Motor imagery classification in Brain computer interface (BCI) based on EEG signal by using machine learning technique
TLDR
This paper focuses on classification of motor imagery in Brain Computer Interface by using classifiers from machine learning technique and SVM, Logistic Regression and Naïve Bayes classifier achieved the highest accuracy with 89.09% in AUC measurement.Abstract:
This paper focuses on classification of motor imagery in Brain Computer Interface (BCI) by using classifiers from machine learning technique. The BCI system consists of two main steps which are feature extraction and classification. The Fast Fourier Transform (FFT) features is extracted from the electroencephalography (EEG) signals to transform the signals into frequency domain. Due to the high dimensionality of data resulting from the feature extraction stage, the Linear Discriminant Analysis (LDA) is used to minimize the number of dimension by finding the feature subspace that optimizes class separability. Five classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree and Logistic Regression are used in the study. The performance was tested by using Dataset 1 from BCI Competition IV which consists of imaginary hand and foot movement EEG data. As a result, SVM, Logistic Regression and Naive Bayes classifier achieved the highest accuracy with 89.09% in AUC measurement.read more
Citations
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A novel hybrid kernel function relevance vector machine for multi-task motor imagery EEG classification
TL;DR: The experimental results show that the proposed method improves the accuracy and Kappa coefficient for the multi-task motor imagery EEG classification problem and is effective for classification at both local and global feature levels.
Journal ArticleDOI
Automated Alcoholism Detection Using Fourier-Bessel Series Expansion Based Empirical Wavelet Transform
TL;DR: The Fourier-Bessel series expansion based empirical wavelet transform (FBSE-EWT) is proposed for automated alcoholism detection using electroencephalogram (EEG) signals and suggests that LS-SVM with radial basis function (RBF) kernel achieves a highest average accuracy, sensitivity, and specificity of 99.1% with top 20 significant features.
Journal ArticleDOI
Motor imagery classification in brain-machine interface with machine learning algorithms: Classical approach to multi-layer perceptron model
TL;DR: Wang et al. as discussed by the authors proposed a subject-independent generalized MLP model with ≈90% accuracy and half the classification time compared to traditional ML-based models, which suggests the possibility of a much accurate and robust generalized BCI (subject independent) if this model integrates sophisticated optimization.
Journal ArticleDOI
Motor imagery classification in brain-machine interface with machine learning algorithms: Classical approach to multi-layer perceptron model
TL;DR: Wang et al. as discussed by the authors proposed a subject-independent generalized MLP model with ≈90% accuracy and half the classification time compared to traditional ML-based models, which suggests the possibility of a much accurate and robust generalized BCI (subject independent) if this model integrates sophisticated optimization.
Journal ArticleDOI
EEG based alcoholism detection by oscillatory modes decomposition second order difference plots and machine learning
TL;DR: In this article , a novel hybridization of the oscillatory modes decomposition, features mining based on the Second Order Difference Plots (SODPs) of oscillatory mode, and machine learning algorithms is devised for an effective identification of alcoholism.
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Python Machine Learning
TL;DR: Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.
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