scholarly journals DeePay: Deep Learning Decodes EEG to Predict Consumer’s Willingness to Pay for Neuromarketing

2021 ◽  
Author(s):  
Adam Hakim ◽  
Itamar Golan ◽  
Sharon Yefet ◽  
Dino J. Levy
Keyword(s):  
Decision Making ◽  
Feature Extraction ◽  
Deep Learning ◽  
Neural Mechanism ◽  
Intrinsic Noise ◽  
Data Sets ◽  
Consumer Neuroscience ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Increasing Demand

<div> <p>There is an increasing demand within consumer-neuroscience (or neuromarketing) for objective neural measures to quantify consumers’ preferences and predict responses to marketing campaigns. However, the properties of EEG datasets raise various difficulties when performing predictions on them, such as the small size of data sets, high dimensionality, the need for elaborate feature extraction, intrinsic noise, and unpredictable between-subject variations. We aimed to overcome these limitations by combining unique techniques within a Deep Learning (DL) framework, while providing interpretable results for neuroscientific and decision-making insight. In this study, we developed a DL model to predict subject-specific preferences based on their EEG data. In each trial, 213 subjects observed a product’s image, out of 72 possible products, and then reported how much they were willing to pay (WTP) for the product. The DL employed EEG recordings from product observation to predict the corresponding reported WTP values. Our results showed 75.09% accuracy in predicting high vs. low WTP, surpassing other models and a manual feature extraction approach. Meanwhile, network visualizations provided the predictive frequencies of neural activity and their scalp distributions, shedding light on the neural mechanism involved with evaluation. In conclusion, we show that DLNs may be the superior method to perform EEG-based predictions, to the benefit of decision-making researchers and marketing practitioners alike.</p> </div> <br>

scholarly journals DeePay: Deep Learning Decodes EEG to Predict Consumer’s Willingness to Pay for Neuromarketing

2021 ◽  
Author(s):  
Adam Hakim ◽  
Itamar Golan ◽  
Sharon Yefet ◽  
Dino J. Levy
Keyword(s):  
Decision Making ◽  
Feature Extraction ◽  
Deep Learning ◽  
Neural Mechanism ◽  
Intrinsic Noise ◽  
Data Sets ◽  
Consumer Neuroscience ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Increasing Demand

<div> <p>There is an increasing demand within consumer-neuroscience (or neuromarketing) for objective neural measures to quantify consumers’ preferences and predict responses to marketing campaigns. However, the properties of EEG datasets raise various difficulties when performing predictions on them, such as the small size of data sets, high dimensionality, the need for elaborate feature extraction, intrinsic noise, and unpredictable between-subject variations. We aimed to overcome these limitations by combining unique techniques within a Deep Learning (DL) framework, while providing interpretable results for neuroscientific and decision-making insight. In this study, we developed a DL model to predict subject-specific preferences based on their EEG data. In each trial, 213 subjects observed a product’s image, out of 72 possible products, and then reported how much they were willing to pay (WTP) for the product. The DL employed EEG recordings from product observation to predict the corresponding reported WTP values. Our results showed 75.09% accuracy in predicting high vs. low WTP, surpassing other models and a manual feature extraction approach. Meanwhile, network visualizations provided the predictive frequencies of neural activity and their scalp distributions, shedding light on the neural mechanism involved with evaluation. In conclusion, we show that DLNs may be the superior method to perform EEG-based predictions, to the benefit of decision-making researchers and marketing practitioners alike.</p> </div> <br>

scholarly journals DeePay: Deep Learning Decodes EEG to Predict Consumer’s Willingness to Pay for Neuromarketing

2021 ◽  
Author(s):  
Adam Hakim ◽  
Itamar Golan ◽  
Sharon Yefet ◽  
Dino J. Levy
Keyword(s):  
Decision Making ◽  
Feature Extraction ◽  
Deep Learning ◽  
Neural Mechanism ◽  
Intrinsic Noise ◽  
Data Sets ◽  
Consumer Neuroscience ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Increasing Demand

