Abstract
Direction of Arrival (DOA) estimation methods, like MUSIC, can be applied to EEG signals for brain source localization. However, they show a severe degradation at small signal-to-noise ratios on the EEG sensors and for large amounts of brain sources. Inspired on the SEAD method, this article introduces a new method that analyses the eigenvalues of a modified spatial covariance matrix of the EEG signals to produce a two-dimensional spectrum whose peaks more robustly estimate the source positions on a horizontal section of the brain. The key approach is to select the eigenvalues that are less affected by the noise and use them to produce the spectrum. To assess the accuracy and robustness of the proposed method, we compared its root-mean-square-error performance at different noise conditions to those of MUSIC and NSF. The proposed method showed the lowest estimation errors for different amounts of brain sources and grid densities.
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Cruz, L.F., Magalhães, M.G., Kunzler, J.A., Coelho, A.A.S., Lemos, R.P. (2019). A New Approach for Brain Source Position Estimation Based on the Eigenvalues of the EEG Sensors Spatial Covariance Matrix. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G. (eds) World Congress on Medical Physics and Biomedical Engineering 2018. IFMBE Proceedings, vol 68/2. Springer, Singapore. https://doi.org/10.1007/978-981-10-9038-7_50
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DOI: https://doi.org/10.1007/978-981-10-9038-7_50
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