Recognition of human emotion with spectral features using multi layer-perceptron

2020 ◽  
Vol 24 (3) ◽  
pp. 227-233
Author(s):  
A. Pramod Reddy ◽  
Vijayarajan V
Keyword(s):  
Support Vector Machine ◽  
Spectral Density ◽  
Emotion Recognition ◽  
Power Spectral Density ◽  
Multilayer Perceptron ◽  
Support Vector ◽  
Spectral Features ◽  
Emotional States ◽  
Multi Layer Perceptron ◽  
Power Spectral

For emotion recognition, here the features extracted from prevalent speech samples of Berlin emotional database are pitch, intensity, log energy, formant, mel-frequency ceptral coefficients (MFCC) as base features and power spectral density as an added function of frequency. In these work seven emotions namely anger, neutral, happy, Boredom, disgust, fear and sadness are considered in our study. Temporal and Spectral features are considered for building AER(Automatic Emotion Recognition) model. The extracted features are analyzed using Support Vector Machine (SVM) and with multilayer perceptron (MLP) a class of feed-forward ANN classifiers is/are used to classify different emotional states. We observed 91% accuracy for Angry and Boredom emotional classes by using SVM and more than 96% accuracy using ANN and with an overall accuracy of 87.17% using SVM, 94% for ANN.

CLASSIFICATION OF ICTAL EEG USING MODELING BASED SPECTRAL AND TEMPORAL FEATURES ON INSTANTANEOUS AMPLITUDE-FREQUENCY COMPONENTS OF IMFs

2018 ◽  
Vol 30 (06) ◽  
pp. 1850042 ◽  
Author(s):  
K. S. Biju ◽  
M. G. Jibukumar
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Classification Accuracy ◽  
Spectral Features ◽  
Eeg Signal ◽  
Instantaneous Amplitude ◽  
Eeg Signals ◽  
Hilbert Huang Transform ◽  
Temporal Features ◽  
Power Spectral

In the present study, a method for classifying the different ictal stages in electroencephalogram (EEG) signals is proposed. The main symptoms of epilepsy are indicated by ictal activities, which trigger widespread neurological disorders other than stroke and thus affect the world population. In this work, a novel ictal classification method that combines the spectral and temporal features of twin components in Hilbert–Huang transform is proposed. Spectral features of instantaneous amplitude (IA) function are obtained based on the power spectral density of autoregressive (AR) modeling. Here four different cases of ictal activities of EEG signal are classified. In each case first and second intrinsic mode function of Hilbert–Huang transform are tabulated. The power spectral density of AR(6) and AR(10) model are done for IA1 and IA2 components of each case. Temporal features of either instantaneous frequency (IF) function or IA are computed. The feature vectors are tested in a well-known database of different classes in interictal, ictal, and normal activities of EEG signals. The discriminating power of each vector is evaluated through one-way analysis of variance, and the classification results are verified using an artificial neural network (ANN) classifier. The performance of the classifier was assessed in term of sensitivity, specificity, and total classification accuracy. The spectral features of the AR(10) of IA and the temporal features of IA yielded 100% accuracy, 100% sensitivity, and 100% specificity in the ictal classification. By contrast, these features obtained only 83.33% of the total classification accuracy in ictal and interictal EEG signal.

SPECTRAL FEATURES OF THE ALTERNATING CLUSTER PROCESS

2010 ◽  
Vol 09 (03) ◽  
pp. 301-312
Author(s):  
FERDINAND GRÜNEIS
Keyword(s):  
Spectral Density ◽  
Poisson Process ◽  
Power Spectral Density ◽  
Random Noise ◽  
Excess Noise ◽  
Spectral Features ◽  
Cluster Process ◽  
State Process ◽  
Power Spectral ◽  
The Impact

The alternating cluster process is a Poisson process the rate of which is modulated by an underlying two-state process. We derive the power spectral density of the alternating cluster process; besides random noise we obtain excess noise due to the impact of modulation.

scholarly journals Klasifikasi Sinyal EEG Dengan Power Spectra Density Berbasis Metode Welch Dan MLP Backpropagation

2019 ◽  
Vol 3 (1) ◽  
pp. 17-25
Author(s):  
Nursuci Putri Husain ◽  
Nurseno Bayu Aji
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Principle Component Analysis ◽  
Visual Analysis ◽  
Component Analysis ◽  
Eeg Signal ◽  
Multi Layer Perceptron ◽  
Principle Component ◽  
Power Spectral ◽  
Welch Method

Electroencephalogram (EEG) signal is a signal that could become an information for study about disorders of brain function  such as Epilepsi. EEG that detected in epileptic seizures produce patterns that allow doctors to distinguish it from normal conditions. However, a visual analysis can not be done continuously. This study proposed a new hybrid method of EEG signal classification using Power Spectral Density (PSD) based on Welch method, Principle Component Analysis (PCA), and Multi Layer Perceptron Backpropagation.There are 3 main stages in this study, firstly preprocessing the dataset of EEG signals by Power Spectral Density (PSD) based on Welch method, then Principle Component Analysis (PCA) as a method of  dimensionallity reduction of the EEG signal data and the Multi Layer Perceptron Backpropagation for classifying a signal. Based on experimental results, the proposed method is successfully obtain high accuracy for the 80-20% training-testing partition (99.68%).  

Fuzzy Modeling of Multi-Degree-of-Freedom Power Spectral Density Systems

2009 ◽  
Vol 2 (1) ◽  
pp. 40-47
Author(s):  
Montasser Tahat ◽  
Hussien Al-Wedyan ◽  
Kudret Demirli ◽  
Saad Mutasher
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Fuzzy Modeling ◽  
Degree Of Freedom ◽  
Power Spectral
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