Classification of Multi-Class BCI Data by Common Spatial Pattern and Fuzzy System

Nguyen, Thanh; Hettiarachchi, Imali; Khatami, Amin; Gordon-Brown, Lee; Lim, Chee Peng; Nahavandi, Saeid

doi:10.1109/access.2018.2841051

Cited by 58 publications

(21 citation statements)

References 35 publications

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“…Furthermore, One-Versus-the-Rest (OVR) approach has been proposed to tackle with multi-class problems [22], which cannot be considered as a genuine extension of two-class CSP to more than two-class cases. In [23] a new classification method has been proposed based on fuzzy system for multi-class problem. The proposed method outperformed in comparison with existing classifiers such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal

et al. 2021

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Common spatial pattern (CSP) is shown to be an effective pre-processing algorithm in order to discriminate different classes of motor-based EEG signals by obtaining suitable spatial filters. The performance of these filters can be improved by regularized CSP, in which available prior information is added in terms of regularization terms into the objective function of conventional CSP. Variety of prior information can be used in this way. In this paper, we used time correlation between different classes of EEG signal as the prior information, which is clarified similarity between different classes of signal for regularizing CSP. Furthermore, the proposed objective function can be easily extended to more than two-class problems. We used three different standard datasets to evaluate the performance of the proposed method. Correlation-based CSP (CCSP) outperformed original CSP as well as the existing regularized CSP, Principle Component Cnalysis (PCA) and Fisher Discriminate Analysis (FDA) in both two-class and multi-class scenarios. The simulation results showed that the proposed method outperformed conventional CSP by 6.9% in 2-class and 2.23% in multi-class problem in term of mean classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal

et al. 2021

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“…It is worth noting that several excellent studies tabulated in Table 5 have also been carried out to classify the similar MI dataset. Nguyen et al (2018) proposed multi-class CSP based feature extraction method and fusion of fuzzy logic system (FLS) particle swarm optimisation (PSO) algorithm for the classification purpose. For the three subjects, they achieved an average accuracy of 86.5%.…”

Section: Discussionmentioning

confidence: 99%

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

Rashid

Bari

Hasan

et al. 2021

PeerJ Computer Science

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Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier’s performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.

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“…The system achieves an accuracy of 64.4%. The work in [34] achieves an accuracy of 77.6% for the data set IIIa (BCI competition III) using a fuzzy system combined with a multi-class extension of the CSP algorithm. Finally, [23] uses the same dataset but with only four classes (the NC class is not included in the work).…”

Section: Discussionmentioning

confidence: 99%

A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface

et al. 2019

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A brain-computer interface (BCI), based on motor imagery EEG, uses information extracted from the electroencephalography signals generated by a person who intends to perform any action. One of the most important issues of current research is how to detect automatically whether the user intends to send some message to a certain device. This study presents a proposal, based on a hierarchical structured system, for recognising intentional and non-intentional mental tasks on a BCI system by applying machine learning techniques to the EEG signals. First-level clustering is performed to distinguish between intentional control (IC) and non-intentional control (NC) state patterns. Then, the patterns recognised as IC are passed on to a second stage where supervised learning techniques are used to classify them. In BCI applications, it is critical to correctly classify NC states with a low false positive rate (FPR) to avoid undesirable effects. According to the literature, we selected a maximum FPR of 10%. Under these conditions, our proposal achieved an average test accuracy of 66.6%, with an 8.2% FPR, for the BCI competition IIIa dataset. The main contribution of this paper is the hierarchical approach, based on machine learning paradigms, which performs intentional and non-intentional discrimination and, depending on the case, classifies the intended command selected by the user.

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Classification of Multi-Class BCI Data by Common Spatial Pattern and Fuzzy System

Cited by 58 publications

References 35 publications

Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal

Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface

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