Analysis of dimension reduction by PCA and AdaBoost on spelling paradigm EEG data

Yildirim, Asil; Halıcı, Uğur

doi:10.1109/bmei.2013.6746932

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…The P300 EEG signal is a complex combination of superimposed multi-band waveforms, and a number of classification methods have been used to decode the P300 signal. For example, principal component analysis (PCA) and local Fisher discriminant analysis (LFDA) are commonly used to reduce the dimensionality of features ( Bernat et al, 2007 ), while linear discriminant analysis (LDA) ( Dodia et al, 2019 ), support vector machine (SVM) ( Li et al, 2014 ), decision tree (DT) ( Guan et al, 2019 ), random forest (RF) ( Akram et al, 2015 ; Masud et al, 2018 ), ADB ( Hongzhi et al, 2012 ; Yildirim and Halici, 2014 ), and k-nearest neighbor (k-NN) methods ( Guney et al, 2021 ) are commonly used for P300 classification.…”

Section: Introductionmentioning

confidence: 99%

A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network

Song

et al. 2022

Front. Comput. Neurosci.

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BackgroundElectroencephalogram (EEG)-based brain-computer interface (BCI) systems are widely utilized in various fields, including health care, intelligent assistance, identity recognition, emotion recognition, and fatigue detection. P300, the main event-related potential, is the primary component detected by EEG-based BCI systems. Existing algorithms for P300 classification in EEG data usually perform well when tested in a single participant, although they exhibit significant decreases in accuracy when tested in new participants. We attempted to address this lack of generalizability associated with existing classification methods using a novel convolutional neural network (CNN) model developed using logistic regression (LR).Materials and MethodsWe proposed an LR-CNN model comprising two parts: a combined LR-based memory model and a CNN-based generalization model. The LR-based memory model can learn the individual features of participants and addresses the decrease in accuracy caused by individual differences when applied to new participants. The CNN-based generalization model can learn the common features among participants, thereby reducing overall classification bias and improving overall classification accuracy.ResultsWe compared our method with existing, commonly used classification methods through three different sets of experiments. The experimental results indicated that our method could learn individual differences among participants. Compared with other commonly used classification methods, our method yielded a marked improvement (>90%) in classification among new participants.ConclusionThe accuracy of the proposed model in the face of new participants is better than that of existing, commonly used classification methods. Such improvements in cross-subject test accuracy will aid in the development of BCI systems.

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Section: Introductionmentioning

confidence: 99%

A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network

Song

et al. 2022

Front. Comput. Neurosci.

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“…K-Nearest Neighbor (kNN) has also been used in P300 studies and has yielded satisfactory results [25]. AdaBoost is also a popular classifier and has also been used in P300 speller studies [26,27]. Random Forest (RF) classifier has also been widely used in P300 speller based study [28].…”

Section: Introductionmentioning

confidence: 99%

Classification of brain signal (EEG) induced by shape-analogous letter perception

Bose

Goh

Wong

et al. 2019

Advanced Engineering Informatics

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Visual perception of English letters involves different underlying brain processes including brain activity alteration in multiple frequency bands. However, shape analogous letters elicit brain activities which are not obviously distinct and it is therefore difficult to differentiate those activities. In order to address discriminative feasibility and classification performance of the perception of shape-analogous letters, we performed an experiment in where EEG signals were obtained from 20 subjects while they were perceiving shape analogous letters (i.e., 'p', 'q', 'b', and 'd'). Spectral power densities from five typical frequency bands (i.e., delta, theta, alpha, beta and gamma) were extracted as features, which were then classified by either individual widely-used classifiers, namely k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF) and AdaBoost (ADA), or an ensemble of some of them. The F-score was employed to select most discriminative features so that the dimension of features was reduced. The results showed that the RF achieved the highest accuracy of 74.1% in the case of multi-class classification. In the case of binary classification, the best performance (Accuracy 86.39%) was achieved by the RF classifier in terms of average accuracy across all possible pairs of the letters. In addition, we employed decision fusion strategy to exert complementary strengths of different classifiers. The results demonstrated that the performance was elevated from 74.10% to 76.63% for the multi-class classification and from 86.39% to 88.08% for the binary class classification.

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An improved SIFT algorithm for robust emotion recognition under various face poses and illuminations

Shi

et al. 2019

Neural Comput & Applic

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Analysis of dimension reduction by PCA and AdaBoost on spelling paradigm EEG data

Cited by 5 publications

References 8 publications

A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network

A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network

Classification of brain signal (EEG) induced by shape-analogous letter perception

An improved SIFT algorithm for robust emotion recognition under various face poses and illuminations

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