Bispectral analysis-based approach for steady-state visual evoked potentials detection

Trigui, Omar; Zouch, Wassim; Slima, Mohamed Ben; Messaoud, Mohamed Ben

doi:10.1007/s11042-018-6029-y

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…Previous studies have demonstrated that using bispectrum features as classifier inputs in BCI systems can achieve good performance [41]- [43]. Moreover, it has been shown that bispectrum can work especially well for MI BCIs because of the ERD/ERS phenomenon which is most frequently analyzed in the frequency domain [44].…”

Section: Discussionmentioning

confidence: 99%

Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing

Jin

Chang

Daly

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

107

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The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrumbased channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also

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Section: Discussionmentioning

confidence: 99%

Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing

Jin

Chang

Daly

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

107

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“…SSVEP paradigm was classi ed using canonical correlation analysis (CCA) [26,35,[39][40][41][42][43]. A statistical algorithm based on multivariable analysis is based on correlation between datasets.…”

Section: Feature Classi Cation Modelsmentioning

confidence: 99%

Synchronized Detection of Evoked Potentials to Drive a High Information Transfer Rate Hybrid Brain-Computer Interface Application

Katyal

Singla

2021

ABE

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“…The extensively adopted cognitive control signals in the BCI community are the SSVEP, P300, motor imagery and SCP [10] [7]. Because of the superior SNR together with quicker ITR, the intentness of SSVEP-based BCIs is progressing significantly [11].…”

Section: Introductionmentioning

confidence: 99%

Five-Class SSVEP Response Detection using Common-Spatial Pattern (CSP)-SVM Approach

Rashid

Sulaiman

Mustafa

et al. 2020

IJIE

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Brain-computer interface (BCI) technologies significantly facilitate the interaction between physically impaired people and their surroundings. In electroencephalography (EEG) based BCIs, a variety of physiological responses including P300, motor imagery, movement-related potential, steady-state visual evoked potential (SSVEP) and slow cortical potential have been utilized. Because of the superior signal-to-noise ratio (SNR) together with quicker information transfer rate (ITR), the intentness of SSVEP-based BCIs is progressing significantly. This paper represents the feature extraction and classification frameworks to detect five classes EEG-SSVEP responses. The common-spatial pattern (CSP) has been employed to extract the features from SSVEP responses and these features have been classified through the support vector machine (SVM). The proposed architecture has achieved the highest classification accuracy of 88.3%. The experimental result proves that the proposed architecture could be utilized for the detection of SSVEP responses to develop any BCI applications.

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Bispectral analysis-based approach for steady-state visual evoked potentials detection

Cited by 9 publications

References 26 publications

Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing

Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing

Synchronized Detection of Evoked Potentials to Drive a High Information Transfer Rate Hybrid Brain-Computer Interface Application

Five-Class SSVEP Response Detection using Common-Spatial Pattern (CSP)-SVM Approach

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