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Adaptive localization of moving EEG sources using augmented complex tensor factorization | IEEE Conference Publication | IEEE Xplore

Adaptive localization of moving EEG sources using augmented complex tensor factorization


Abstract:

In this paper, an adaptive localization algorithm for moving EEG sources based on tensor factorization is proposed. Moreover, using the augmented complex statistics in te...Show More

Abstract:

In this paper, an adaptive localization algorithm for moving EEG sources based on tensor factorization is proposed. Moreover, using the augmented complex statistics in tensor factorization enabled us to exploit the full second order information by involving the effect of pseudo-covariance matrix. We simulated EEG signals by using EEG forward solution for moving source dipoles. Then pairing adjacent electrodes to form complex EEG data facilitates the use of cross information. In order to use the recursive least squares tracking (RLST) as an adaptive version of parallel factor (PARAFAC) algorithm, we generated a third order tensor from stacking augmented covariance matrices. The results of this paper compare two windowing schemes of adaptive algorithm with several metrics.
Date of Conference: 05-07 July 2017
Date Added to IEEE Xplore: 23 October 2017
ISBN Information:
Conference Location: Barcelona, Spain

References

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