scholarly journals Exploring Sampling in the Detection of Multicategory EEG Signals

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Siuly Siuly ◽  
Enamul Kabir ◽  
Hua Wang ◽  
Yanchun Zhang
Keyword(s):  
Nearest Neighbor ◽  
Optimal Allocation ◽  
Multinomial Logistic Regression ◽  
Support Vector ◽  
Statistical Features ◽  
Eeg Signals ◽  
Ridge Estimator ◽  
Time Period ◽  
Eeg Data ◽  
Machine Leaning

The paper presents a structure based on samplings and machine leaning techniques for the detection of multicategory EEG signals where random sampling (RS) and optimal allocation sampling (OS) are explored. In the proposed framework, before using the RS and OS scheme, the entire EEG signals of each class are partitioned into several groups based on a particular time period. The RS and OS schemes are used in order to have representative observations from each group of each category of EEG data. Then all of the selected samples by the RS from the groups of each category are combined in a one set named RS set. In the similar way, for the OS scheme, an OS set is obtained. Then eleven statistical features are extracted from the RS and OS set, separately. Finally this study employs three well-known classifiers:k-nearest neighbor (k-NN), multinomial logistic regression with a ridge estimator (MLR), and support vector machine (SVM) to evaluate the performance for the RS and OS feature set. The experimental outcomes demonstrate that the RS scheme well represents the EEG signals and thek-NN with the RS is the optimum choice for detection of multicategory EEG signals.

scholarly journals Classification of EEG Signals using Nonlinear Features and Preprocessing Techniques

2021 ◽  
Vol 10 (5) ◽  
pp. 297-301
Author(s):  
Saneesh Cleatus T ◽  
Dr. Thungamani M
Keyword(s):  
Brain Tumor ◽  
Nearest Neighbor ◽  
Processing Technique ◽  
Support Vector ◽  
Statistical Features ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Mode Decomposition ◽  
Nonlinear Features

In this paper we study the effect of nonlinear preprocessing techniques in the classification of electroencephalogram (EEG) signals. These methods are used for classifying the EEG signals captured from epileptic seizure activity and brain tumor category. For the first category, preprocessing is carried out using elliptical filters, and statistical features such as Shannon entropy, mean, standard deviation, skewness and band power. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were used for the classification. For the brain tumor EEG signals, empirical mode decomposition is used as a pre-processing technique along with standard statistical features for the classification of normal and abnormal EEG signals. For epileptic signals we have achieved an average accuracy of 94% for a three-class classification and for brain tumor signals we have achieved a classification accuracy of 98% considering it as a two class problem.

scholarly journals EEG data processing in ADHD diagnosis and neurofeedback

2020 ◽  
Vol 40 (3) ◽  
pp. 116-123
Author(s):  
Zoran Šverko ◽  
Ivan Markovinović ◽  
Miroslav Vrankić ◽  
Saša Vlahinić
Keyword(s):  
Data Processing ◽  
Mean Value ◽  
Adhd Group ◽  
Eeg Signal ◽  
Statistical Features ◽  
Eeg Signals ◽  
New Approach ◽  
Mean Values ◽  
Adhd Diagnosis ◽  
Eeg Data

In this paper, EEG data processing was conducted in order to define the parameters for neurofeedback. A new survey was conducted based on a brief review of previous research. Two groups of participants were chosen: ADHD (3) and nonADHD (14). The main part of this study includes EEG signal data pre-processing and processing. We have outlined statistical features of observed EEG signals such as mean value, grand-mean value and their ratios. It can be concluded that an increase in grand-mean values of power theta-low beta ratio on Cz electrode gives confirmation of previous research. The value of alpha-delta power ratio higher than 1 on C3, Cz, P3, Pz, P4 in ADHD group is proposed as a new approach to classification. Based on these conclusions we will design a neurofeedback protocol as a continuation of this work.

scholarly journals An Investigation of Insider Threat Mitigation Based on EEG Signal Classification

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6365
Author(s):  
Jung Hwan Kim ◽  
Chul Min Kim ◽  
Man-Sung Yim
Keyword(s):  
Nearest Neighbor ◽  
Nuclear Industry ◽  
Classification Model ◽  
Support Vector ◽  
Insider Threat ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Nuclear Facilities ◽  
Time Frequency ◽  
Threat Mitigation

This study proposes a scheme to identify insider threats in nuclear facilities through the detection of malicious intentions of potential insiders using subject-wise classification. Based on electroencephalography (EEG) signals, a classification model was developed to identify whether a subject has a malicious intention under scenarios of being forced to become an insider threat. The model also distinguishes insider threat scenarios from everyday conflict scenarios. To support model development, 21-channel EEG signals were measured on 25 healthy subjects, and sets of features were extracted from the time, time–frequency, frequency and nonlinear domains. To select the best use of the available features, automatic selection was performed by random-forest-based algorithms. The k-nearest neighbor, support vector machine with radial kernel, naïve Bayes, and multilayer perceptron algorithms were applied for the classification. By using EEG signals obtained while contemplating becoming an insider threat, the subject-wise model identified malicious intentions with 78.57% accuracy. The model also distinguished insider threat scenarios from everyday conflict scenarios with 93.47% accuracy. These findings could be utilized to support the development of insider threat mitigation systems along with existing trustworthiness assessments in the nuclear industry.

