scholarly journals EEG-based human emotion recognition using entropy as a feature extraction measure

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Pragati Patel ◽  
Raghunandan R ◽  
Ramesh Naidu Annavarapu
Keyword(s):  
Emotion Recognition ◽  
Emotional State ◽  
Computer Interface ◽  
Eeg Signal ◽  
Emotion Classification ◽  
Emotion Identification ◽  
Physiological Signal ◽  
Wide Range ◽  
Electrical Activities ◽  
The Brain

AbstractMany studies on brain–computer interface (BCI) have sought to understand the emotional state of the user to provide a reliable link between humans and machines. Advanced neuroimaging methods like electroencephalography (EEG) have enabled us to replicate and understand a wide range of human emotions more precisely. This physiological signal, i.e., EEG-based method is in stark comparison to traditional non-physiological signal-based methods and has been shown to perform better. EEG closely measures the electrical activities of the brain (a nonlinear system) and hence entropy proves to be an efficient feature in extracting meaningful information from raw brain waves. This review aims to give a brief summary of various entropy-based methods used for emotion classification hence providing insights into EEG-based emotion recognition. This study also reviews the current and future trends and discusses how emotion identification using entropy as a measure to extract features, can accomplish enhanced identification when using EEG signal.

scholarly journals Analytical Comparison of Two Emotion Classification Models Based on Convolutional Neural Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Huiping Jiang ◽  
Demeng Wu ◽  
Rui Jiao ◽  
Zongnan Wang
Keyword(s):  
Wavelet Transform ◽  
Classification Accuracy ◽  
Eeg Signal ◽  
Classification Models ◽  
Emotion Classification ◽  
Common Space ◽  
Average Classification Accuracy ◽  
The Common ◽  
The Brain

Electroencephalography (EEG) is the measurement of neuronal activity in different areas of the brain through the use of electrodes. As EEG signal technology has matured over the years, it has been applied in various methods to EEG emotion recognition, most significantly including the use of convolutional neural network (CNN). However, these methods are still not ideal, and shortcomings have been found in the results of some models of EEG feature extraction and classification. In this study, two CNN models were selected for the extraction and classification of preprocessed data, namely, common spatial patterns- (CSP-) CNN and wavelet transform- (WT-) CNN. Using the CSP-CNN, we first used the common space model to reduce dimensionality and then applied the CNN directly to extract and classify the features of the EEG; while, with the WT-CNN model, we used the wavelet transform to extract EEG features, thereafter applying the CNN for classification. The EEG classification results of these two classification models were subsequently analyzed and compared, with the average classification accuracy of the CSP-CNN model found to be 80.56%, and the average classification accuracy of the WT-CNN model measured to 86.90%. Thus, the findings of this study show that the average classification accuracy of the WT-CNN model was 6.34% higher than that of the CSP-CNN.

scholarly journals Historical perspectives, challenges, and future directions of implantable brain-computer interfaces for sensorimotor applications

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Santosh Chandrasekaran ◽  
Matthew Fifer ◽  
Stephan Bickel ◽  
Luke Osborn ◽  
Jose Herrero ◽  
...  
Keyword(s):  
Computer Technology ◽  
Traumatic Injury ◽  
The Body ◽  
Computer Interface ◽  
Future Directions ◽  
Historical Perspectives ◽  
Computer Interfaces ◽  
Wide Range ◽  
Study Participants ◽  
The Brain

AbstractAlmost 100 years ago experiments involving electrically stimulating and recording from the brain and the body launched new discoveries and debates on how electricity, movement, and thoughts are related. Decades later the development of brain-computer interface technology began, which now targets a wide range of applications. Potential uses include augmentative communication for locked-in patients and restoring sensorimotor function in those who are battling disease or have suffered traumatic injury. Technical and surgical challenges still surround the development of brain-computer technology, however, before it can be widely deployed. In this review we explore these challenges, historical perspectives, and the remarkable achievements of clinical study participants who have bravely forged new paths for future beneficiaries.

scholarly journals Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Tae-Ju Lee ◽  
Seung-Min Park ◽  
Kwee-Bo Sim
Keyword(s):  
Signal Analysis ◽  
Harmony Search ◽  
Heuristic Method ◽  
Heuristic Approach ◽  
Computer Interface ◽  
Eeg Signal ◽  
Feature Classification ◽  
The Brain ◽  
Eeg Signal Analysis ◽  
Bci System

