-722
Author(s):  
Ning Ji ◽  
Jun Tan ◽  
An Shan Pei ◽  
Jia Fei Dai ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Clinical Evaluation ◽  
Brain Function ◽  
Alpha Rhythm ◽  
Physiological Status ◽  
Noise Resistance ◽  
Middle Aged ◽  
Coupling Analysis ◽  
Scale Change ◽  
The Difference

This paper presents the Multiscale Mutual Mode Entropy algorithm to quantify the coupling degree between two alpha rhythm EEG time series which are simultaneously acquired. The results show that in the process of scale change, the young and middle-aged differ from each other in terms of the coupling degree of alpha rhythm EEG and the difference grow clear gradually. So the Multiscale Mutual Mode Entropy can be used to analyze the coupling information of time series under different physiological status, and it also has good noise resistance. Besides, as an indicator of measuring brain function, in the future it can also come to the aid of clinical evaluation of brain function.

COMPARISON OF ICTAL AND INTERICTAL EEG SIGNALS USING FRACTAL FEATURES

2013 ◽  
Vol 23 (06) ◽  
pp. 1350028 ◽  
Author(s):  
YU WANG ◽  
WEIDONG ZHOU ◽  
QI YUAN ◽  
XUELI LI ◽  
QINGFANG MENG ◽  
...  
Keyword(s):  
Feature Analysis ◽  
Support Vector ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Eeg Classification ◽  
Significant Difference ◽  
Interictal Eeg ◽  
Nonlinear Features ◽  
The Difference ◽  
Multi Scales

The feature analysis of epileptic EEG is very significant in diagnosis of epilepsy. This paper introduces two nonlinear features derived from fractal geometry for epileptic EEG analysis. The features of blanket dimension and fractal intercept are extracted to characterize behavior of EEG activities, and then their discriminatory power for ictal and interictal EEGs are compared by means of statistical methods. It is found that there is significant difference of the blanket dimension and fractal intercept between interictal and ictal EEGs, and the difference of the fractal intercept feature between interictal and ictal EEGs is more noticeable than the blanket dimension feature. Furthermore, these two fractal features at multi-scales are combined with support vector machine (SVM) to achieve accuracies of 97.58% for ictal and interictal EEG classification and 97.13% for normal, ictal and interictal EEG classification.

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