Electroencephalogram (EEG) signals are often corrupted with unintended artifacts which need to be removed for extracting meaningful clinical information from them. Typically a priori knowledge of the nature of the artifacts is needed for such purpose. Artifact contamination of EEG is even more prominent for pervasive EEG systems where the subjects are free to move and thereby introducing a wide variety of motion-related artifacts. This makes hard to get a priori knowledge about their characteristics rendering conventional artifact removal techniques often ineffective. In this paper, we explore the performance of two hybrid artifact removal algorithms: Wavelet packet transform followed by Independent Component Analysis (WPTICA) and Wavelet Packet Transform followed by Empirical Mode Decomposition (WPTEMD) in pervasive EEG recording scenario, assuming existence of no a priori knowledge about the artifacts and compare their performance with two existing artifact removal algorithms. Artifact cleaning performance has been measured using Root Mean Square Error (RMSE) and Artifact to Signal Ratio (ASR) - an index similar to traditional Signal to Noise Ratio (SNR), and also by observing normalized power distribution topography over the scalp. Comparison has been made first using semi-simulated signals and then with real experimentally acquired EEG data with commercially available 19-channel pervasive EEG system Enobio corrupted by eight types of artifact. Our explorations show that WPTEMD consistently gives best artifact cleaning performance not only in semi-simulated scenario but also in the case of real EEG data containing artifacts.
Multi-channel EEG, due to its cost-effective and non-invasive nature, has been widely used in various clinical and commercial applications, starting from quantification of cognitive ability of a subject to aid diagnosis of neuro-degenerative diseases and, most recently, in Brain Computer Interface (BCI) application [1]. The most difficult part in dealing with EEG signals is the presence of artifacts that arise due to subject movements, physiological activity (e.g. respiration, cardiac and myogenic) and electrode contact problems. Unlike other physiological signals, e.g. Electrocardiogram (ECG), EEG does not have well-defined signal morphology and therefore it is often difficult to identify the artifacts uniquely from the actual EEG signal, since their frequency spectra often overlap; for instance, muscle activity is characterized by high amplitude, wide spectral distribution and variable topographical distribution [2]. This has triggered a whole body of research work to identify and suppress artifacts from the actual EEG signal. Typically, when the subject is at resting state, only a few artifacts can arise which could be well-characterized. Using this a-priori knowledge, several algorithms have been proposed for artifact removal from EEG following two main approaches:
(a) Measuring artifacts with supplementary sensors like Electro-oculogram (EOG) [10], and then applying linear filtering and regression to separate them from EEG;
(b) Blind Source Separation (BSS) techniques, like Independent Component Analysis (ICA) [3][4][5]. However, the fundamental point in all these approaches remains the same -existence of a-priori knowledge of the characteristics of the artifact based on which the algorithms are tuned to achieve maximal performance.
In the recent years, wireless EEG systems [6] are becoming popular as they use dry contact electrodes which do not require conductive gel and skin preparation [7] resulting in reduced setup time for experiments and data acquisition. Since it allows studying the brain waves in unconstrained naturalistic settings [8], wireless EEG may consent assessment of cognitive functionalities of a subject [1], [9] during daily life. But such potential is hindered by the fact that, due to higher degrees of freedom of body movement, a wide variety of motion artifacts (e.g. head movement in yaw, pitch and roll, hand movement, talking and chewing, etc.) are introduced in recorded EEG. In practice, these movements strongly affect the recordings in such a way that the underlying EEG signal may not be recognizable. The biggest problem is that these artifacts are radically different from the traditional EEG literature (with high inter-trial variability) and being of random natures no a-priori knowledge exists about their characteristics, based on which they could be separated from EEG using the above mentioned approaches (see section 1.2 for more details). Moreover, the number of channels in wireless EEG is often less than the conventional EEG systems; therefore, the assumption that the clean EEG parameters should follow a normal distribution, used in formulating artifact separation algorithm like [10], could be violated because of low numbers of electrodes in pervasive EEG system.
In this paper, following the suggestion made by Uriguen [11], we explore the performance of two hybrid artifact removal techniques in the context of pervasive EEG with limited number of channels:
(a) Wavelet Packet Transform followed by Empirical Mode Decomposition (WPTEMD) (b) Wavelet Packet Transform followed by ICA (WPTICA). The main goal is to assess their performance in identifying and suppressing the artifacts corrupting the EEG signals without requiring any a-priori knowledge of the artifact characteristics, and consequently there is no possibility for tuning the thresholds of the algorithms as in [12]. Specifically, our exploration is targeted towards a pervasive unconstrained EEG scenario where the body movements are allowed. Our exploration started with semi-simulated dataset for benchmarking the performance of these algorithms against two state-of-the-art artifact separation methods based on thresholding criteria, namely wICA [12] and FASTER [10]. The relative performance was analyzed through the Root Mean Squared Error (RMSE) and a new quantitative index, called -Artifact to Signal Ratio (ASR) where the latter is analogous to the well-known Signal to Noise Ratio (SNR) used widely for quantifying signal quality. This exploration with semi-simulated data also helped in standardizing the above mentioned performance metric. Our results showed that these algorithms outperform [12] and [10] by 51.88% at recovering the EEG signal more accurately. Further exploration was carried out using commercially available pervasive EEG acquisition system Enobio [6] to capture EEG data corrupted with movement-related real-life artifacts. Even in these scenario, our results show that the WPTEMD is capable of reducing the
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