Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition

Abstract

BackgroundBrain oscillatory activities are stochastic and non-linearly dynamic, due to their non-phase-locked nature and inter-trial variability. Non-phase-locked rhythmic signals can vary from trial-to-trial dependent upon variations in a subject's performance and state, which may be linked to fluctuations in expectation, attention, arousal, and task strategy. Therefore, a method that permits the extraction of the oscillatory signal on a single-trial basis is important for the study of subtle brain dynamics, which can be used as probes to study neurophysiology in normal brain and pathophysiology in the diseased.MethodsThis paper presents an empirical mode decomposition (EMD)-based spatiotemporal approach to extract neural oscillatory activities from multi-channel electroencephalograph (EEG) data. The efficacy of this approach manifests in extracting single-trial post-movement beta activities when performing a right index-finger lifting task. In each single trial, an EEG epoch recorded at the channel of interest (CI) was first separated into a number of intrinsic mode functions (IMFs). Sensorimotor-related oscillatory activities were reconstructed from sensorimotor-related IMFs chosen by a spatial map matching process. Post-movement beta activities were acquired by band-pass filtering the sensorimotor-related oscillatory activities within a trial-specific beta band. Signal envelopes of post-movement beta activities were detected using amplitude modulation (AM) method to obtain post-movement beta event-related synchronization (PM-bERS). The maximum amplitude in the PM-bERS within the post-movement period was subtracted by the mean amplitude of the reference period to find the single-trial beta rebound (BR).ResultsThe results showed single-trial BRs computed by the current method were significantly higher than those obtained from conventional average method (P < 0.01; matched-pair Wilcoxon test). The proposed method provides high signal-to-noise ratio (SNR) through an EMD-based decomposition and reconstruction process, which enables event-related oscillatory activities to be examined on a single-trial basis.ConclusionsThe EMD-based method is effective for artefact removal and extracting reliable neural features of non-phase-locked oscillatory activities in multi-channel EEG data. The high extraction rate of the proposed method enables the trial-by-trial variability of oscillatory activities can be examined, which provide a possibility for future profound study of subtle brain dynamics.

Instantaneous Granger Causality with the Hilbert-Huang Transform

“…The only exception is the nonparametric GC computation with WT proposed by Dhamala et al [6]. Indeed, if it were not for these authors' innovative idea of using the WilsonBurg spectral factorization algorithm to obtain the transfer matrix and the error covariance matrix Σ (see (9)), this implementation would has not be possible. Still, the time and frequency resolution of the HHT is far superior to the WT.…”

“…However, it is highly dependent on the quality of the empirical mode decomposition (EMD) algorithm used to compute the IMFs [7]. Although Hilbert spectra and Hilbert marginal spectra have been widely used for the spectral characterization of data [8][9][10], this method has also been used to study phase locking [11,12] and coherence [13,14]. Until now neither GC nor any causality index has been computed with the HHT in spite of previous propositions [14].…”

Адаптивная Фильтрация Физиологических Артефактов На Сигналах Электроэнцефалограмм Человека С Использованием Разложения По Эмпирическим Модам

“…Уже были предприняты попытки разработать методы с использованием эмпирических мод, напри-мер, для удаления глазодвигательных артефактов [19], а также для выделения кортикальных паттернов бета-активности на ЭЭГ [20]. Данные работы продемонстри-ровали перспективность разложения по эмпирическим модам, однако полученные результаты лежат в доста-точно узкой области -удаление артефактов только конкретного типа (глазодвигательных) или выделение весьма специфического типа активности на ЭЭГ.…”

“…Предложенный метод не требует регистрации дополнительных физио-логических сигналов (ЭОГ, ЭКГ, МЭГ), что отличает его от большинства классических методов удаления артефактов, а также предлагает простой и унифициро-ванный алгоритм фильтрации ЭЭГ, структура которого не зависит от типа удаляемых артефактов, но в то же время позволяет удалять артефакты различных типов, в отличие от подходов в работах [19,20].…”

“…Разложение сигнала по эмпирическим модам яв-ляется частью преобразования Гильберта−Хуанга и представляет собой один из современных методов частотно-временного анализа нелинейных нестационар-ных сигналов [17][18][19][20][21]. Данный метод позволяет пред-ставить исследуемый сигнал в виде набора амплитудно-модулированных компонент с нулевым средним -так называемых эмпирических мод.…”