Classification of EEG signals for brain-computer interface applications: Performance comparison

Ilyas, Mohd Zaizu; Saad, Puteh; Ghani, Abdul Rahman Izaini

doi:10.1109/icoras.2016.7872610

Cited by 30 publications

(26 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Based on the classification results, the type of task is identified. Famous classification approaches used in literature are Linear Discriminant Analysis (LDA) [12] [13], Support Vector Machines (SVM) [12], k-nearest neighbors, Logistic Regression (LR) [14], Quadratic Classifiers [15], Recurrent Neural Network (RNN) [16]. Some other BCI uses feature extraction, selection, and classification as one block, in deep learning [6].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Classification of Non-Discriminant ERD/ERS Comprising Motor Imagery Electroencephalography Signals

Asi¹,

Sultan²,

Muneer³

et al. 2020

IJACSA

View full text Add to dashboard Cite

Classification of Motor Imagery (MI) Electroencephalography (EEG) signals has always been an important aspect of Brain Computer Interface (BCI) systems. Event Related Desynchronization (ERD)/ Event Related Synchronization (ERS) plays a significant role in finding discriminant features of MI EEG signals. ERD/ERS is one type and Evoked Potential (EP) is another type of brain response. This study focuses upon the classification of MI EEG signals by Removing Evoked Potential (REP) from non-discriminant MI EEG data in filter band selection, called REP. This optimization is done to enhance the classification performance. A comprehensive comparison of several pipelines is presented by using famous feature extraction methods, namely Common Spatial Pattern (CSP), XDawn. The effectiveness of REP is demonstrated on the PhysioNet dataset which is an online data resource. Comparison is done between the performance of pipelines including proposed one (Common Spatial Pattern (CSP) and Gaussian Process Classifier (GPC)) as well as before and after applying REP. It is observed that the REP approach has improved the classification accuracy of all the subjects used as well as all the pipelines, including state of the art algorithms, up to 20%.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…Some authors claimed that Power Spectral Density (PSD) is most effective in extracting patterns for classification MI EEG data [13]. Support Vector Machines (SVM) is very famous in EEG classification [14]. SVM with Differential Equation (DE) [12], SVM with PSD [25] showed good performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Non-Discriminant ERD/ERS Comprising Motor Imagery Electroencephalography Signals

Asi¹,

Sultan²,

Muneer³

et al. 2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…From the training data set, which are labeled, we can calculate the constants ai of the logistic regression equation, and the maximum likelihood estimation technique is used to get the values of constants. Prediction using logistic regression is an easy task, and if the coefficients are accurate, the prediction is robotic (Ilyas et al, 2016). From the mathematical point of view, the SVM classifier is a constrained minimization problem that is solved using the Lagrange multiplier method (Lee et al, 2019).…”

Section: Loss Functionmentioning

confidence: 99%

Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques

2020

Journal of Integrative Neuroscience

View full text Add to dashboard Cite

Electroencephalography is the recording of brain electrical activities that can be used to diagnose brain seizure disorders. By identifying brain activity patterns and their correspondence between symptoms and diseases, it is possible to give an accurate diagnosis and appropriate drug therapy to patients. This work aims to categorize electroencephalography signals on different channels' recordings for classifying and predicting epileptic seizures. The collection of the electroencephalography recordings contained in the dataset attributes 179 information and 11,500 instances. Instances are of five categories, where one is the symptoms of epilepsy seizure. We have used traditional, ensemble methods and deep machine learning techniques highlighting their performance for the epilepsy seizure detection task. One dimensional convolutional neural network, ensemble machine learning techniques like bagging, boosting (AdaBoost, gradient boosting, and XG boosting), and stacking is implemented. Traditional machine learning techniques such as decision tree, random forest, extra tree, ridge classifier, logistic regression, K-Nearest Neighbor, Naive Bayes (gaussian), and Kernel Support Vector Machine (polynomial, gaussian) are used for classifying and predicting epilepsy seizure. Before using ensemble and traditional techniques, we have preprocessed the data set using the Karl Pearson coefficient of correlation to eliminate irrelevant attributes. Further accuracy of classification and prediction of the classifiers are manipulated using k-fold crossvalidation methods and represent the Receiver Operating Characteristic Area Under the Curve for each classifier. After sorting and comparing algorithms, we have found the convolutional neural network and extra tree bagging classifiers to have better performance than all other ensemble and traditional classifiers.

show abstract

“…As instruções de comando para a indicação de movimentos e imaginação de movimentos enviadas ao voluntário, foram realizadas de forma dessincronizada [8]. A Figura 2 ilustra a aquisição dos sinais de EEG juntamente com os sinais digitais enviados durante os ensaios.…”

Section: Hardware De Aquisiçãounclassified

Análise Comparativa Da Classicação De Sinais De Eletroencefalografia No Domínio Do Tempo Utilizando Redes PMC E RBF

Broniera¹,

Nunes²,

Krueger³

et al. 2018

Anais Do v Congresso Brasileiro De Eletromiografia E Cinesiologia E X Simpósio De Engenharia Biomédica

View full text Add to dashboard Cite

Resumo: Este trabalho utiliza-se de redes neurais artificiais como classificadoras de padrões que analisam os sinais de eletroencefalografia (EEG) Os padrões de registros EEG possuem regularidade em frequência e amplitude, os quais possuem espectro de frequências subdividido em faixas que estão relacionadas a diversos estados físicos (andar, correr) e comportamentais (decisões, concentração). Segundo [2] estes padrões são divididos em ondas alfa (α), beta (β), teta (Ɵ), delta (δ) e gama ( ). Os sinais de EEG podem ser distorcidos por interferências como: Interferências eletromagnéticas, contrações musculares do paciente, dos eletrodos e do sistema de aquisição do sinal. Desta forma, os sinais podem apresentar diferenças entre indivíduos sob mesmas circunstâncias, fato este, que dificulta a classificação e a possível modelagem matemática dos padrões EEG [3].Neste sentido, técnicas são propostas para classificação dos padrões de registros EEG. Em [4] são utilizadas redes neurais artificiais integradas a um sistema de neurofeedback com o objetivo de classificar os níveis de atenção em indivíduos saudáveis.Dentre as aplicações que utilizam as ICM destaca-se o reconhecimento de padrões de imagética motora em seres humanos. Segundo [5] esta técnica aliada a outras tais como eletroestimulação funcional (FES) e robótica podem auxiliar no processo de reabilitação de pacientes com grau de lesões diferentes.No trabalho apresentado em [6] é testada uma interface cérebro-máquina aliada ao sistema de

show abstract

Classification of EEG signals for brain-computer interface applications: Performance comparison

Cited by 30 publications

References 15 publications

Classification of Non-Discriminant ERD/ERS Comprising Motor Imagery Electroencephalography Signals

Classification of Non-Discriminant ERD/ERS Comprising Motor Imagery Electroencephalography Signals

Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques

Análise Comparativa Da Classicação De Sinais De Eletroencefalografia No Domínio Do Tempo Utilizando Redes PMC E RBF

Contact Info

Product

Resources

About