scholarly journals Biological Computation Indexes of Brain Oscillations in Unattended Facial Expression Processing Based on Event-Related Synchronization/Desynchronization

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Bo Yu ◽  
Lin Ma ◽  
Haifeng Li ◽  
Lun Zhao ◽  
Hongjian Bo ◽  
...  
Keyword(s):  
Facial Expressions ◽  
Emotional Experience ◽  
Vital Role ◽  
Brain Oscillations ◽  
Eeg Signals ◽  
Frequency Bands ◽  
Set Up ◽  
Biological Computation ◽  
Expression Processing ◽  
Electroencephalogram Eeg

Estimation of human emotions from Electroencephalogram (EEG) signals plays a vital role in affective Brain Computer Interface (BCI). The present study investigated the different event-related synchronization (ERS) and event-related desynchronization (ERD) of typical brain oscillations in processing Facial Expressions under nonattentional condition. The results show that the lower-frequency bands are mainly used to update Facial Expressions and distinguish the deviant stimuli from the standard ones, whereas the higher-frequency bands are relevant to automatically processing different Facial Expressions. Accordingly, we set up the relations between each brain oscillation and processing unattended Facial Expressions by the measures of ERD and ERS. This research first reveals the contributions of each frequency band for comprehension of Facial Expressions in preattentive stage. It also evidences that participants have emotional experience under nonattentional condition. Therefore, the user’s emotional state under nonattentional condition can be recognized in real time by the ERD/ERS computation indexes of different frequency bands of brain oscillations, which can be used in affective BCI to provide the user with more natural and friendly ways.

Graph Attention Network with Focal Loss for Seizure Detection on Electroencephalography Signals

2021 ◽  
pp. 2150027
Author(s):  
Yanna Zhao ◽  
Gaobo Zhang ◽  
Changxu Dong ◽  
Qi Yuan ◽  
Fangzhou Xu ◽  
...  
Keyword(s):  
Detection Method ◽  
Seizure Detection ◽  
Vital Role ◽  
Eeg Signals ◽  
Attention Network ◽  
Data Imbalance ◽  
Imbalance Problem ◽  
Eeg Data ◽  
Epilepsy Diagnosis ◽  
Electroencephalogram Eeg

Automatic seizure detection from electroencephalogram (EEG) plays a vital role in accelerating epilepsy diagnosis. Previous researches on seizure detection mainly focused on extracting time-domain and frequency-domain features from single electrodes, while paying little attention to the positional correlations between different EEG channels of the same subject. Moreover, data imbalance is common in seizure detection scenarios where the duration of nonseizure periods is much longer than the duration of seizures. To cope with the two challenges, a novel seizure detection method based on graph attention network (GAT) is presented. The approach acts on graph-structured data and takes the raw EEG data as input. The positional relationship between different EEG signals is exploited by GAT. The loss function of the GAT model is redefined using the focal loss to tackle data imbalance problem. Experiments are conducted on the CHB-MIT dataset. The accuracy, sensitivity and specificity of the proposed method are 98.89[Formula: see text], 97.10[Formula: see text] and 99.63[Formula: see text], respectively.

scholarly journals EEG-Based Emotion Recognition by Convolutional Neural Network with Multi-Scale Kernels

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5092
Author(s):  
Tran-Dac-Thinh Phan ◽  
Soo-Hyung Kim ◽  
Hyung-Jeong Yang ◽  
Guee-Sang Lee
Keyword(s):  
Emotion Recognition ◽  
Binary Classification ◽  
Eeg Signals ◽  
Frequency Bands ◽  
Multi Scale ◽  
Eeg Data ◽  
Crucial Information ◽  
The Time Domain ◽  
Electroencephalogram Eeg ◽  
Selection Of

Besides facial or gesture-based emotion recognition, Electroencephalogram (EEG) data have been drawing attention thanks to their capability in countering the effect of deceptive external expressions of humans, like faces or speeches. Emotion recognition based on EEG signals heavily relies on the features and their delineation, which requires the selection of feature categories converted from the raw signals and types of expressions that could display the intrinsic properties of an individual signal or a group of them. Moreover, the correlation or interaction among channels and frequency bands also contain crucial information for emotional state prediction, and it is commonly disregarded in conventional approaches. Therefore, in our method, the correlation between 32 channels and frequency bands were put into use to enhance the emotion prediction performance. The extracted features chosen from the time domain were arranged into feature-homogeneous matrices, with their positions following the corresponding electrodes placed on the scalp. Based on this 3D representation of EEG signals, the model must have the ability to learn the local and global patterns that describe the short and long-range relations of EEG channels, along with the embedded features. To deal with this problem, we proposed the 2D CNN with different kernel-size of convolutional layers assembled into a convolution block, combining features that were distributed in small and large regions. Ten-fold cross validation was conducted on the DEAP dataset to prove the effectiveness of our approach. We achieved the average accuracies of 98.27% and 98.36% for arousal and valence binary classification, respectively.

