HALF-WAVE SEGMENT FEATURE EXTRACTION OF EEG SIGNALS OF PATIENTS WITH DEPRESSION AND PERFORMANCE EVALUATION OF NEURAL NETWORK CLASSIFIERS

A detailed understanding of key signal characteristics has enabled the use of artificial neural networks (ANN) for feature detection and classification of EEG signals in clinical research. The present study is performed to classify EEG signals of normal and depression patients with wavelet parameters as key input features. The characteristics of depression cannot be made out by visual inspection of EEG records unlike epilepsy which is well characterized by sudden recurrent and transient waveforms. In this study, a comparison is made between the performance of feedforward neural network (FFNN) and probabilistic neural network (PNN) while classifying the EEG signals of normal and depression patients. Classification capabilities of both the methods are validated with the EEG recordings from 30 normal controls and 30 depression patients. One-way ANOVA provided a statistical significant difference between the two classes of EEG signals recorded. Preprocessing for feature extraction is done using discrete wavelet transform (DWT). The time domain and relative wavelet energy (RWE) features calculated from the sub-bands are given as a set of input to the neural network. Another set of feature used independently for training the network is the wavelet entropy (WE). The FFNN achieved a classification accuracy of 100% and PNN gave an accuracy of 58.75% with time domain and wavelet energy as the input features. With wavelet entropy as the input feature, FFNN further showed 98.75% classification accuracy while PNN gave an accuracy of only 46.5%. The results indicate that FFNN with the given input features is more suitable for the classification of EEG signals with mood changing depressive disorders.

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Automated Classification of Mental Arithmetic Tasks Using Recurrent Neural Network and Entropy Features Obtained from Multi-Channel EEG Signals

Electronics ◽

10.3390/electronics10091079 ◽

2021 ◽

Vol 10 (9) ◽

pp. 1079

Author(s):

Abhishek Varshney ◽

Samit Kumar Ghosh ◽

Sibasankar Padhy ◽

Rajesh Kumar Tripathy ◽

U. Rajendra Acharya

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Classification Accuracy ◽

Mental Arithmetic ◽

Cognitive Workload ◽

Automated Classification ◽

Eeg Signals ◽

Arithmetic Task ◽

Mental Arithmetic Task

The automated classification of cognitive workload tasks based on the analysis of multi-channel EEG signals is vital for human–computer interface (HCI) applications. In this paper, we propose a computerized approach for categorizing mental-arithmetic-based cognitive workload tasks using multi-channel electroencephalogram (EEG) signals. The approach evaluates various entropy features, such as the approximation entropy, sample entropy, permutation entropy, dispersion entropy, and slope entropy, from each channel of the EEG signal. These features were fed to various recurrent neural network (RNN) models, such as long-short term memory (LSTM), bidirectional LSTM (BLSTM), and gated recurrent unit (GRU), for the automated classification of mental-arithmetic-based cognitive workload tasks. Two cognitive workload classification strategies (bad mental arithmetic calculation (BMAC) vs. good mental arithmetic calculation (GMAC); and before mental arithmetic calculation (BFMAC) vs. during mental arithmetic calculation (DMAC)) are considered in this work. The approach was evaluated using the publicly available mental arithmetic task-based EEG database. The results reveal that our proposed approach obtained classification accuracy values of 99.81%, 99.43%, and 99.81%, using the LSTM, BLSTM, and GRU-based RNN classifiers, respectively for the BMAC vs. GMAC cognitive workload classification strategy using all entropy features and a 10-fold cross-validation (CV) technique. The slope entropy features combined with each RNN-based model obtained higher classification accuracy compared with other entropy features for the classification of the BMAC vs. GMAC task. We obtained the average classification accuracy values of 99.39%, 99.44%, and 99.63% for the classification of the BFMAC vs. DMAC tasks, using the LSTM, BLSTM, and GRU classifiers with all entropy features and a hold-out CV scheme. Our developed automated mental arithmetic task system is ready to be tested with more databases for real-world applications.

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Best Feature Extraction and Classification Algorithms for EEG Signals in Neuromarketing

Frontiers in Biomedical Technologies ◽

10.18502/fbt.v7i3.4621 ◽

2020 ◽

Author(s):

Jafar Zamani ◽

Ali Boniadi Naieni

Keyword(s):

Feature Extraction ◽

Brain Activity ◽

Support Vector ◽

High Temporal Resolution ◽

Discrete Wavelet ◽

Eeg Signal ◽

Eeg Signals ◽

High Efficient ◽

The Brain

Purpose: There are many methods for advertisements of products and neuromarketing is new area in this field. In neuromarketing, we use neuroscience information for revealing Consumer behavior by extracting brain activity. Functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG), and Electroencephalography (EEG) are high efficient tools for investigating the brain activity in neuromarketing. EEG signal is a high temporal resolution and a cheap method for examining the brain activity. Materials and Methods: 32 subjects (16 males and 16 females) aging between 20-35 years old participated in this study. We proposed neuromarketing method exploit EEG system for predicting consumer preferences while they view E-commerce products. We apply some important preprocessing steps for noise and artifacts elimination of the EEG signal. In next step feature extraction methods are applied on the EEG data such as Discrete Wavelet Transform (DWT) and statistical features. The goal of this study is classification of analyzed EEG signal to likes and dislikes using supervised algorithms. We use Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF) for data classification. The mentioned methods were used for whole and lobe brain data. Results: The results show high efficacy for SVM algorithms than other methods. Accuracy, sensitivity, specificity and precision parameters were used for evaluation of the model performance. The results show high performance of SVM algorithms for classification of the data with accuracy more than 87% and 84% for whole and parietal lobe data. Conclusion: We designed a tool with EEG signals for extraction brain activity of consumers using neuromarketing methods. We investigated the effects of advertising on brain activity of consumers by EEG signals measures.

