Estimating the hurst exponent in motor imagery-based brain computer interface

Abstract

The objective of this paper is to detect sensorimotor rhythms (mu and beta) produced by right and left hand motor imagery. The electroencephalographic (EEG) data were recorded with 8 g.tec active electrodes by means of g.MOBIlab+ module. The EEG data are wavelet multiresolution decomposed into subbands of interest (7.5 -15 Hz-mu rhythm, 15-30Hz-beta rhythm). We applied absolute moment and aggregated variance methods to estimate the Hurst exponent of these decomposed signals, with different types of wavelet. We obtained very good discrimination on channels C3 and CP3 for right hand motor imagery signal and on channels C4 and CP4, when left hand was imaginarily moved. The subjects discriminated better the beta rhythm.

Analysis of similarities in respiratory tract genomes: an approach using the Hurst exponent and the non-decimated discrete wavelet transform

“…The aggregate variance method is based on the self-similarity property of the samples of a process (Aldea and Tarniceriu 2013). When considering X a time series with length N, it is divided into d subseries of length m, and for each aggregate series, composed by an average given by Aldea and Tarniceriu (2013):…”

“…From the perspective of Garcin (2022), this method allows the researcher to make a good assessment of the model specification, and is a generalization of the aggregate variance method as it uses the same principle as 𝑋 𝑚 (Aldea and Tarniceriu 2013).…”

Detection and Classification of Mu Rhythm using Phase Synchronization for a Brain Computer Interface

“…Common spatial pattern (CSP) [2], power spectral density (PSD) [1], adaptive auto regressive (AAR) parameters [3] and Hurst exponent [4] have been used to extract from EEG amplitude distinctive features in different mental states. Phase synchrony is a mechanism for dynamic integration of distributed neural networks in the brain.…”

“…To overcome these problems, methods that are focusing on phase instead of the amplitude have been proposed: the phase locking value (PLV) [8], that uses only the relative phase between signals to measure the phase-synchronization, the imaginary component of the coherency (ImC) [9] as a conservative index of phasesynchronization, phase lag index (PLI) [10] as a potential improvement of the ImC and the weighted phase lag index (WPLI) [4] in order to increase the capacity to detect true changes in phase synchronization.…”