Individual event-related potentials extraction by VSS LMS adaptive Hermite model filter

Benkherrat, Moncef; Nefati, Tahar; Vidal, Franck; Hasbroucq, Thierry; Burle, Borı́s

doi:10.1109/iecon.2009.5415104

Cited by 2 publications

(3 citation statements)

References 22 publications

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“…with A = exp −2/(L − 3) and B = (1/2) 1 − exp −2/(L − 3) . The SNR r value in Table 3 is the arithmetic mean of the individual values obtained for each two successive sweeps by (16).…”

Section: Real Data Analysismentioning

confidence: 99%

“…Using the orthonormal Hermite basis functions (OHBFs) as an input of the ALC and the variable step-size LMS (VSS-LMS) algorithm to model ERPs seems to be a very promising method. Benkherrat et al [16] already presented general results of the adaptive Hermite model filter for the SNR enhancement of individual ERP responses. However, a study on wider corpus with more details, discussions and evaluations is needed to confirm the pertinence of the method.…”

Section: Introductionmentioning

confidence: 99%

“…Benkherrat et al . [16] already presented general results of the adaptive Hermite model filter for the SNR enhancement of individual ERP responses. However, a study on wider corpus with more details, discussions and evaluations is needed to confirm the pertinence of the method.…”

Section: Introductionmentioning

confidence: 99%

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Denoising of single‐trial event‐related potentials using adaptive modelling

Boudiaf

Benkherrat

Mansouri

2017

IET signal process.

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In this study, the authors present a modelling method based on the adaptive linear combiner to denoise single-trial event-related potentials. The orthonormal Hermite basis functions act as inputs of the adaptive linear combiner. To estimate and to adjust the parameters of the adaptive filter, the authors use the variable step-size least mean square algorithm which is well suited to track rapid changes of non-stationary signals. The performance of the method is tested with simulated evoked potentials and with real visual event-related potentials. For simulated data, the adaptive Hermite model gave significant enhancement in latency and amplitude estimation as well as in the observation of single-trial event-related potentials, in comparison with wavelet techniques and with other models of adaptive filters. For the real data, the proposed method filters the ongoing electroencephalogram activity, thus allowing a better identification of single-trial visual event-related potentials. The results confirm that the Hermite adaptive linear combiner model provides a simple and fast tool that helps to study single-trial event-related potential responses.

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Section: Real Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%