Detection of 2D and 3D Video Transitions Based on EEG Power

2020 ◽  
Author(s):  
Negin Manshouri ◽  
Mesut Melek ◽  
Temel Kayıkcıoglu
Keyword(s):  
Steady State ◽  
Support Vector ◽  
Success Rates ◽  
K Nearest Neighbors ◽  
3D Vision ◽  
Linear Discriminant ◽  
Parietal Lobes ◽  
Brain Signals ◽  
Sudden Transition ◽  
2D And 3D

Abstract Despite the long and extensive history of 3D technology, it has recently attracted the attention of researchers. This technology has become the center of interest of young people because of the real feelings and sensations it creates. People see their environment as 3D because of their eye structure. In this study, it is hypothesized that people lose their perception of depth during sleepy moments and that there is a sudden transition from 3D vision to 2D vision. Regarding these transitions, the EEG signal analysis method was used for deep and comprehensive analysis of 2D and 3D brain signals. In this study, a single-stream anaglyph video of random 2D and 3D segments was prepared. After watching this single video, the obtained EEG recordings were considered for two different analyses: the part involving the critical transition (transition state) and the state analysis of only the 2D versus 3D or 3D versus 2D parts (steady state). The main objective of this study is to see the behavioral changes of brain signals in 2D and 3D transitions. To clarify the impacts of the human brain’s power spectral density (PSD) in 2D-to-3D (2D_3D) and 3D-to-2D (3D_2D) transitions of anaglyph video, nine visual healthy individuals were prepared for testing in this pioneering study. Spectrogram graphs based on short time Fourier transform (STFT) were considered to evaluate the power spectrum analysis in each EEG channel of transition or steady state. Thus, in 2D and 3D transition scenarios, important channels representing EEG frequency bands and brain lobes will be identified. To classify the 2D and 3D transitions, the dominant bands and time intervals representing the maximum difference of PSD were selected. Afterward, effective features were selected by applying statistical methods such as standard deviation, maximum (max) and Hjorth parameters to epochs indicating transition intervals. Ultimately, k-nearest neighbors, support vector machine and linear discriminant analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The frontal, temporal and partially parietal lobes show 2D_3D and 3D_2D transitions with a good classification success rate. Overall, it was found that Hjorth parameters and LDA algorithms have 71.11% and 77.78% classification success rates for transition and steady state, respectively.

scholarly journals Detection of 2D and 3D Video Transitions Based on EEG Power

2020 ◽  
Author(s):  
Negin Manshouri ◽  
Mesut Melek ◽  
Temel Kayikcioglu
Keyword(s):  
Steady State ◽  
Support Vector ◽  
Success Rates ◽  
K Nearest Neighbors ◽  
3D Vision ◽  
Linear Discriminant ◽  
Parietal Lobes ◽  
Brain Signals ◽  
Sudden Transition ◽  
2D And 3D

Despite the long and extensive history of 3D technology, it has recently attracted the attention of researchers. This technology has become the center of interest of young people because of the real feelings and sensations it creates. People see their environment as 3D because of their eye structure. In this study, it is hypothesized that people lose their perception of depth during sleepy moments and that there is a sudden transition from 3D vision to 2D vision. Regarding these transitions, the EEG signal analysis method was used for deep and comprehensive analysis of 2D and 3D brain signals. In this study, a single-stream anaglyph video of random 2D and 3D segments was prepared. After watching this single video, the obtained EEG recordings were considered for two different analyses: the part involving the critical transition (transition-state) and the state analysis of only the 2D versus 3D or 3D versus 2D parts (steady-state). The main objective of this study is to see the behavioral changes of brain signals in 2D and 3D transitions. To clarify the impacts of the human brain’s power spectral density (PSD) in 2D-to-3D (2D_3D) and 3D-to-2D (3D_2D) transitions of anaglyph video, 9 visual healthy individuals were prepared for testing in this pioneering study. Spectrogram graphs based on Short Time Fourier transform (STFT) were considered to evaluate the power spectrum analysis in each EEG channel of transition or steady-state. Thus, in 2D and 3D transition scenarios, important channels representing EEG frequency bands and brain lobes will be identified. To classify the 2D and 3D transitions, the dominant bands and time intervals representing the maximum difference of PSD were selected. Afterward, effective features were selected by applying statistical methods such as standard deviation (SD), maximum (max), and Hjorth parameters to epochs indicating transition intervals. Ultimately, k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The frontal, temporal, and partially parietal lobes show 2D_3D and 3D_2D transitions with a good classification success rate. Overall, it was found that Hjorth parameters and LDA algorithms have 71.11% and 77.78% classification success rates for transition and steady-state, respectively.