<div> <p>There is an increasing demand within consumer-neuroscience (or neuromarketing) for objective neural measures to quantify consumers’ preferences and predict responses to marketing campaigns. However, the properties of EEG datasets raise various difficulties when performing predictions on them, such as the small size of data sets, high dimensionality, the need for elaborate feature extraction, intrinsic noise, and unpredictable between-subject variations. We aimed to overcome these limitations by combining unique techniques within a Deep Learning (DL) framework, while providing interpretable results for neuroscientific and decision-making insight. In this study, we developed a DL model to predict subject-specific preferences based on their EEG data. In each trial, 213 subjects observed a product’s image, out of 72 possible products, and then reported how much they were willing to pay (WTP) for the product. The DL employed EEG recordings from product observation to predict the corresponding reported WTP values. Our results showed 75.09% accuracy in predicting high vs. low WTP, surpassing other models and a manual feature extraction approach. Meanwhile, network visualizations provided the predictive frequencies of neural activity and their scalp distributions, shedding light on the neural mechanism involved with evaluation. In conclusion, we show that DLNs may be the superior method to perform EEG-based predictions, to the benefit of decision-making researchers and marketing practitioners alike.</p> </div> <br>

scholarly journals Early Classification of Motor Tasks Using Dynamic Functional Connectivity Graphs from EEG

2020 ◽  
Author(s):  
Foroogh Shamsi ◽  
Ali Haddad ◽  
Laleh Najafizadeh
Keyword(s):  
Feature Extraction ◽  
Functional Connectivity ◽  
Extraction Method ◽  
Motor Tasks ◽  
Dynamic Functional Connectivity ◽  
Feature Extraction Method ◽  
Brain Functions ◽  
Eeg Data ◽  
Eeg Recordings

AbstractObjectiveClassification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem.ApproachThe proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a Long Short Term Memory (LSTM) classifier is employed for classification.Main resultsFeatures extracted from various durations of post-stimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% about only 500 ms after stimulus presentation.SignificanceOur results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using a short interval of the data in the order of hundreds of milliseconds (e.g. 500 ms).This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.

scholarly journals Deep Learning for Screening COVID-19 using Chest X-Ray Images

2020 ◽  
Author(s):  
Sanhita Basu ◽  
Sushmita Mitra ◽  
Nilanjan Saha
Keyword(s):  
Deep Learning ◽  
Lung Parenchyma ◽  
Clinical Findings ◽  
Data Sets ◽  
X Rays ◽  
X Ray ◽  
Chest X Ray ◽  
Increasing Demand ◽  
Novel Coronavirus ◽  
Diverse Data

AbstractWith the ever increasing demand for screening millions of prospective “novel coronavirus” or COVID-19 cases, and due to the emergence of high false negatives in the commonly used PCR tests, the necessity for probing an alternative simple screening mechanism of COVID-19 using radiological images (like chest X-Rays) assumes importance. In this scenario, machine learning (ML) and deep learning (DL) offer fast, automated, effective strategies to detect abnormalities and extract key features of the altered lung parenchyma, which may be related to specific signatures of the COVID-19 virus. However, the available COVID-19 datasets are inadequate to train deep neural networks. Therefore, we propose a new concept called domain extension transfer learning (DETL). We employ DETL, with pre-trained deep convolutional neural network, on a related large chest X-Ray dataset that is tuned for classifying between four classes viz. normal, other_disease, pneumonia and Covid — 19. A 5-fold cross validation is performed to estimate the feasibility of using chest X-Rays to diagnose COVID-19. The initial results show promise, with the possibility of replication on bigger and more diverse data sets. The overall accuracy was measured as 95.3% ± 0.02. In order to get an idea about the COVID-19 detection transparency, we employed the concept of Gradient Class Activation Map (Grad-CAM) for detecting the regions where the model paid more attention during the classification. This was found to strongly correlate with clinical findings, as validated by experts.

