scholarly journals Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5456
Author(s):  
Hamid Mukhtar ◽  
Saeed Mian Qaisar ◽  
Atef Zaguia
Keyword(s):  
Neural Networks ◽  
Performance Comparison ◽  
Complex Nature ◽  
Normal Person ◽  
Neuronal System ◽  
Classification Problems ◽  
Eeg Signals ◽  
Average Accuracy ◽  
Regularization Techniques ◽  
Using Data

Alcoholism is attributed to regular or excessive drinking of alcohol and leads to the disturbance of the neuronal system in the human brain. This results in certain malfunctioning of neurons that can be detected by an electroencephalogram (EEG) using several electrodes on a human skull at appropriate positions. It is of great interest to be able to classify an EEG activity as that of a normal person or an alcoholic person using data from the minimum possible electrodes (or channels). Due to the complex nature of EEG signals, accurate classification of alcoholism using only a small dataset is a challenging task. Artificial neural networks, specifically convolutional neural networks (CNNs), provide efficient and accurate results in various pattern-based classification problems. In this work, we apply CNN on raw EEG data and demonstrate how we achieved 98% average accuracy by optimizing a baseline CNN model and outperforming its results in a range of performance evaluation metrics on the University of California at Irvine Machine Learning (UCI-ML) EEG dataset. This article explains the stepwise improvement of the baseline model using the dropout, batch normalization, and kernel regularization techniques and provides a comparison of the two models that can be beneficial for aspiring practitioners who aim to develop similar classification models in CNN. A performance comparison is also provided with other approaches using the same dataset.

scholarly journals A Hybrid EEG-Based Emotion Recognition Approach Using Wavelet Convolutional Neural Networks (WCNN) and Support Vector Machine

2021 ◽  
pp. 1-29
Author(s):  
Sara Bagherzadeh ◽  
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Convolutional Neural Networks ◽  
Hybrid Approach ◽  
Support Vector ◽  
Emotional States ◽  
Eeg Signals ◽  
Color Representation ◽  
Time Frequency ◽  
Average Accuracy

Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool which makes processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate. In this paper, a hybrid approach based on deep features extracted from Wavelet CNNs (WCNNs) weighted layers and multiclass support vector machine (MSVM) is proposed to improve recognition of emotional states from electroencephalogram (EEG) signals. First, EEG signals were preprocessed and converted to time-frequency (T-F) color representation or scalogram using the continuous wavelet transform (CWT) method. Then, scalograms were fed into four popular pre-trained CNNs, AlexNet, ResNet-18, VGG-19 and Inception-v3 to fine-tune them. Then, the best feature layer from each one was used as input to the MSVM method to classify four quarters of the valence-arousal model. Finally, subject-independent Leave-One-Subject-Out criterion was used to evaluate the proposed method on DEAP and MAHNOB-HCI databases. Results show that extracting deep features from the earlier convolutional layer of ResNet-18 (Res2a) and classifying using the MSVM increases the average accuracy, precision and recall about 20% and 12% for MAHNOB-HCI and DEAP databases, respectively. Also, combining scalograms from four regions of pre-frontal, frontal, parietal and parietal-occipital and two regions of frontal and parietal achieved the higher average accuracy of 77.47% and 87.45% for MAHNOB-HCI and DEAP databases, respectively. Combining CNN and MSVM increased recognition of emotion from EEG signal and results were comparable to state-of-the-art studies.

scholarly journals Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Ángel Morera ◽  
Ángel Sánchez ◽  
José Francisco Vélez ◽  
Ana Belén Moreno
Keyword(s):  
Neural Networks ◽  
Experimental Study ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
Classification Problem ◽  
Gender Classification ◽  
Network Configuration ◽  
Classification Problems ◽  
Design Complexity ◽  
Average Accuracy

Demographic handwriting-based classification problems, such as gender and handedness categorizations, present interesting applications in disciplines like Forensic Biometrics. This work describes an experimental study on the suitability of deep neural networks to three automatic demographic problems: gender, handedness, and combined gender-and-handedness classifications, respectively. Our research was carried out on two public handwriting databases: the IAM dataset containing English texts and the KHATT one with Arabic texts. The considered problems present a high intrinsic difficulty when extracting specific relevant features for discriminating the involved subclasses. Our solution is based on convolutional neural networks since these models had proven better capabilities to extract good features when compared to hand-crafted ones. Our work also describes the first approach to the combined gender-and-handedness prediction, which has not been addressed before by other researchers. Moreover, the proposed solutions have been designed using a unique network configuration for the three considered demographic problems, which has the advantage of simplifying the design complexity and debugging of these deep architectures when handling related handwriting problems. Finally, the comparison of achieved results to those presented in related works revealed the best average accuracy in the gender classification problem for the considered datasets.

scholarly journals A hybrid deep neural network for classification of schizophrenia using EEG Data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Sun ◽  
Rui Cao ◽  
Mengni Zhou ◽  
Waqar Hussain ◽  
Bin Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Spatial Information ◽  
Short Term Memory ◽  
Healthy Controls ◽  
Eeg Signals ◽  
Brain Topography ◽  
Average Accuracy ◽  
Schizophrenic Patients ◽  
Rgb Images