scholarly journals Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller

2021 ◽  
Vol 11 (23) ◽  
pp. 11252
Author(s):  
Ayana Mussabayeva ◽  
Prashant Kumar Jamwal ◽  
Muhammad Tahir Akhtar
Keyword(s):  
Healthy Subjects ◽  
Single Channel ◽  
Event Related Potentials ◽  
Support Vector ◽  
Ensemble Classifiers ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
P300 Speller ◽  
Related Potentials ◽  
Als Patients

Classification of brain signal features is a crucial process for any brain–computer interface (BCI) device, including speller systems. The positive P300 component of visual event-related potentials (ERPs) used in BCI spellers has individual variations of amplitude and latency that further changse with brain abnormalities such as amyotrophic lateral sclerosis (ALS). This leads to the necessity for the users to train the speller themselves, which is a very time-consuming procedure. To achieve subject-independence in a P300 speller, ensemble classifiers are proposed based on classical machine learning models, such as the support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbors (kNN), and the convolutional neural network (CNN). The proposed voters were trained on healthy subjects’ data using a generic training approach. Different combinations of electroencephalography (EEG) channels were used for the experiments presented, resulting in single-channel, four-channel, and eight-channel classification. ALS patients’ data represented robust results, achieving more than 90% accuracy when using an ensemble of LDA, kNN, and SVM on four active EEG channels data in the occipital area of the brain. The results provided by the proposed ensemble voting models were on average about 5% more accurate than the results provided by the standalone classifiers. The proposed ensemble models could also outperform boosting algorithms in terms of computational complexity or accuracy. The proposed methodology shows the ability to be subject-independent, which means that the system trained on healthy subjects can be efficiently used for ALS patients. Applying this methodology for online speller systems removes the necessity to retrain the P300 speller.

scholarly journals Study of Machine Learning Techniques for EEG Eye State Detection

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 53
Author(s):  
Francisco Laport ◽  
Paula M. Castro ◽  
Adriana Dapena ◽  
Francisco J. Vazquez-Araujo ◽  
Daniel Iglesia
Keyword(s):  
Machine Learning ◽  
Mental States ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Linear Discriminant ◽  
State Identification ◽  
Brain Signals ◽  
State Classification ◽  
Learning Techniques

A comparison of different machine learning techniques for eye state identification through Electroencephalography (EEG) signals is presented in this paper. (1) Background: We extend our previous work by studying several techniques for the extraction of the features corresponding to the mental states of open and closed eyes and their subsequent classification; (2) Methods: A prototype developed by the authors is used to capture the brain signals. We consider the Discrete Fourier Transform (DFT) and the Discrete Wavelet Transform (DWT) for feature extraction; Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) for state classification; and Independent Component Analysis (ICA) for preprocessing the data; (3) Results: The results obtained from some subjects show the good performance of the proposed methods; and (4) Conclusion: The combination of several techniques allows us to obtain a high accuracy of eye identification.

scholarly journals Diagnostic Performance of 2D and 3D T2WI-Based Radiomics Features With Machine Learning Algorithms to Distinguish Solid Solitary Pulmonary Lesion

2021 ◽  
Vol 11 ◽  
Author(s):  
Qi Wan ◽  
Jiaxuan Zhou ◽  
Xiaoying Xia ◽  
Jianfeng Hu ◽  
Peng Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Diagnostic Performance ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Selection Methods ◽  
Linear Discriminant ◽  
2D And 3D

ObjectiveTo evaluate the performance of 2D and 3D radiomics features with different machine learning approaches to classify SPLs based on magnetic resonance(MR) T2 weighted imaging (T2WI).Material and MethodsA total of 132 patients with pathologically confirmed SPLs were examined and randomly divided into training (n = 92) and test datasets (n = 40). A total of 1692 3D and 1231 2D radiomics features per patient were extracted. Both radiomics features and clinical data were evaluated. A total of 1260 classification models, comprising 3 normalization methods, 2 dimension reduction algorithms, 3 feature selection methods, and 10 classifiers with 7 different feature numbers (confined to 3–9), were compared. The ten-fold cross-validation on the training dataset was applied to choose the candidate final model. The area under the receiver operating characteristic curve (AUC), precision-recall plot, and Matthews Correlation Coefficient were used to evaluate the performance of machine learning approaches.ResultsThe 3D features were significantly superior to 2D features, showing much more machine learning combinations with AUC greater than 0.7 in both validation and test groups (129 vs. 11). The feature selection method Analysis of Variance(ANOVA), Recursive Feature Elimination(RFE) and the classifier Logistic Regression(LR), Linear Discriminant Analysis(LDA), Support Vector Machine(SVM), Gaussian Process(GP) had relatively better performance. The best performance of 3D radiomics features in the test dataset (AUC = 0.824, AUC-PR = 0.927, MCC = 0.514) was higher than that of 2D features (AUC = 0.740, AUC-PR = 0.846, MCC = 0.404). The joint 3D and 2D features (AUC=0.813, AUC-PR = 0.926, MCC = 0.563) showed similar results as 3D features. Incorporating clinical features with 3D and 2D radiomics features slightly improved the AUC to 0.836 (AUC-PR = 0.918, MCC = 0.620) and 0.780 (AUC-PR = 0.900, MCC = 0.574), respectively.ConclusionsAfter algorithm optimization, 2D feature-based radiomics models yield favorable results in differentiating malignant and benign SPLs, but 3D features are still preferred because of the availability of more machine learning algorithmic combinations with better performance. Feature selection methods ANOVA and RFE, and classifier LR, LDA, SVM and GP are more likely to demonstrate better diagnostic performance for 3D features in the current study.

