EEG Pattern Recognition: An Efficient Improvement Combination of ERD/ERS/Laterality Features to Create a Self-paced BCI System

Avilés‐Cruz, Carlos; Villegas-Cortéz, Juan; Ferreyra-Ramírez, Andrés; López, Arturo Zúñiga

doi:10.1007/978-3-319-39393-3_23

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…Thefeatureextractionstepisthemostinterestingsinceithasthegreatesteffectontheclassification accuracyratewhichrepresentstheabilityofthesystemtopreciselyidentifythebrainstates (Avilés-Cruz et al, 2016). In fact, although the classifier has a high ability to discriminate between the differentclasses,animprecisecharacterisationcanweakenitsperformances.…”

Section: Introductionmentioning

confidence: 99%

Hilbert-Huang Transform and Welch's Method for Motor imagery based Brain Computer Interface

Trigui

Zouch

Messaoud

2017

International Journal of Cognitive Informatics and Natural Intelligence

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The features extraction is the main step in a Brain-Computer Interface (BCI) design. Its goal is to create features easy to be interpreted in order to produce the most accurate control commands. For this end, these features must include all the original signal characteristics. The generated brain's signals' non-stationary and nonlinearity constitute a limitation to the improvement of the performances of systems based on traditional signal processing such as Fourier Transform. This work deals with the comparison of features extraction between Hilbert-Huang Transform (HHT) and Welch's method for Power Spectral Density estimation (PSD) then on the creation of an adaptive method combining the two. The parameters optimization of each method is firstly performed to reach the best classification accuracy rate. The study shows that the PSD estimation is sensitive to the parametric variation whereas the HHT method is mainly robust. The classification results show that an adaptive joint method can reach 90% of accuracy rate for a mental activity period of 1s.

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Section: Introductionmentioning

confidence: 99%

Hilbert-Huang Transform and Welch's Method for Motor imagery based Brain Computer Interface

Trigui

Zouch

Messaoud

2017

International Journal of Cognitive Informatics and Natural Intelligence

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An effective feature extraction method by power spectral density of EEG signal for 2-class motor imagery-based BCI

Kim

Sun

Liú

et al. 2018

Med Biol Eng Comput

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EEG signals have weak intensity, low signal-to-noise ratio, non-stationary, non-linear, time-frequency-spatial characteristics. Therefore, it is important to extract adaptive and robust features that reflect time, frequency and spatial characteristics. This paper proposes an effective feature extraction method WDPSD (feature extraction from the Weighted Difference of Power Spectral Density in an optimal channel couple) that can reflect time, frequency and spatial characteristics for 2-class motor imagery-based BCI system. In the WDPSD method, firstly, Power Spectral Density (PSD) matrices of EEG signals are calculated in all channels, and an optimal channel couple is selected from all possible channel couples by checking non-stationary and class separability, and then a weight matrix which reflects non-stationary of PSD difference matrix in selected channel couple is calculated; finally, the robust and adaptive features are extracted from the PSD difference matrix weighted by the weight matrix. The proposed method is evaluated from EEG signals of BCI Competition IV Dataset 2a and Dataset 2b. The experimental results show a good classification accuracy in single session, session-to-session, and the different types of 2-class motor imagery for different subjects.

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Spectral Subtraction Denoising Preprocessing Block to Improve Slow Cortical Potential Based Brain–Computer Interface

et al. 2017

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EEG Pattern Recognition: An Efficient Improvement Combination of ERD/ERS/Laterality Features to Create a Self-paced BCI System

Cited by 5 publications

References 14 publications

Hilbert-Huang Transform and Welch's Method for Motor imagery based Brain Computer Interface

Hilbert-Huang Transform and Welch's Method for Motor imagery based Brain Computer Interface

An effective feature extraction method by power spectral density of EEG signal for 2-class motor imagery-based BCI

Spectral Subtraction Denoising Preprocessing Block to Improve Slow Cortical Potential Based Brain–Computer Interface

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