This paper presents a heuristic method for electroencephalography (EEG) grouping and feature classification using harmony search (HS) for improving the accuracy of the brain-computer interface (BCI) system. EEG, a noninvasive BCI method, uses many electrodes on the scalp, and a large number of electrodes make the resulting analysis difficult. In addition, traditional EEG analysis cannot handle multiple stimuli. On the other hand, the classification method using the EEG signal has a low accuracy. To solve these problems, we use a heuristic approach to reduce the complexities in multichannel problems and classification. In this study, we build a group of stimuli using the HS algorithm. Then, the features from common spatial patterns are classified by the HS classifier. To confirm the proposed method, we perform experiments using 64-channel EEG equipment. The subjects are subjected to three kinds of stimuli: audio, visual, and motion. Each stimulus is applied alone or in combination with the others. The acquired signals are processed by the proposed method. The classification results in an accuracy of approximately 63%. We conclude that the heuristic approach using the HS algorithm on the BCI is beneficial for EEG signal analysis.

scholarly journals Emotion Recognition and Regulation Based on Stacked Sparse Auto-Encoder Network and Personalized Reconfigurable Music

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 593
Author(s):  
Yinsheng Li ◽  
Wei Zheng
Keyword(s):  
Emotional Regulation ◽  
Emotional State ◽  
Emotional Adjustment ◽  
Recognition Rate ◽  
Traditional Music ◽  
Music Preference ◽  
Emotional States ◽  
Eeg Signal ◽  
Long Duration ◽  
The Brain

Music can regulate and improve the emotions of the brain. Traditional emotional regulation approaches often adopt complete music. As is well-known, complete music may vary in pitch, volume, and other ups and downs. An individual’s emotions may also adopt multiple states, and music preference varies from person to person. Therefore, traditional music regulation methods have problems, such as long duration, variable emotional states, and poor adaptability. In view of these problems, we use different music processing methods and stacked sparse auto-encoder neural networks to identify and regulate the emotional state of the brain in this paper. We construct a multi-channel EEG sensor network, divide brainwave signals and the corresponding music separately, and build a personalized reconfigurable music-EEG library. The 17 features in the EEG signal are extracted as joint features, and the stacked sparse auto-encoder neural network is used to classify the emotions, in order to establish a music emotion evaluation index. According to the goal of emotional regulation, music fragments are selected from the personalized reconfigurable music-EEG library, then reconstructed and combined for emotional adjustment. The results show that, compared with complete music, the reconfigurable combined music was less time-consuming for emotional regulation (76.29% less), and the number of irrelevant emotional states was reduced by 69.92%. In terms of adaptability to different participants, the reconfigurable music improved the recognition rate of emotional states by 31.32%.

scholarly journals Complexity of EEG Reflects Socioeconomic Context and Geofootprint

2017 ◽  
Author(s):  
Dhanya Parameshwaran ◽  
Tara C. Thiagarajan
Keyword(s):  
Urban Environments ◽  
Strongly Correlated ◽  
Eeg Signal ◽  
Modern Life ◽  
Cognitive Measure ◽  
Completion Task ◽  
Wide Range ◽  
Modern Experience ◽  
The Brain ◽  
Motorized Transport

ABSTRACTThe fine scale structure and resulting activity of the brain are largely shaped by experience, suggesting that the faster rate and complexity of experience offered by modern civilization may have significant impact on human brain dynamics. Here we defined a new measure of complexity of the EEG signal and compared it across populations spanning incomes from <$1/day to ∼$410/day with a wide range of access to features of modern life such as urban environments, higher education, electricity, motorized transport and telecommunication. Complexity across our sample spanned a 2.75-fold range, separating into two distinct distributions of pre-modern and modern experience. Furthermore, complexity scaled systematically with various technologies and experience factors, of which travel or geofootprint had the strongest relationship. Complexity also had a steep non-linear relationship with income that leveled out at an income of ∼$30/ day. Finally, it was strongly correlated to performance on a pattern completion task indicating its relevance as a cognitive measure. In light of growing income inequality and divergence in access to the tools of modern living across the globe, our findings have significant implications for social policy.

scholarly journals An Emotion Recognition Based on Physiological Signals

2019 ◽  
Vol 8 (9S) ◽  
pp. 335-341
Keyword(s):  
Emotion Recognition ◽  
Pulse Signal ◽  
Physiological Signals ◽  
Computer Interface ◽  
Human Computer Interface ◽  
Emotion Detection ◽  
Extraction Techniques ◽  
Emotion Elicitation ◽  
Physiological Signal ◽  
Plausible Mechanism

Emotion recognition is alluring considerable interest among the researchers. Emotions are discovered by facial, speech, gesture, posture and physiological signals. Physiological signals are a plausible mechanism to recognize emotion using human-computer interaction. The objective of this paper is to put forth the recognition of emotions using physiological signals. Various emotion elicitation protocols, feature extraction techniques, classification methods that aim at recognizing emotions from physiological signals are discussed here. Wrist Pulse Signal is also discussed to fill the lacunae of the other physiological signal for emotion detection. Working on basic as well as non-basic human emotion and human-computer interface will make the system robust.