A Study on Deep Learning Methods in the Concept of Digital Industry 4.0

2021 ◽  
pp. 318-339
Author(s):  
Mehmet Ali Şimşek ◽  
Zeynep Orman
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Deep Learning ◽  
Industry 4.0 ◽  
Production Efficiency ◽  
Learning Technologies ◽  
High Potential ◽  
Data Sets ◽  
Learning Methods ◽  
Intelligence Studies

Nowadays, the main features of Industry 4.0 are interpreted to the ability of machines to communicate with each other and with a system, increasing the production efficiency and development of the decision-making mechanisms of robots. In these cases, new analytical algorithms of Industry 4.0 are needed. By using deep learning technologies, various industrial challenging problems in Industry 4.0 can be solved. Deep learning provides algorithms that can give better results on datasets owing to hidden layers. In this chapter, deep learning methods used in Industry 4.0 are examined and explained. In addition, data sets, metrics, methods, and tools used in the previous studies are explained. This study can lead to artificial intelligence studies with high potential to accelerate the implementation of Industry 4.0. Therefore, the authors believe that it will be very useful for researchers and practitioners who want to do research on this topic.

scholarly journals Towards Mapping Images to Text Using Deep-Learning Architectures

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1606
Author(s):  
Daniela Onita ◽  
Adriana Birlutiu ◽  
Liviu P. Dinu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Image Features ◽  
The Other ◽  
Data Sets ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Pixel Value

Images and text represent types of content that are used together for conveying a message. The process of mapping images to text can provide very useful information and can be included in many applications from the medical domain, applications for blind people, social networking, etc. In this paper, we investigate an approach for mapping images to text using a Kernel Ridge Regression model. We considered two types of features: simple RGB pixel-value features and image features extracted with deep-learning approaches. We investigated several neural network architectures for image feature extraction: VGG16, Inception V3, ResNet50, Xception. The experimental evaluation was performed on three data sets from different domains. The texts associated with images represent objective descriptions for two of the three data sets and subjective descriptions for the other data set. The experimental results show that the more complex deep-learning approaches that were used for feature extraction perform better than simple RGB pixel-value approaches. Moreover, the ResNet50 network architecture performs best in comparison to the other three deep network architectures considered for extracting image features. The model error obtained using the ResNet50 network is less by approx. 0.30 than other neural network architectures. We extracted natural language descriptors of images and we made a comparison between original and generated descriptive words. Furthermore, we investigated if there is a difference in performance between the type of text associated with the images: subjective or objective. The proposed model generated more similar descriptions to the original ones for the data set containing objective descriptions whose vocabulary is simpler, bigger and clearer.

Abstract 15042: Heart Rhythm Classification From an Optimal Lead Subset of the 12-lead Electrocardiogram by Deep Learning

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Changxin Lai ◽  
Shijie Zhou ◽  
Natalia Trayanova
Keyword(s):  
Decision Making ◽  
Feature Extraction ◽  
Deep Learning ◽  
Heart Rhythm ◽  
Significant Loss ◽  
Optimal Subset ◽  
Proposed Model ◽  
Validation Set ◽  
Ecg Leads ◽  
Electrocardiogram Ecg

