Improved Filter Selection Method for Filter Bank Common Spatial Pattern for EEG-Based BCI Systems

Park, Geun-Ho; Lee, Yu-Ri; Kim, Hyoung-Nam

doi:10.12720/ijeee.2.2.101-105

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…The simultaneous optimization of the spatial spectrum filter is used in the second type [20,21]. The third type of approach involves filtering the original EEG signals into multiple frequency subbands, then CSP is applied to extract features from these sub-bands, and classification is performed based on the optimum frequency bands [22][23][24][25]. The intelligent optimization method is the fourth kind of technique utilized to determine the optimum frequency bands.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Motor Imagery Classification Performance of a Double-Layered Feature Selection on Two Different-Sized Datasets

et al. 2021

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Numerous investigations have been conducted to enhance the motor imagery-based brain–computer interface (BCI) classification performance on various aspects. However, there are limited studies comparing their proposed feature selection framework performance on both objective and subjective datasets. Therefore, this study aims to provide a novel framework that combines spatial filters at various frequency bands with double-layered feature selection and evaluates it on published and self-acquired datasets. Electroencephalography (EEG) data are preprocessed and decomposed into multiple frequency sub-bands, whose features are then extracted, calculated, and ranked based on Fisher’s ratio and minimum-redundancy-maximum-relevance (mRmR) algorithm. Informative filter banks are chosen for optimal classification by linear discriminative analysis (LDA). The results of the study, firstly, show that the proposed method is comparable to other conventional methods through accuracy and F1-score. The study also found that hand vs. feet classification is more discriminable than left vs. right hand (4–10% difference). Lastly, the performance of the filter banks common spatial pattern (FBCSP, without feature selection) algorithm is found to be significantly lower (p = 0.0029, p = 0.0015, and p = 0.0008) compared to that of the proposed method when applied to small-sized data.

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Section: Introductionmentioning

confidence: 99%

Evaluating the Motor Imagery Classification Performance of a Double-Layered Feature Selection on Two Different-Sized Datasets

et al. 2021

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“…However, the CSP has a limitation, as the frequency band which can successfully discriminate two tasks, has to be manually established by performing repeated experiments. To overcome this limitation, and for simplifying this manual frequency establishment method its enhanced variants, filter bank CSP (FBCSP) and spectrally weighted CSP (SWCSP) were proposed [14]. The techniques of collective optimization of spectral and spatial filters have has gained attention of researchers, which can be a more efficient alternative to manual selection of frequency band for each subject.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Feature Extraction Techniques for Eeg Based Rain-Computer Interface

Ghumman¹,

Singh²

2019

IJCET

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The analysis of electroencephalogram(EEG) signals, for implementation of brain-computer interface (BCI), has enticed a lot of interest in the research community. It can be used in a variety of applications ranging from medical rehabilitation to pleasure and entertainment. BCI is very promising research domain even in the face of a number of challenges, especially in domain of signal processing, feature extraction and that of classification techniques, as these EEG signals contain considerable amount of noise and artifacts. In this paper, various feature extraction algorithms used in BCI are investigated and compared. Common spatial pattern(CSP) is a popularly applied algorithm for extracting features from EEG signals in an implementation of BCI. Filter bank spatial pattern (FBCSP) and spectrally weighted common spatial pattern (SWCSP) are further extensions of CSP. The performance of these methods is evaluated and calculated on data set 2a of BCI competition IV, on the basis of standardized mean squared error (SMSE) . Results shows that FBCSP considerably outperforms the performance of the other methods under consideration.

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Spatial Filter Bank based on Probabilistic Common Spatial Pattern in Motor Imagery BCI systems

Zarei

Ghassemi

2016

2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering

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Improved Filter Selection Method for Filter Bank Common Spatial Pattern for EEG-Based BCI Systems

Cited by 3 publications

References 13 publications

Evaluating the Motor Imagery Classification Performance of a Double-Layered Feature Selection on Two Different-Sized Datasets

Evaluating the Motor Imagery Classification Performance of a Double-Layered Feature Selection on Two Different-Sized Datasets

Comparison of Feature Extraction Techniques for Eeg Based Rain-Computer Interface

Spatial Filter Bank based on Probabilistic Common Spatial Pattern in Motor Imagery BCI systems

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