Multiple classifier system for EEG signal classification with application to brain–computer interfaces

Ahangi, Amir; Karamnejad, Mehdi; Mohammadi, Nima; Ebrahimpour, Reza; Bagheri, Nasour

doi:10.1007/s00521-012-1074-3

Cited by 77 publications

(33 citation statements)

References 36 publications

(37 reference statements)

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“…Reference [92] considers that the inter-trial changes are inevitable and it is hard to extract consistent features, so the paper clusters the trials with similar characteristics and uses corresponding adjusted classifiers to eliminate the inert-trial instability. Reference [95] combines several classification methods, such as K Nearest Neighbour, Multilayer Perceptron, etc., for introducing robustness to the classifier.…”

Section: Feature Classificationmentioning

confidence: 99%

Review of Brain Computer Interface Components and Corresponding Research Topics

Wang¹,

Wang²,

Liang³

et al. 2016

Advances in Computer Science Research

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Abstract-This paper conducts a research on the literature of Brain Computer Interface(BCI) in recent years, inducing the four components of BCI and the research topics respectively, and providing a guide to newcomers for exploring BCI. BCI is divided into four parts including signal collection, feature extraction, feature classification and practical application. Papers of above four parts are retrieved respectively from the SCI database. Topics of all papers are concluded, based on which the research topics of BCI are assigned to the four parts of BCI. In topics of each part of BCI, similar topics are summarized to the unique topic. Finally some research topics may cover dozens of papers while some topics may cover only several papers. In each of the four BCI components, there are several topics attracting most of the attention. BCI still has a great research value and good prospects for study.

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Section: Feature Classificationmentioning

confidence: 99%

Review of Brain Computer Interface Components and Corresponding Research Topics

Wang¹,

Wang²,

Liang³

et al. 2016

Advances in Computer Science Research

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“…For feature extraction, methods such as Fourier transform, wavelet transforms (WT) and common spatial patterns (CSP) are used.Particularly, time-frequency information is often extracted with the aid of WT due to its ability to achieve high resolutions in the time domain while also preserveresolution in the frequency domain of acquired EEG signals [1][2][3][4].For classification, different approaches have been used, including linear discriminant analysis (LDA) [5], support vector machines (SVM) [6],K-Nearest-Neighbor method (KNN) [7]and Deep Learning (DL) [8].…”

Section: Introductionmentioning

confidence: 99%

Classification of EEG Signal by STFT-CNN Framework: Identification of Right-/left-hand Motor Imagination in BCI Systems

Lu¹,

Jiang²,

Liu³

2017

Proceedings of the 7th International Conference on Computer Engineering and Networks — PoS(CENet2017)

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This paper described the relationship between EEG signals and MI in BCI system. EEG signals are used to classify the direction of motioninto two kinds: left and right. We extracted features from original EEG data using STFT and put them into CNN. The result showed that the framework of STFT-CNN had higher average test accuracy. Furthermore, the generations of motor imagery were analyzed, and the result showed that better classification results will appear in the middle stage with its classification accuracy reaching 92.8%.

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“…There are different features extraction methods for EEG signals suited to discriminate the motor tasks in a BCI paradigm. Among these, the independent component analysis [3], [4], Itakura distances [5]- [7] and phase synchronization methods [8]- [10] are chosen in order to be used for classification with linear discriminant analysis [11], quadratic discriminant analysis [12], Mahalanobis distance [13], the k-nearest neighbors [14], [15] and support vector machine [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Classification Methods for a Motor Task Brain Computer Interface: A Comparative Evaluation for Two Databases

Eva¹,

Lazar²

2017

ijacsa

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Abstract-A comparative evaluation is performed on two databases using three feature extraction techniques and five classification methods for a motor imagery paradigm based on Mu rhythm. In order to extract the features from electroencephalographic signals, three methods are proposed: independent component analysis, Itakura distance and phase synchronization. The last one consists of: phase locking value, phase lag index and weighted phase lag index. The classification of the extracted features is performed using linear discriminant analysis, quadratic discriminant analysis, Mahalanobis distance based on classifier, the k-nearest neighbors and support vector machine. The aim of this comparison is to evaluate which feature extraction method and which classifier is more appropriate in a motor brain computer interface paradigm. The results suggest that the effectiveness of the feature extraction method depends on the classification method used.

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Multiple classifier system for EEG signal classification with application to brain–computer interfaces

Cited by 77 publications

References 36 publications

Review of Brain Computer Interface Components and Corresponding Research Topics

Review of Brain Computer Interface Components and Corresponding Research Topics

Classification of EEG Signal by STFT-CNN Framework: Identification of Right-/left-hand Motor Imagination in BCI Systems

Feature Extraction and Classification Methods for a Motor Task Brain Computer Interface: A Comparative Evaluation for Two Databases

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