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.

Wavelet Transform Based Feature Extraction for EEG Signal Classification

2021 ◽  
Vol 20 ◽  
pp. 199-206
Author(s):  
Seda Postalcioglu
Keyword(s):  
Wavelet Transform ◽  
High Performance ◽  
Feature Vector ◽  
Fast Response ◽  
Eeg Signal ◽  
Real Time Control ◽  
Time Control ◽  
Band Power ◽  
Performance Rate

This study focused on the classification of EEG signal. The study aims to make a classification with fast response and high-performance rate. Thus, it could be possible for real-time control applications as Brain-Computer Interface (BCI) systems. The feature vector is created by Wavelet transform and statistical calculations. It is trained and tested with a neural network. The db4 wavelet is used in the study. Pwelch, skewness, kurtosis, band power, median, standard deviation, min, max, energy, entropy are used to make the wavelet coefficients meaningful. The performance is achieved as 99.414% with the running time of 0.0209 seconds

NEUROECONOMICS, NEUROPHYSIOLOGY AND THE COMMON CURRENCY HYPOTHESIS

2008 ◽  
Vol 24 (3) ◽  
pp. 419-429 ◽  
Author(s):  
Anthony Landreth ◽  
John Bickle
Keyword(s):  
Rational Choice ◽  
Dopamine Release ◽  
Research Programme ◽  
Neural Mechanism ◽  
The Other ◽  
Common Currency ◽  
The Common ◽  
The Brain ◽  
Electrophysiological Methods

We briefly describe ways in which neuroeconomics has made contributions to its contributing disciplines, especially neuroscience, and a specific way in which it could make future contributions to both. The contributions of a scientific research programme can be categorized in terms of (1) description and classification of phenomena, (2) the discovery of causal relationships among those phenomena, and (3) the development of tools to facilitate (1) and (2). We consider ways in which neuroeconomics has advanced neuroscience and economics along each line. Then, focusing on electrophysiological methods, we consider a puzzle within neuroeconomics whose solution we believe could facilitate contributions to both neuroscience and economics, in line with category (2). This puzzle concerns how the brain assigns reward values to otherwise incomparable stimuli. According to the common currency hypothesis, dopamine release is a component of a neural mechanism that solves comparability problems. We review two versions of the common currency hypothesis, one proposed by Read Montague and colleagues, the other by William Newsome and colleagues, and fit these hypotheses into considerations of rational choice.

scholarly journals RESEARCH ON BRAIN NETWORK CLASSIFICATION METHOD BASED ON INTEGRATED MODEL

2020 ◽  
pp. 1-2
Author(s):  
Zhang- sensen
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Brain Network ◽  
Classification Model ◽  
Comparative Experiment ◽  
Machine Learning Methods ◽  
Healthy Elderly ◽  
Healthy Elderly People ◽  
The Brain

mild cognitive impairment (MCI) is a condition between healthy elderly people and alzheimer's disease (AD). At present, brain network analysis based on machine learning methods can help diagnose MCI. In this paper, the brain network is divided into several subnets based on the shortest path,and the feature vectors of each subnet are extracted and classified. In order to make full use of subnet information, this paper adopts integrated classification model for classification.Each base classification model can predict the classification of a subnet,and the classification results of all subnets are calculated as the classification results of brain network.In order to verify the effectiveness of this method,a brain network of 66 people was constructed and a comparative experiment was carried out.The experimental results show that the classification accuracy of the integrated classification model proposed in this paper is 19% higher than that of SVM,which effectively improves the classification accuracy

scholarly journals 1D conditional generative adversarial network for spectrum-to-spectrum translation of simulated chemical reflectance signatures

2021 ◽  
Author(s):  
Cara Murphy ◽  
John Kerekes
Keyword(s):  
Classification Accuracy ◽  
Domain Adaptation ◽  
Real Data ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Average Classification Accuracy ◽  
Adversarial Network ◽  
Chemical Residues ◽  
Reflectance Data

The classification of trace chemical residues through active spectroscopic sensing is challenging due to the lack of physics-based models that can accurately predict spectra. To overcome this challenge, we leveraged the field of domain adaptation to translate data from the simulated to the measured domain for training a classifier. We developed the first 1D conditional generative adversarial network (GAN) to perform spectrum-to-spectrum translation of reflectance signatures. We applied the 1D conditional GAN to a library of simulated spectra and quantified the improvement in classification accuracy on real data using the translated spectra for training the classifier. Using the GAN-translated library, the average classification accuracy increased from 0.622 to 0.723 on real chemical reflectance data, including data from chemicals not included in the GAN training set.

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 Multimodal Object Classification Models Inspired by Multisensory Integration in the Brain

2019 ◽  
Vol 9 (1) ◽  
pp. 3 ◽  
Author(s):  
Rajesh Amerineni ◽  
Resh S. Gupta ◽  
Lalit Gupta
Keyword(s):  
Multisensory Integration ◽  
Object Classification ◽  
Classification Systems ◽  
Multilayer Perceptrons ◽  
Classification Models ◽  
Multivariate Statistical ◽  
Statistical Classifiers ◽  
Multimodal Classification ◽  
The Brain

