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In this paper a new method for localization of sources of brain rhythmic activity is presented. The empirical mode decomposition (EMD) method is applied to an appropriate channel and one of the extracted intrinsic mode functions (IMFs) is selected as a reference signal for one of the brain rhythms. Then the spatial notch filter which is a constrained spatial filter based on a reference is applied...
A new supervised approach for decomposition of single channel signal mixtures is introduced in this paper. The performance of the traditional singular spectrum analysis algorithm is significantly improved by applying tensor decomposition instead of traditional singular value decomposition. As another contribution to this subspace analysis method, the inherent frequency diversity of the data has been...
A new method for instantaneous phase tracking of oscillatory signals in a narrow band frequency range is proposed. Empirical mode decomposition (EMD), as an adaptive and data-driven method for analyzing non-linear and non-stationary time series, is applied to a mixture of signals. Then, one of the resulted intrinsic mode functions (IMFs) is used for estimating the instantaneous phase of the signal...
A new and effective approach for mental fatigue analysis is presented here. Empirical mode decomposition (EMD), as a fully adaptive and data-driven method for analyzing nonlinear and nonstationary time series, is presented for measuring the synchronization of the brain rhythms from different brain lobes. The EMD algorithm is applied to a desired channel and each time one of the extracted intrinsic...
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