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Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

Complexity ◽

10.1155/2018/9385947 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 44

Author(s):

Vladimir A. Maksimenko ◽

Semen A. Kurkin ◽

Elena N. Pitsik ◽

Vyacheslav Yu. Musatov ◽

Anastasia E. Runnova ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Motor Imagery ◽

High Frequency ◽

Classification Accuracy ◽

Eeg Signals ◽

Pass Filter ◽

Time Frequency ◽

Artificial Neural

We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10-10 system, we select an appropriate type of ANN which reaches 80 ± 10% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8–13 Hz) and delta (1–5 Hz) brainwaves than in the high-frequency beta brainwave (15–30 Hz). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs. We demonstrate that the filtration of high-frequency spectral components significantly enhances the classification performance (up to 90 ± 5% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain-computer interfaces for untrained subjects.

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CLASSIFICATION OF EEG SIGNALS IN NORMAL AND DEPRESSION CONDITIONS BY ANN USING RWE AND SIGNAL ENTROPY

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519412400192 ◽

2012 ◽

Vol 12 (04) ◽

pp. 1240019 ◽

Cited By ~ 35

Author(s):

SUBHA D. PUTHANKATTIL ◽

PAUL K. JOSEPH

Keyword(s):

Neural Network ◽

Signal Processing ◽

Automated Analysis ◽

Complex Nature ◽

Discrete Wavelet ◽

Frequency Noise ◽

Eeg Signals ◽

Detailed Assessment ◽

Parseval’S Theorem

EEG is useful for the analysis of the functional activity of the brain and a detailed assessment of this non-stationary waveform can provide crucial parameters indicative of the mental state of patients. The complex nature of EEG signals calls for automated analysis using various signal processing methods. This paper attempts to classify the EEG signals of normal and depression patients using well-established signal processing techniques involving relative wavelet energy (RWE) and artificial feedForward neural network. High frequency noise present in the recorded signal is removed using total variation filtering (TVF). Classification of the frequency bands of EEG signals into appropriate detail levels and approximation level is carried out using an eight-level multiresolution decomposition method of discrete wavelet transform (DWT). Parseval's theorem is used for calculating the energy at different resolution levels. RWE analysis gives information about the signal energy distribution at different decomposition levels. Both RWE and feedforward Network are used to classify the signals from normal controls and depression patients. The performance of the artificial neural network was evaluated using the classification accuracy and its value of 98.11% indicates a great potential for classifying normal and depression signals.

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Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data

Journal of Electrical and Computer Engineering ◽

10.1155/2014/347307 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 10

Author(s):

Qingqing Lu ◽

Jiexin Pu ◽

Zhonghua Liu

Keyword(s):

Feature Extraction ◽

Ground Penetrating Radar ◽

Classification Accuracy ◽

Support Vector ◽

Discrete Wavelet ◽

Noisy Environment ◽

Feature Based ◽

Approximation Coefficients ◽

Ground Penetrating

Ground penetrating radar (GPR) is a powerful tool for detecting objects buried underground. However, the interpretation of the acquired signals remains a challenging task since an experienced user is required to manage the entire operation. Particularly difficult is the classification of the material type of underground objects in noisy environment. This paper proposes a new feature extraction method. First, discrete wavelet transform (DWT) transforms A-Scan data and approximation coefficients are extracted. Then, fractional Fourier transform (FRFT) is used to transform approximation coefficients into fractional domain and we extract features. The features are supplied to the support vector machine (SVM) classifiers to automatically identify underground objects material. Experiment results show that the proposed feature-based SVM system has good performances in classification accuracy compared to statistical and frequency domain feature-based SVM system in noisy environment and the classification accuracy of features proposed in this paper has little relationship with the SVM models.

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Classification of alzheimer's disease subjects from MRI using fuzzy neural network with feature extraction using discrete wavelet transform

Biomedical Research ◽

10.4066/biomedicalresearch.29-16-2319 ◽

2018 ◽

Cited By ~ 1

Author(s):

Geetha C ◽

Pugazhenthi D

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Feature Extraction ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Fuzzy Neural Network ◽

Discrete Wavelet ◽

Fuzzy Neural

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Classification of emotions from EEG signals using time-order representation based on the S-transform and convolutional neural network

Electronics Letters ◽

10.1049/el.2020.2380 ◽

2020 ◽

Vol 56 (25) ◽

pp. 1359-1361

Author(s):

S.K. Khare ◽

A. Nishad ◽

A. Upadhyay ◽

V. Bajaj

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Eeg Signals ◽

S Transform ◽

Time Order ◽

Order Representation

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Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽

10.3390/s19040916 ◽

2019 ◽

Vol 19 (4) ◽

pp. 916 ◽

Cited By ~ 2

Author(s):

Wen Cao ◽

Chunmei Liu ◽

Pengfei Jia

Keyword(s):

Feature Extraction ◽

Kernel Function ◽

Electronic Nose ◽

Classification Accuracy ◽

Extraction Methods ◽

Object Function ◽

Optimal Value ◽

Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

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