AUTOMATED IDENTIFICATION OF EPILEPTIC AND ALCOHOLIC EEG SIGNALS USING RECURRENCE QUANTIFICATION ANALYSIS

2012 ◽  
Vol 12 (05) ◽  
pp. 1240028 ◽  
Author(s):  
EE PING NG ◽  
TEIK-CHENG LIM ◽  
SUBHAGATA CHATTOPADHYAY ◽  
MURALIDHAR BAIRY
Keyword(s):  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Gaussian Mixture ◽  
Recurrence Quantification Analysis ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Recurrence Quantification ◽  
Non Linear ◽  
Quantification Analysis

Epilepsy is a common neurological disorder characterized by recurrence seizures. Alcoholism causes organic changes in the brain, resulting in seizure attacks similar to epileptic fits. Hence, it is challenging to differentiate the cause of fits as epileptic or alcoholism, which is important for deciding on the treatment in the neurology ward. The focus of this paper is to automatically differentiate epileptic, normal, and alcoholic electroencephalogram (EEG) signals. As the EEG signals are non-linear and dynamic in nature, it is difficult to tell the subtle changes in these signals with the help of linear techniques or by the naked eye. Therefore, to analyze the normal (control), epileptic, and alcoholic EEG signals, two non-linear methods, such as recurrence plots (RPs) and then recurrence quantification analysis (RQA) are adopted. Approximately 10 RQA parameters have been used to classify the EEG signals into three distinct classes, i.e., normal, epileptic, and alcoholic. Six classifiers, such as support vector machine (SVM), radial basis probabilistic neural network (RBPNN), decision tree (DT), Gaussian mixture model (GMM), k-nearest neighbor (kNN), and fuzzy Sugeno classifiers have been developed to accomplish this task. Results show that the GMM classifier outperformed the other classifiers with a classification sensitivity of 99.6%, specificity of 98.3%, and accuracy of 98.6%.

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.

Machine Learning Methods as a Test Bed for EEG Analysis in BCI Paradigms

2020 ◽  
pp. 1577-1597
Author(s):  
Kusuma Mohanchandra ◽  
Snehanshu Saha
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Support Vector ◽  
Test Bed ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Broad Perspective ◽  
Learning Techniques

Machine learning techniques, is a crucial tool to build analytical models in EEG data analysis. These models are an excellent choice for analyzing the high variability in EEG signals. The advancement in EEG-based Brain-Computer Interfaces (BCI) demands advanced processing tools and algorithms for exploration of EEG signals. In the context of the EEG-based BCI for speech communication, few classification and clustering techniques is presented in this book chapter. A broad perspective of the techniques and implementation of the weighted k-Nearest Neighbor (k-NN), Support vector machine (SVM), Decision Tree (DT) and Random Forest (RF) is explained and their usage in EEG signal analysis is mentioned. We suggest that these machine learning techniques provides not only potentially valuable control mechanism for BCI but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.

scholarly journals InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7251
Author(s):  
Hong Zeng ◽  
Jiaming Zhang ◽  
Wael Zakaria ◽  
Fabio Babiloni ◽  
Borghini Gianluca ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Support Vector ◽  
Learning Method ◽  
Learning Models ◽  
Eeg Signals ◽  
Eeg Data ◽  
State Detection ◽  
Fatigue Detection ◽  
Electroencephalogram Eeg ◽  
Machine Learning Models

Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fatigue across subjects through using EEG signals remains a challenge. EasyTL is a kind of transfer-learning model, which has demonstrated better performance in the field of image recognition, but not yet been applied in cross-subject EEG-based applications. In this paper, we propose an improved EasyTL-based classifier, the InstanceEasyTL, to perform EEG-based analysis for cross-subject fatigue mental-state detection. Experimental results show that InstanceEasyTL not only requires less EEG data, but also obtains better performance in accuracy and robustness than EasyTL, as well as existing machine-learning models such as Support Vector Machine (SVM), Transfer Component Analysis (TCA), Geodesic Flow Kernel (GFK), and Domain-adversarial Neural Networks (DANN), etc.