AbstractSchizophrenia is a serious mental illness that causes great harm to patients, so timely and accurate detection is essential. This study aimed to identify a better feature to represent electroencephalography (EEG) signals and improve the classification accuracy of patients with schizophrenia and healthy controls by using EEG signals. Our research method involves two steps. First, the EEG time series is preprocessed, and the extracted time-domain and frequency-domain features are transformed into a sequence of red–green–blue (RGB) images that carry spatial information. Second, we construct hybrid deep neural networks (DNNs) that combine convolution neural networks and long short-term memory to address RGB images to classify schizophrenic patients and healthy controls. The results show that the fuzzy entropy (FuzzyEn) feature is more significant than the fast Fourier transform (FFT) feature in brain topography. The deep learning (DL) method that we propose achieves an average accuracy of 99.22% with FuzzyEn and an average accuracy of 96.34% with FFT. These results show that the best effect is to extract fuzzy features as input features from EEG time series and then use a hybrid DNN for classification. Compared with the most advanced methods in this field, significant improvements have been achieved.

scholarly journals Artificial Neural Networks for Resources Optimization in Energetic Environment

2021 ◽  
Author(s):  
Gianni D'Angelo ◽  
Francesco Palmieri ◽  
Antonio Robustelli
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Resource Planning ◽  
Training Dataset ◽  
Software Products ◽  
Planning Optimization ◽  
Average Accuracy ◽  
Common Task ◽  
Artificial Neural ◽  
Using Data

Abstract Resource Planning Optimization (RPO) is a common task that many companies need to face to obtain several benefits, like budget improvements and run-time analyses. It is often addressed by using several software products and tools, based on sophisticated mathematical artifacts. However, these tools are not able to provide a practical solution because they are often expensive and time-consuming. On the other hand, Artificial Intelligence-based approaches have been increasingly used in many industrial and scientific fields in last decades, and have demonstrated to be a valid alternative to the classical mathematical-based methods. For this purpose, the following paper aims to investigate the use of multiple Artificial Neural Networks (ANNs) for solving a RPO problem related to the scheduling of different Combined Heat & Power (CHP) generators. The experimental results, carried out by using data extracted by considering a real Microgrid system, have confirmed the effectiveness of the proposed approach. Additionally, we show that multiple neural networks achieve up to a 6% improvement in average accuracy over Naive Bayes classifier, up to a 12% over Multi-Layer Perceptron classifier and up to a 13% over state-of-the-art ANNs in the presence of unbalanced training dataset.

scholarly journals Hyperparameter Optimization and Regularization on Fashion-MNIST Classification

2019 ◽  
Vol 8 (2) ◽  
pp. 3713-3719
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Data Augmentation ◽  
Network Models ◽  
Classification Problems ◽  
Regularization Techniques ◽  
Cropping Rotation ◽  
The Impact ◽  
Deep Learning Model ◽  
Learning In Computer Vision

Nowadays the most exciting technology breakthrough has been the rise of the deep learning. In computer vision Convolutional Neural Networks (CNN or ConvNet) are the default deep learning model used for image classification problems. In these deep network models, feature extraction is figure out by itself and these models tend to perform well with huge amount of samples. Herein we explore the impact of various Hyper-Parameter Optimization (HPO) methods and regularization techniques with deep neural networks on FashionMNIST (F-MNIST) dataset which is proposed by Zalando Research. We have proposed deep ConvNet architectures with Data Augmentation and explore the impact of this by configuring the hyperparameters and regularization methods. As deep learning requires a lots of data, the insufficiency of image samples can be expand through various data augmentation methods like Cropping, Rotation, Flipping, and Shifting. The experimental results show impressive results on this new benchmarking dataset F-MNIST

Neural networks trained with high-dimensional functions approximation data in high-dimensional space

2021 ◽  
pp. 1-12
Author(s):  
Jian Zheng ◽  
Jianfeng Wang ◽  
Yanping Chen ◽  
Shuping Chen ◽  
Jingjin Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Dimensional Space ◽  
Data Distribution ◽  
High Dimensional ◽  
Sufficient Information ◽  
Sufficient Data ◽  
High Dimensional Space ◽  
Positive Effects ◽  
The Neural Networks ◽  
Using Data

Neural networks can approximate data because of owning many compact non-linear layers. In high-dimensional space, due to the curse of dimensionality, data distribution becomes sparse, causing that it is difficulty to provide sufficient information. Hence, the task becomes even harder if neural networks approximate data in high-dimensional space. To address this issue, according to the Lipschitz condition, the two deviations, i.e., the deviation of the neural networks trained using high-dimensional functions, and the deviation of high-dimensional functions approximation data, are derived. This purpose of doing this is to improve the ability of approximation high-dimensional space using neural networks. Experimental results show that the neural networks trained using high-dimensional functions outperforms that of using data in the capability of approximation data in high-dimensional space. We find that the neural networks trained using high-dimensional functions more suitable for high-dimensional space than that of using data, so that there is no need to retain sufficient data for neural networks training. Our findings suggests that in high-dimensional space, by tuning hidden layers of neural networks, this is hard to have substantial positive effects on improving precision of approximation data.

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