scholarly journals Prediction of preference and effect of music on preference: a preliminary study on electroencephalography from young women

2019 ◽  
pp. 1109-1119
Author(s):  
BÜLENT YILMAZ ◽  
CENGİZ GAZELOĞLU ◽  
FATİH ALTINDİŞ
Keyword(s):  
Support Vector ◽  
Purchasing Behavior ◽  
Forward Selection ◽  
Eeg Signals ◽  
K Nearest Neighbors ◽  
Brain Signals ◽  
Power Spectral ◽  
Eeg Data ◽  
Mean Differences ◽  
Sequential Forward Selection

Neuromarketing is the application of the neuroscientific approaches to analyze and understand economically relevant behavior. In this study, the effect of loud and rhythmic music in a sample neuromarketing setup is investigated. The second aim was to develop an approach in the prediction of preference using only brain signals. In this work, 19- channel EEG signals were recorded and two experimental paradigms were implemented: no music/silence and rhythmic, loud music using a headphone, while viewing women shoes. For each 10-sec epoch, normalized power spectral density (PSD) of EEG data for six frequency bands was estimated using the Burg method. The effect of music was investigated by comparing the mean differences between music and no music groups using independent two-sample t-test. In the preference prediction part sequential forward selection, k-nearest neighbors (k-NN) and the support vector machines (SVM), and 5-fold cross-validation approaches were used. It is found that music did not affect like decision in any of the power bands, on the contrary, music affected dislike decisions for all bands with no exceptions. Furthermore, the accuracies obtained in preference prediction study were between 77.5 and 82.5% for k-NN and SVM techniques. The results of the study showed the feasibility of using EEG signals in the investigation of the music effect on purchasing behavior and the prediction of preference of an individual.

Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies

2017 ◽  
Vol 11 (4) ◽  
pp. 80-92
Author(s):  
Junjie Bai ◽  
Kan Luo ◽  
Jun Peng ◽  
Jinliang Shi ◽  
Ying Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Affective Computing ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Cognitive Computing ◽  
Bayes Classifier ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Neuro Fuzzy

Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this article, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A set of classifications and machine learning algorithms are explored and comparatively studied for MER, which includes Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Neuro-Fuzzy Networks Classification (NFNC), Fuzzy KNN (FKNN), Bayes classifier and Linear Discriminant Analysis (LDA). Experimental results show that the SVM, FKNN and LDA algorithms are the most effective methodologies that obtain more than 80% accuracy for MER.

scholarly journals Using Machine Learning to Grade the Mango’s Quality Based on External Features Captured by Vision System

2020 ◽  
Vol 10 (17) ◽  
pp. 5775
Author(s):  
Nguyen Truong Minh Long ◽  
Nguyen Truong Thinh
Keyword(s):  
Machine Learning ◽  
Vision System ◽  
Human Perception ◽  
Poor Quality ◽  
Machine Learning Algorithms ◽  
Internal Quality ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Length Width

Nowadays, mangoes and other fruits are classified according to human perception of low productivity, which is a poor quality of classification. Therefore, in this study, we suggest a novel evaluation of internal quality focused on external features of mango as well as its weight. The results show that evaluation is more effective than using only one of the external features or weight combining an expensive nondestructive (NDT) measurement. Grading of fruits is implemented by four models of machine learning as Random Forest (RF), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Models have inputs such as length, width, defect, weight, and outputs being mango classifications such as grade G1, G2, and G3. The unstructured data of 4983 of captured images combining with load-cell signals are transferred to structured data to generate a completed dataset including density. The data normalization and elimination of outliers (DNEO) are used to create a better dataset which prepared for machine learning algorithms. Moreover, an unbiased performance estimate for the training process carried out by the nested cross-validation (NCV) method. In the experiment, the methods of machine learning have high accurate over 87.9%, especially the model of RF gets 98.1% accuracy.

