Blind source separation and artefact cancellation for single channel bioelectrical signal

Abstract

Abstract-Bioelectrical signal analysis is gaining significant interests from both academics and industries due to its capability for improved diagnosis and therapy of chronic diseases. In practice, different bio-signals, such as EEG, ECG, EOG and EMG, are usually contaminating each other, and the measured signal is the linear combination of them. It is critical to separate them since analysis of one type or several of them separately is of more interest. In the case of multichannel recording, several blind source separation methods are available to extract its original components. However, for single channel scenarios, the problem has yet to be well studied. Therefore in this paper, we explore blind source separation and artefact cancellation for a single channel signal by combining signal decomposition method singular spectrum analysis (SSA) with different blind source separation methods, such as principal component analysis (PCA), maximum noise fraction (MNF), independent component analysis (ICA) and canonical correlation analysis (CCA). We also systematically compare the separation performance by combing different decomposition methods (wavelet transform (WT), ensemble empirical mode decomposition (EEMD) and SSA) with blind source separation methods (PCA, MNF ICA and CCA). The good simulation results have demonstrated the effectiveness and efficiency of the proposed method.

Identification, characterisation, and correction of artefacts in electroencephalographic data in study of stationary and mobile electroencephalograph

“…The main idea of artefact identification is to determine what frequencies dominate in the recorded signal. If the signal frequencies deviate significantly from the frequency of the EEG signal, the classifier selects this fragment as the artefact [6,12,13].…”

“…The most popular methods of removing artefacts are PCA and ICA [6,16]. This paper contains a comparison between removing artefacts from the signal gathered from a stationary and a mobile amplifier.…”

“…Most often artefacts can be registered by many or even all the electrodes attached to the scalp of the examined person. Along with that, the EEG components occurring in a typical EEG recording are usually quite local [6,7]. Artefact correction is an essential part of pre-processing data to obtain a clean EEG recording [1].…”

“…Mobile EEG does not hamper the examined person due to the absence of cables connecting it with a computer. It also meets the needs of creating BCIs not only because of its mobility but also due to the fact, that this type of EEG is equipped with several auxiliary devices, such as accelerometer, which helps to correct the artefacts from lower levels of recording [6]. The goal of the research was to compare an old-type stationary electroencephalograph with a modern type mobile EEG, in terms of noise level and artefacts.…”

Remove Diverse Artifacts Simultaneously From a Single-Channel EEG Based on SSA and ICA: A Semi-Simulated Study

“…SSA has been used to analyse EEG recordings in several studies. For instance, Maddirala et al proposed to utilize SSA-based method to remove EOG, EMG, [25]- [27] and motion artifacts [28] from single-channel EEG; Zhang et al demonstrated the feasibility of ECG artifacts removal from single-channel EEG by the combination of SSA and BSS method [29]; Hu et al proposed to employ SSA-based method for extracting brain rhythms from EEG recordings [30]. Based on previous studies, we know that SSA has been successfully utilized for various artifacts removal from EEG, even has enabled the separation of different sources overlapping in time-frequency space.…”