Introduction: Deep learning (DL) has achieved promising performance on common heart rhythms classification using 12-lead electrocardiogram (ECG). However, two major concerns hinder the DL’s application - lack of interpretability and overfitting caused by using the full 12-lead ECG as input. Objective: We proposed a hybrid DL model with enhanced interpretability to detect 9 common types of heart rhythms from an optimal ECG lead subset, and to quantitively analyze the overfitting. Methods: We used a multicenter dataset of 6,877 annotated 12-lead ECG recordings. The proposed model (Fig. 1A) consists of a feature extraction and a decision-making. The feature extraction used 12 separate neural networks to extract features from each lead. The features were then fed into a random-forest classifier in the decision-making step to classify heart-rhythm types. The classifier was used to interpret the correlations between the heart rhythms and the ECG leads, to find an optimal subset of ECG leads, and to analyze whether using 12-lead ECG added unnecessary complexity to the model and undermined its generalizability. Results: The proposed model detected the correlations between the heart-rhythm types and the ECG leads (Fig. 1B), and identified an optimal ECG lead subset (leads II, aVR, V1, V4). The optimal subset was, in comparison with using 12-lead ECG, significantly better (F1 =0.776 vs. F1 = 0.767, P=0.02) on the validation set for classifying the 9 common heart rhythms. There was no statistical difference on the test set. No overfitting caused by 12-lead ECG was detected in this study. Conclusion: The hybrid DL model based on an optimal 4-lead ECG can interpret rhythm types without significant loss of accuracy in comparison with the 12-lead ECG.

Dynamical Feature Extraction from Brain Activity Time Series

2011 ◽  
pp. 729-735 ◽  
Author(s):  
Chang-Chia Liu ◽  
W. Art Chaovalitwongse ◽  
Panos M. Pardalos ◽  
Basim M. Uthman
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Feature Extraction ◽  
Protein Structure ◽  
Brain Activity ◽  
Data Sets ◽  
Eeg Classification ◽  
Data Mining Techniques ◽  
Dynamical Features ◽  
Eeg Recordings

Neurologists typically study the brain activity through acquired biomarker signals such as Electroencephalograms (EEGs) which have been widely used to capture the interactions between neurons or groups of neurons. Detecting and identifying the abnormal patterns through visual inspection of EEG signals are extremely challenging and require constant attention for well trained and experienced specialists. To resolve this problem, data mining techniques have been successfully applied to the analysis for EEG recordings and other biomarker data sets. For example, Chaovalitwongse et al., (2006; 2007), Prokopyev et al., (2007) and Pardalos et al., (2007) reported the EEG patterns can be classified through dynamical features extracted from the underlying EEG dynamical characteristics. Moreover, in the area of cancer research, Busygin et al., (2006) showed promising results via Bi-clustering data classification technique using selected features from DNA microarrays. Ceci et al., (2007); Krishnamoorthy et al., (2007) also reported that data mining techniques enable protein structure characterization and protein structure prediction. From data mining aspects, feature extraction and selection for time series data sets not only play an important role in data preprocessing but also provide opportunities to uncover the underlying mechanisms of data under study. It also keeps the essential data structure and makes a better representation of acquired data sets that need to be classified. In this work, the properties and descriptions of the most common neurological biomarker namely EEG data sets will be given as well as the motivations and challenges for abnormal EEG classification. The dynamical features for EEG classification will be reviewed and described in the second part of this work. The described dynamical features can also be applied to other types of classification applications for discovering the useful knowledge from obtained data sets. Finally, the potential applications for EEG classification will be discussed and comments for further research directions will be given in the future trends and conclusion sections.

scholarly journals Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

2020 ◽  
Vol 8 (5) ◽  
pp. 1160-1166
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Image Preprocessing ◽  
Learning Techniques ◽  
Component Extraction ◽  
Single Lesion

In this paper existing writing for computer added diagnosis (CAD) based identification of lesions that might be connected in the early finding of Diabetic Retinopathy (DR) is talked about. The recognition of sores, for example, Microaneurysms (MA), Hemorrhages (HEM) and Exudates (EX) are incorporated in this paper. A range of methodologies starting from conventional morphology to deep learning techniques have been discussed. The different strategies like hand crafted feature extraction to automated CNN based component extraction, single lesion identification to multi sore recognition have been explored. The different stages in each methods beginning from the image preprocessing to classification stage are investigated. The exhibition of the proposed strategies are outlined by various performance measurement parameters and their used data sets are tabulated. Toward the end we examined the future headings.

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