Two multimodal classification models aimed at enhancing object classification through the integration of semantically congruent unimodal stimuli are introduced. The feature-integrating model, inspired by multisensory integration in the subcortical superior colliculus, combines unimodal features which are subsequently classified by a multimodal classifier. The decision-integrating model, inspired by integration in primary cortical areas, classifies unimodal stimuli independently using unimodal classifiers and classifies the combined decisions using a multimodal classifier. The multimodal classifier models are implemented using multilayer perceptrons and multivariate statistical classifiers. Experiments involving the classification of noisy and attenuated auditory and visual representations of ten digits are designed to demonstrate the properties of the multimodal classifiers and to compare the performances of multimodal and unimodal classifiers. The experimental results show that the multimodal classification systems exhibit an important aspect of the “inverse effectiveness principle” by yielding significantly higher classification accuracies when compared with those of the unimodal classifiers. Furthermore, the flexibility offered by the generalized models enables the simulations and evaluations of various combinations of multimodal stimuli and classifiers under varying uncertainty conditions.

scholarly journals Fine-Grained Image Classification for Crop Disease Based on Attention Mechanism

2020 ◽  
Vol 11 ◽  
Author(s):  
Guofeng Yang ◽  
Yong He ◽  
Yong Yang ◽  
Beibei Xu
Keyword(s):  
Image Classification ◽  
Classification Accuracy ◽  
Attention Mechanism ◽  
Classification Model ◽  
Classification Models ◽  
Fine Grained ◽  
Complex Scenes ◽  
Crop Disease ◽  
Visual Disturbances

Fine-grained image classification is a challenging task because of the difficulty in identifying discriminant features, it is not easy to find the subtle features that fully represent the object. In the fine-grained classification of crop disease, visual disturbances such as light, fog, overlap, and jitter are frequently encountered. To explore the influence of the features of crop leaf images on the classification results, a classification model should focus on the more discriminative regions of the image while improving the classification accuracy of the model in complex scenes. This paper proposes a novel attention mechanism that effectively utilizes the informative regions of an image, and describes the use of transfer learning to quickly construct several fine-grained image classification models of crop disease based on this attention mechanism. This study uses 58,200 crop leaf images as a dataset, including 14 different crops and 37 different categories of healthy/diseased crops. Among them, different diseases of the same crop have strong similarities. The NASNetLarge fine-grained classification model based on the proposed attention mechanism achieves the best classification effect, with an F1 score of up to 93.05%. The results show that the proposed attention mechanism effectively improves the fine-grained classification of crop disease images.

Application of the Hilbert_Huang Transform and Wavelet Transform in the EEG Processing

2013 ◽  
Vol 756-759 ◽  
pp. 1753-1757
Author(s):  
Gui Xin Zhang ◽  
Ping Dong Wu ◽  
Man Ling Huang
Keyword(s):  
Wavelet Transform ◽  
Brain Activity ◽  
Eeg Signal ◽  
Hilbert Huang Transform ◽  
Signal Processing Method ◽  
Communication Method ◽  
Machine Interface ◽  
And Control ◽  
The Brain ◽  
Eeg Signal Processing

Brain-Machine Interface (BMI) could make people control machine through EEG which is produced by the brain activity, and it provide a new communication method between human and machine. The research for BMI will extend the ability of communication and control the environment and machine. The key point of the BMI is how to abstract and distinguish different EEG characters. Therefore, EEG signal processing method is the emphasis of BMI. Wavelet Transform and Hilbert-Huang Transform are used to analyze the EEG signal in this paper. The results indicate that these two methods could abstract the main characters of the EEG, but the Hilbert-Huang Transform could express the distributing status in the time and frequency aspect of the EEG more accurately, because it produces the self-adaptive basis according the data, and obtain the local and instantaneous frequency of the EEG.

scholarly journals Automated VOI Analysis in FDDNP PET Using Structural Warping: Validation through Classification of Alzheimer's Disease Patients

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Moses Q. Wilks ◽  
Hillary Protas ◽  
Mirwais Wardak ◽  
Vladimir Kepe ◽  
Gary W. Small ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Image Warping ◽  
Surface Mapping ◽  
Common Space ◽  
Automated Method ◽  
3D Image Warping ◽  
The Common ◽  
Single Set

We evaluate an automated approach to the cortical surface mapping (CSM) method of VOI analysis in PET. Although CSM has been previously shown to be successful, the process can be long and tedious. Here, we present an approach that removes these difficulties through the use of 3D image warping to a common space. We test this automated method using studies of FDDNP PET in Alzheimer's disease and mild cognitive impairment. For each subject, VOIs were created, through CSM, to extract regional PET data. After warping to the common space, a single set of CSM-generated VOIs was used to extract PET data from all subjects. The data extracted using a single set of VOIs outperformed the manual approach in classifying AD patients from MCIs and controls. This suggests that this automated method can remove variance in measurements of PET data and can facilitate accurate, high-throughput image analysis.

scholarly journals Emotion classification based on brain wave: a survey

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ting-Mei Li ◽  
Han-Chieh Chao ◽  
Jianming Zhang
Keyword(s):  
Classification Methods ◽  
Emotion Classification ◽  
Brain Wave ◽  
Brain Science ◽  
Emotion Analysis ◽  
Human Emotion ◽  
Novel Method ◽  
Human Emotions ◽  
The Brain

AbstractBrain wave emotion analysis is the most novel method of emotion analysis at present. With the progress of brain science, it is found that human emotions are produced by the brain. As a result, many brain-wave emotion related applications appear. However, the analysis of brain wave emotion improves the difficulty of analysis because of the complexity of human emotion. Many researchers used different classification methods and proposed methods for the classification of brain wave emotions. In this paper, we investigate the existing methods of brain wave emotion classification and describe various classification methods.

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