MIDC Role in Infrastructural Development in Navi Mumbai

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Prashant H. Bhagat
Keyword(s):  
Industrial Development ◽  
Industrial Sector ◽  
Vital Role ◽  
The State ◽  
Infrastructure Development ◽  
Industrial Areas ◽  
Industrial Estates ◽  
Development Corporation ◽  
Set Up ◽  
Industrial Development Corporation

The BID (Board of Industrial Development) framed the legislation and it was introduced before the state legislation and passed in the form of Maharashtra Industrial Act which gave birth to Maharashtra Industrial Development Corporation (MIDC), as a separate corporation on August 1, 1962. The BID was the first personnel strength of MIDC. A small ceremony at Wagle Estate Thane, under the Chairmanship of the Chief Minister Shri Y.B. Chavan, marked the birth of MIDC on August 1, 1962. The Board of Industrial Development during its existence between October 1, 1960 and August 1, 1962 has done enough spade work to identify the locations for setting up industrial areas in different parts of the state. Thus, right in the first year of establishment MIDC came up with 14 industrial areas, to initiate action for infrastructure and help entrepreneurs set up the industrial units in those areas. Maharashtra Industrial Development Corporation is the nodal industrial infrastructure development agency of the Maharashtra Government with the basic objective of setting up industrial areas with a provision of industrial infrastructure all over the state for planned and systematic industrial development. MIDC is an innovative, professionally managed, and user friendly organization that provides the world industrial infrastructure. MIDC has played a vital role in the development of industrial infrastructure in the state of Maharashtra. As the state steps into the next millennium, MIDC lives up to its motto Udyamat Sakal Samruddhi i.e., prosperity to all through industrialization. Indeed, in the endeavor of the state to retain its prime position in the industrial sector, MIDC has played a pivotal role in the last 35 years. MIDC has developed 268 industrial estates across the state which spread over 52653 hectares of land. The growth of the Corporation, achieved in the various fields, during the last three years, could be gauged from the fact that the area currently in possession of MIDC has doubled from 25,000 hectares in 1995.

scholarly journals Detecting driver mental fatigue based on Electroencephalogram (EEG) signals during simulated driving

2021 ◽  
Vol 1070 (1) ◽  
pp. 012096
Author(s):  
S Pradeep Kumar ◽  
Suganiya Murugan ◽  
Jerritta Selvaraj ◽  
Arun Sahayadhas
Keyword(s):  
Mental Fatigue ◽  
Eeg Signals ◽  
Simulated Driving ◽  
Electroencephalogram Eeg

The face of future: Face expressions during future thinking

2021 ◽  
pp. 174702182199299
Author(s):  
Mohamad El Haj ◽  
Emin Altintas ◽  
Ahmed A Moustafa ◽  
Abdel Halim Boudoukha
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Emotional Experience ◽  
Future Scenarios ◽  
Future Thinking ◽  
Analysis Software ◽  
Emotional Facial Expressions ◽  
Facial Analysis ◽  
The Face ◽  
Face Expressions

Future thinking, which is the ability to project oneself forward in time to pre-experience an event, is intimately associated with emotions. We investigated whether emotional future thinking can activate emotional facial expressions. We invited 43 participants to imagine future scenarios, cued by the words “happy,” “sad,” and “city.” Future thinking was video recorded and analysed with a facial analysis software to classify whether facial expressions (i.e., happy, sad, angry, surprised, scared, disgusted, and neutral facial expression) of participants were neutral or emotional. Analysis demonstrated higher levels of happy facial expressions during future thinking cued by the word “happy” than “sad” or “city.” In contrast, higher levels of sad facial expressions were observed during future thinking cued by the word “sad” than “happy” or “city.” Higher levels of neutral facial expressions were observed during future thinking cued by the word “city” than “happy” or “sad.” In the three conditions, the neutral facial expressions were high compared with happy and sad facial expressions. Together, emotional future thinking, at least for future scenarios cued by “happy” and “sad,” seems to trigger the corresponding facial expression. Our study provides an original physiological window into the subjective emotional experience during future thinking.

