Enhancing Event-Related Potentials Based on Maximum a Posteriori Estimation with a Spatial Correlation Prior

Maki, Hayato; Toda, Tomoki; Sakti, Sakriani; Neubig, Graham; Nakamura, Satoshi

doi:10.1587/transinf.2015cbp0008

Cited by 1 publication

(1 citation statement)

References 17 publications

(22 reference statements)

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“…The algorithm is identical to the one with the independent component analysis (ICA) method proposed by Iyer et al [9,10]. Even if the ICA is the common multi-channel method for the EEG analysis, another idea is the maximum likelihoodbased approach that, in contrast with the ICA, does not make assumptions on the number of sources [11].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%