A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking

Han, Jianming; Zhao, Yuwei; Sun, Honghao; Chen, Jiayun; Ke, Ang; Xu, Gesen; Zhang, Hualiang; Zhou, Jin; Wang, Changyong

doi:10.3389/fnins.2018.00217

Cited by 15 publications

(22 citation statements)

References 40 publications

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“…A feature reduction algorithm has been proposed by Han et al [8] for the EEG framework. Pre-processing is performed while using autoregressive coefficients.…”

Section: Related Workmentioning

confidence: 99%

Classification of Motor Imagery Using a Combination of User-Specific Band and Subject-Specific Band for Brain-Computer Interface

Jusas

Samuvel

2019

Applied Sciences

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The essential task of a Brain-Computer Interface (BCI) is to extract the motor imagery features from Electro-Encephalogram (EEG) signals for classifying the thought process. It is necessary to analyse these obtained signals in both the time domain and frequency domains. It is observed that the combination of multiple algorithms increases the performance of the feature extraction process. This paper identifies combinations that have not been attempted previously and improves the accuracy of the overall process, although other authors implemented different combinations of the techniques. The focus is given more on the feature extraction process and frequency bands, which are user-specific and subject-specific frequency bands. In both time and frequency domains, after analysing EEG signals with the time domain parameter, we select the frequency band and the timing while using the Fisher ratio of the time domain parameter (TDP). We used Fisher discriminant analysis (FDA)-type F-score to simultaneously select the frequency band and time segment for multi-class classification. We extracted subject-specific TDP features from the training trials to train the classifier when optimal time-frequency areas were selected for each subject. In this paper, various methods are explored for obtaining the features, which are Time Domain Parameters (TDP), Fast Fourier Transform (FFT), Principal Component Analysis (PCA), R2, Fast Correlation Based Filter (FCBF), Empirical Mode Decomposition (EMD), and Intrinsic time-scale decomposition (ITD). After the extraction process, PCA is used for dimensionality reduction. An efficient result was obtained with the combination of TDP, FFT, and PCA. We used the multi-class Fisher′s linear discriminant analysis (LDA) as the classifier, which was in line with the FDA-type F-score. It is observed that the combination of feature extraction techniques to the frequency bands that were selected by the Fisher ratio and FDA type F-score along with Fisher′s LDA classifier had higher accuracy than the results obtained other researches. A kappa coefficient accuracy of 0.64 is obtained for the proposed technique. Our method leads to better classification performance when compared to state-of-the-art methods. The novelty of the approach is based on the combination of frequency bands and two feature extraction methods.

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“…A feature reduction algorithm has been proposed by Han et al [8] for the EEG framework. Pre-processing is performed while using autoregressive coefficients.…”

Section: Related Workmentioning

confidence: 99%

Classification of Motor Imagery Using a Combination of User-Specific Band and Subject-Specific Band for Brain-Computer Interface

Jusas

Samuvel

2019

Applied Sciences

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“…Han et al [10] proposed an EEG classification framework based on EEG feature compression and convergent iterative channel positioning. The framework begins with an EEG signal pre-processing and single channel based on autoregressive coefficients or time domain parameters feature extraction.…”

Section: Related Workmentioning

confidence: 99%

Classification of Motor Imagery Using Combination of Feature Extraction and Reduction Methods for Brain-Computer Interface

Jusas

Samuvel

2019

ITC

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The motor imagery (MI) based brain-computer interface systems (BCIs) can help with new communication ways. A typical electroencephalography (EEG)-based BCI system consists of several components including signal acquisition, signal pre-processing, feature extraction and feature classification. This paper focuses on the feature extraction step and proposes to use a combination of different feature extraction and feature reduction methods. The research presented in the paper explores the methods of band power, time domain parameters, fast Fourier transform and channel variance for feature extraction. These methods are investigated by combining them in pairs. The application of two feature extraction methods increases the number of selected features that can be redundant or irrelevant. The utilization of too many features can lead to wrong classification results. Therefore, the methods of feature reduction have to be applied. The following feature reduction methods are investigated: principal component analysis, sequential forward selection, sequential backward selection, locality preserving projections and local Fisher discriminant analysis. The combination of the methods of fast Fourier transform, channel variance and principal component analysis performed the best among the combinations of methods. The obtained classification accuracy of the above-mentioned combination of the methods is much higher than that of the individual feature extraction method. The novelty of the approach is based on consolidated sequence of methods for feature extraction and feature reduction.

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“…Redundant task-irrelevant channels tend to introduce undesirable interference and boost noise level, as the activated brain regions for the same intent are relatively small and differ among subjects [17]. Therefore, several MI-relevant channel selection algorithms have been proposed [18]- [22].…”

Section: Introductionmentioning

confidence: 99%

“…TDP Fisher's discriminant analysis (TDP-FDA) described in [21] selects the MI-relevant channels with high Fisher ratios of TDPs. The feature compressing and channel ranking (FCCR) approach described in [22] attempts to reduce the TDP feature dimension by clustering and selects the MI-relevant channels based on a robust feature selection (RFS) algorithm [25].…”

Section: Introductionmentioning

confidence: 99%

Optimal Channel Selection Using Correlation Coefficient for CSP Based EEG Classification

Park

Chung

2020

IEEE Access

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In this paper, we present an optimal channel selection method to improve common spatial pattern (CSP) related features for motor imagery (MI) classification. In contrast to existing channel selection methods, in which channels significantly contributing to the classification in terms of the signal power are selected, distinctive channels in terms of correlation coefficient values are selected in the proposed method. The distinctiveness of a channel is quantified by the number of channels with which it yields large difference in correlation coefficient values for binary motor imagery (MI) tasks, rather than by the largeness of the difference itself. For each distinctive channel, a group of channels is formed by gathering strongly correlated channels and the Fisher score is computed using the feature output, based on the filter-bank CSP (FBCSP) exclusively applied to the channel group. Finally, the channel group with the highest Fisher score is chosen as the selected channels. The proposed method selects the fewest channels on average and outperforms existing channel selection approaches. The simulation results confirm performance improvement for two publicly available BCI datasets, BCI competition III dataset IVa and BCI competition IV dataset I, in comparison with existing methods. INDEX TERMS electroencephalography (EEG), Brain-Computer Interfaces (BCIs), correlation coefficient, common spatial pattern (CSP), channel selection

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A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking

Cited by 15 publications

References 40 publications

Classification of Motor Imagery Using a Combination of User-Specific Band and Subject-Specific Band for Brain-Computer Interface

Classification of Motor Imagery Using a Combination of User-Specific Band and Subject-Specific Band for Brain-Computer Interface

Classification of Motor Imagery Using Combination of Feature Extraction and Reduction Methods for Brain-Computer Interface

Optimal Channel Selection Using Correlation Coefficient for CSP Based EEG Classification

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