Revealing False Positive Features in Epileptic EEG Identification

2020 ◽  
Vol 30 (11) ◽  
pp. 2050017 ◽  
Author(s):  
Jian Lian ◽  
Yunfeng Shi ◽  
Yan Zhang ◽  
Weikuan Jia ◽  
Xiaojun Fan ◽  
...  
Keyword(s):  
Feature Selection ◽  
Nearest Neighbor ◽  
State Of The Art ◽  
The State ◽  
Support Vector ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Eeg Signals ◽  
Eeg Classification

Feature selection plays a vital role in the detection and discrimination of epileptic seizures in electroencephalogram (EEG) signals. The state-of-the-art EEG classification techniques commonly entail the extraction of the multiple features that would be fed into classifiers. For some techniques, the feature selection strategies have been used to reduce the dimensionality of the entire feature space. However, most of these approaches focus on the performance of classifiers while neglecting the association between the feature and the EEG activity itself. To enhance the inner relationship between the feature subset and the epileptic EEG task with a promising classification accuracy, we propose a machine learning-based pipeline using a novel feature selection algorithm built upon a knockoff filter. First, a number of temporal, spectral, and spatial features are extracted from the raw EEG signals. Second, the proposed feature selection algorithm is exploited to obtain the optimal subgroup of features. Afterwards, three classifiers including [Formula: see text]-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) are used. The experimental results on the Bonn dataset demonstrate that the proposed approach outperforms the state-of-the-art techniques, with accuracy as high as 99.93% for normal and interictal EEG discrimination and 98.95% for interictal and ictal EEG classification. Meanwhile, it has achieved satisfactory sensitivity (95.67% in average), specificity (98.83% in average), and accuracy (98.89% in average) over the Freiburg dataset.

scholarly journals Exploring Douglas-Peucker Algorithm in the Detection of Epileptic Seizure from Multicategory EEG Signals

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Roozbeh Zarei ◽  
Jing He ◽  
Siuly Siuly ◽  
Guangyan Huang ◽  
Yanchun Zhang
Keyword(s):  
Principal Component ◽  
Epileptic Seizures ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Eeg Signals ◽  
Decision Tree Classifier ◽  
Efficient Detection ◽  
Eeg Data ◽  
Tree Classifier ◽  
Representative Samples

Discovering the concealed patterns of Electroencephalogram (EEG) signals is a crucial part in efficient detection of epileptic seizures. This study develops a new scheme based on Douglas-Peucker algorithm (DP) and principal component analysis (PCA) for extraction of representative and discriminatory information from epileptic EEG data. As the multichannel EEG signals are highly correlated and are in large volumes, the DP algorithm is applied to extract the most representative samples from EEG data. The PCA is utilised to produce uncorrelated variables and to reduce the dimensionality of the DP samples for better recognition. To verify the robustness of the proposed method, four machine learning techniques, random forest classifier (RF), k-nearest neighbour algorithm (k-NN), support vector machine (SVM), and decision tree classifier (DT), are employed on the obtained features. Furthermore, we assess the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that the DP technique effectively extracts the representative samples from EEG signals compressing up to over 47% sample points of EEG signals. The results also indicate that the proposed feature method with the RF classifier achieves the best performance and yields 99.85% of the overall classification accuracy (OCA). The proposed method outperforms the most recently reported methods in terms of OCA in the same epileptic EEG database.

scholarly journals Efficient electro encephelogram classification system using support vector machine classifier and adaptive learning technique

2022 ◽  
Vol 25 (1) ◽  
pp. 291
Author(s):  
Virupaxi Balachandra Dalal ◽  
Satish S. Bhairannawar
Keyword(s):  
Support Vector Machine ◽  
Adaptive Learning ◽  
Classification System ◽  
Training Data ◽  
Support Vector ◽  
Detection Accuracy ◽  
Eeg Signals ◽  
The Neural Network ◽  
Eeg Data ◽  
Learning Technique

Complex <span>modern signal processing is used to automate the analysis of electro encephelogram (EEG) signals. For the diagnosis of seizures, approaches that are simple and precise may be preferable rather than difficult and time-consuming. In this paper, efficient EEG classification system using support vector machine (SVM) and Adaptive learning technique is proposed. The database EEG signals are subjected to temporal and spatial filtering to remove unwanted noise and to increase the detection accuracy of the classifier by selecting the specific bands in which most of the EEG data are present. The neural network based SVM is used to classify the test EEG data with respect to training data. The cost-sensitive SVM with proposed Adaptive learning classifies the EEG signals where the adaptive learning with probability based function helps in prediction of the future samples and this leads in improving the accuracy with detection time. The detection accuracy of the proposed algorithm is compared with existing which shows that the proposed algorithm can classify the EEG signal more </span>effectively.

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