scholarly journals Hand Surgery in Thailand

2021 ◽  
Vol 13 (01) ◽  
pp. 35-41
Author(s):  
Sunyarn Niempoog ◽  
Kiat Witoonchart ◽  
Woraphon Jaroenporn
Keyword(s):  
World War Ii ◽  
Traffic Accidents ◽  
Hand Surgery ◽  
Vital Role ◽  
Second Phase ◽  
World War ◽  
Third Phase ◽  
Foreign Countries ◽  
General Surgeons ◽  
Set Up

AbstractModern hand surgery in Thailand started after the end of World War II. It is divided into 4 phases. In the initial phase (1950-1965), the surgery of the hand was mainly performed by general surgeons. In 1965-1975, which was the second phase, many plastic surgeons and orthopaedic surgeons graduated from foreign countries and came back to Thailand. They played a vital role in the treatment of the surgery of the hand and set up hand units in many centers. They also contributed to the establishment of the “Thai Society for Surgery of the Hand,” which still continues to operate. In the third phase (1975-2000), there was a dramatic development of microsurgery because of the rapid economic expansion. There were many replantation, free tissue transfers, and brachial plexus surgeries in traffic and factory-related accidents. The first hand-fellow training program began in 1993. In the fourth phase (since 2000), the number of hand injuries from factory-related accidents began declining. But the injury from traffic accidents had been increasing both in severity and number. Moreover, the diseases of hand that relate to aging and degeneration had been on the rise. Thai hand surgeons have been using several state-of-the-art technologies such as arthroscopic and endoscopic surgery. They are continuing to invent innovations, generating international publications, and frequently being invited as speakers in foreign countries.

scholarly journals EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

2019 ◽  
Vol 9 (11) ◽  
pp. 326 ◽  
Author(s):  
Hong Zeng ◽  
Zhenhua Wu ◽  
Jiaming Zhang ◽  
Chen Yang ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Speaker Recognition ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Single Subject ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Electroencephalogram Eeg ◽  
Deep Learning Model

Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.

AGE-BASED VARIATIONS OF FRACTAL STRUCTURE OF EEG SIGNAL IN PATIENTS WITH EPILEPSY

Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850051 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
SAJAD JAFARI
Keyword(s):  
Fractal Dimension ◽  
Brain Research ◽  
Age Groups ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals ◽  
Important Challenge ◽  
Patients With Epilepsy ◽  
Process Complexity ◽  
Electroencephalogram Eeg

It is known that aging affects neuroplasticity. On the other hand, neuroplasticity can be studied by analyzing the electroencephalogram (EEG) signal. An important challenge in brain research is to study the variations of neuroplasticity during aging for patients suffering from epilepsy. This study investigates the variations of the complexity of EEG signal during aging for patients with epilepsy. For this purpose, we employed fractal dimension as an indicator of process complexity. We classified the subjects in different age groups and computed the fractal dimension of their EEG signals. Our investigations showed that as patients get older, their EEG signal will be more complex. The method of investigation that has been used in this study can be further employed to study the variations of EEG signal in case of other brain disorders during aging.

Emotion-specific effects of facial expressions and postures on emotional experience.

1989 ◽  
Vol 57 (1) ◽  
pp. 100-108 ◽  
Author(s):  
Sandra E. Duclos ◽  
James D. Laird ◽  
Eric Schneider ◽  
Melissa Sexter ◽  
et al
Keyword(s):  
Facial Expressions ◽  
Emotional Experience ◽  
Specific Effects

scholarly journals Time-ResNeXt for epilepsy recognition based on EEG signals in wireless networks

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Shaoqiang Wang ◽  
Shudong Wang ◽  
Song Zhang ◽  
Yifan Wang
Keyword(s):  
Deep Learning ◽  
Network Structure ◽  
Signal Recognition ◽  
Eeg Signals ◽  
Real World Data ◽  
Data Set ◽  
Practical Applications ◽  
Epilepsy Diagnosis ◽  
Single Data ◽  
Electroencephalogram Eeg

Abstract To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset) and has great potential for improving clinical practice.

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