TFD thresholding in estimating the number of EEG components and the dominant if using the short-term rényi entropy

Abstract

Time-frequency (TF) based EEG signal analysis using the local or short-term Rényi entropy often requires low-energy cross-terms and noise suppression prior to the estimation of the local number of components and the dominant component instantaneous frequency (IF). This can be easily accomplished by thresholding in the TF domain with the preset TF threshold value, often chosen empirically. The paper investigates the sensitivity of the method based on the local Rényi entropy to the chosen threshold value. The study was performed on real-life left and right hand movements EEG signals. As shown in the paper, the number of the EEG components extracted using the short-term Rényi entropy is highly sensitive to the chosen TF threshold value, unlike the dominant IF which was shown to be highly robust to TF thresholding. Hence, characterization of the EEG signals using the short-term Rényi entropy should include both detecting the number of EEG components and the dominant component IF estimation.