scholarly journals Using Linear Discriminant Analysis for Dimensionality Reduction for Predicting Anomalies of BGP data

2019 ◽  
Vol 8 (11) ◽  
pp. 1989-1995
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
High Dimensions ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Data Packets ◽  
Vector Machines

Border Gateway Protocol (BGP) is a vital protocol on the internet for transfer of data packets among Autonomous System (AS). Security is a major concern for the transmission of BGP packets which are often attacked by worms or are hijacked by an attacker which results in requests entering black holes or loss of connection to the particular sites. The BGP anomalies can be reduced by analyzing the BGP datasets. Since, ASes communicate through messages, therefore, the anomalies can be reduced by identifying the corrupted BGP message in the dataset. In this paper, BGP anomalies have been classified by applying Machine learning (ML) algorithms. The dataset contains information about the sending and receiving time between ASes. The classifiers were used to predict the anomalies. Since the dataset had high dimensions, the dimensions were reduced using Linear Discriminant Analysis (LDA) and then Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Linear Regression, Logistic Regression and Multi-Layer Perceptron (MLP) have been used to classify the anomalies.

scholarly journals Classification of Resting-State Status Based on Sample Entropy and Power Spectrum of Electroencephalography (EEG)

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ahmed M. A. Mohamed ◽  
Osman N. Uçan ◽  
Oğuz Bayat ◽  
Adil Deniz Duru
Keyword(s):  
Resting State ◽  
Sample Entropy ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Frequency Bands ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open ◽  
The Brain

An electroencephalogram (EEG) is a significant source of diagnosing brain issues. It is also a mediator between the external world and the brain, especially in the case of any mental illness; however, it has been widely used to monitor the dynamics of the brain in healthy subjects. This paper discusses the resting state of the brain with eyes open (EO) and eyes closed (EC) by using sixteen channels by the use of conventional frequency bands and entropy of the EEG signal. The Fast Fourier Transform (FFT) and sample entropy (SE) of each sensor are computed as methods of feature extraction. Six classifiers, including logistic regression (LR), K-Nearest Neighbors (KNN), linear discriminant (LD), decision tree (DT), support vector machine (SVM), and Gaussian Naive Bayes (GNB) are used to discriminate the resting states of the brain based on the extracted features. EEG data were epoched with one-second-length windows, and they were used to compute the features to classify EO and EC conditions. Results showed that the LR and SVM classifiers had the highest average classification accuracy (97%). Accuracies of LD, KNN, and DT were 95%, 93%, and 92%, respectively. GNB gained the least accuracy (86%) when conventional frequency bands were used. On the other hand, when SE was used, the average accuracies of SVM, LD, LR, GNB, KNN, and DT algorithms were 92% 90%, 89%, 89%, 86%, and 86%, respectively.

scholarly journals Discrimination between Inhibitory and Excitatory Neurons of Mouse Hippocampus Based on the Shape of Extracellular Spike Waveforms

2021 ◽  
Author(s):  
Mehrdad Oghazian ◽  
Farzad Saffari ◽  
Ali Khadem
Keyword(s):  
Classification Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Shape Features ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Mouse Hippocampus ◽  
Excitatory Neurons ◽  
Spike Waveforms

Purpose: Inhibitory and excitatory neurons play an essential role in brain function, and we aim to introduce an automatic method to discriminate these two populations based on features of the shape of their spikes. Consequently, we will explain the spike extraction from raw data of a single shank electrode and determine the best features of spike waveforms for the classification of neurons. It is noteworthy that, to the best of our knowledge, classification of inhibitory and excitatory neurons using the shape features extracted from their spike waveforms has not been done before. Materials and Methods: In this paper, we use a dataset of mouse hippocampus neurons in which the neuron types (inhibitory or excitatory) have been verified optogenetically. For the classification of mouse hippocampus neurons, we extracted eight shape features of their spike waveforms in addition to their firing rates and used three types of classifiers: K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) to analyze the discriminatory power of features based on the accuracy of the classifications. Results: We showed that Spike asymmetry, Peak-to-trough ratio, Recovery slope, and Duration between peaks were four shape features of spike waveforms participated in the optimum feature subsets that resulted in maximum classification accuracy. Moreover, the SVM classifier with RBF kernel resulted in maximum accuracy of %96.91 ± %13.03 and was identified as the best classifier. Conclusion: In this study, we found that shape features of spike waveforms can accurately classify inhibitory and excitatory neurons of mouse hippocampus. Also, we found an optimum subset of shape features of spike waveforms that resulted in better classification performance than previously proposed subsets of features used for clustering of neurons. Our findings open a promising way toward a functional classification of neurons automatically.

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