scholarly journals Human Emotional State Assessment Based on a Video Portrayal

2020 ◽  
Vol 13 (4) ◽  
pp. 4-24 ◽  
Author(s):  
V.A. Barabanschikov ◽  
E.V. Suvorova
Keyword(s):  
Emotion Recognition ◽  
Recognition Test ◽  
Emotional State ◽  
State Assessment ◽  
Identification Accuracy ◽  
Emotional Expressions ◽  
Emotional States ◽  
Wide Range ◽  
Similarities And Differences ◽  
Valence And Arousal

The article is devoted to the results of approbation of the Geneva Emotion Recognition Test (GERT), a Swiss method for assessing dynamic emotional states, on Russian sample. Identification accuracy and the categorical fields’ structure of emotional expressions of a “living” face are analysed. Similarities and differences in the perception of affective groups of dynamic emotions in the Russian and Swiss samples are considered. A number of patterns of recognition of multi-modal expressions with changes in valence and arousal of emotions are described. Differences in the perception of dynamics and statics of emotional expressions are revealed. GERT method confirmed it’s high potential for solving a wide range of academic and applied problems.

scholarly journals Emotional State Recognition Using Facial Expression, Voice, and Physiological Signal

2018 ◽  
Vol 6 (1) ◽  
pp. 1-20
Author(s):  
Tahirou Djara ◽  
Abdoul Matine Ousmane ◽  
Antoine Vianou
Keyword(s):  
Signal Processing ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Emotional State ◽  
Processing Algorithm ◽  
State Recognition ◽  
Physiological Signal ◽  
Emotional Interaction

Emotion recognition is an important aspect of affective computing, one of whose aims is the study and development of behavioral and emotional interaction between human and machine. In this context, another important point concerns acquisition devices and signal processing tools which lead to an estimation of the emotional state of the user. This article presents a survey about concepts around emotion, multimodality in recognition, physiological activities and emotional induction, methods and tools for acquisition and signal processing with a focus on processing algorithm and their degree of reliability.

scholarly journals Emotional State Recognition Using Facial Expression, Voice, and Physiological Signal

2020 ◽  
pp. 1946-1967
Author(s):  
Tahirou Djara ◽  
Abdoul Matine Ousmane ◽  
Antoine Vianou
Keyword(s):  
Signal Processing ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Emotional State ◽  
Processing Algorithm ◽  
State Recognition ◽  
Physiological Signal ◽  
Emotional Interaction

Emotion recognition is an important aspect of affective computing, one of whose aims is the study and development of behavioral and emotional interaction between human and machine. In this context, another important point concerns acquisition devices and signal processing tools which lead to an estimation of the emotional state of the user. This article presents a survey about concepts around emotion, multimodality in recognition, physiological activities and emotional induction, methods and tools for acquisition and signal processing with a focus on processing algorithm and their degree of reliability.

scholarly journals Source Localization of EEG Brainwaves Activities via Mother Wavelets Families for SWT Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tarek Frikha ◽  
Najmeddine Abdennour ◽  
Faten Chaabane ◽  
Oussama Ghorbel ◽  
Rami Ayedi ◽  
...  
Keyword(s):  
Source Localization ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Cognitive State ◽  
Eeg Signal ◽  
Mother Wavelet ◽  
Medical Disorders ◽  
The Brain ◽  
Mother Wavelets

A Brain-Computer Interface (BCI) is a system used to communicate with an external world through the brain activity. The brain activity is measured by electroencephalography (EEG) signal and then processed by a BCI system. EEG source reconstruction could be a way to improve the accuracy of EEG classification in EEG based brain-computer interface (BCI). The source localization of the human brain activities can be an important resource for the recognition of the cognitive state, medical disorders, and a better understanding of the brain in general. In this study, we have compared 51 mother wavelets taken from 7 different wavelet families, which are applied to a Stationary Wavelet Transform (SWT) decomposition of an EEG signal. This process includes Haar, Symlets, Daubechies, Coiflets, Discrete Meyer, Biorthogonal, and reverse Biorthogonal wavelet families in extracting five different brainwave subbands for source localization. For this process, we used the Independent Component Analysis (ICA) for feature extraction followed by the Boundary Element Model (BEM) and the Equivalent Current Dipole (ECD) for the forward and inverse problem solutions. The evaluation results in investigating the optimal mother wavelet for source localization eventually identified the sym20 mother wavelet as the best choice followed by bior6.8 and coif5.

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