Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research.

Methods for artifact detection and removal from scalp EEG: A review

Trends in EEG-BCI for daily-life: Requirements for artifact removal

Electroencephalogram (EEG), boasting the advantages of portability, low cost, and high-temporal resolution, is a non-invasive brain-imaging modality that can be used to measure different brain states. However, EEG recordings are always contaminated with artifacts from different sources other than neurons, which renders EEG data analysis more difficult, and which potentially results in misleading findings. Therefore, it is essential for many medical and practical applications to remove these artifacts in the preprocessing stage before analyzing EEG data. In the last thirty years, various methods have been developed to remove different types of artifacts from contaminated EEG data; still though, there is no standard method that can be used optimally, and therefore, the research remains attractive as well as challenging. This paper presents an extensive overview of the existing methods for ocular, muscle, and cardiac artifact identification and removal with their comparative advantages and limitations. We also reviewed the schemes developed for validating the performances of algorithms with simulated and real EEG data. In future studies, researchers should focus not only on the combining of different methods with multiple processing stages for efficient removal of artifactual interferences but also on the development of standard criteria for validation of recorded EEG signals.

Identification and Removal of Physiological Artifacts From Electroencephalogram Signals: A Review

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.

The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition

Electroencephalogram (EEG) signals are progressively growing data widely known as biomedical big data, which is applied in biomedical and healthcare research. The measurement and processing of EEG ...

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Taxonomy on EEG Artifacts Removal Methods, Issues, and Healthcare Applications

Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.

Automatic Identification and Removal of Ocular Artifacts in EEG—Improved Adaptive Predictor Filtering for Portable Applications

Formation of Au, Pt, and bimetallic Au–Pt nanostructures by thermal dewetting of single-layer Au, Pt and bilayer Au–Pt thin films on Si substrates was systematically studied. The solid-state dewetting of both single-layer and bilayer metallic films was shown to go through heterogeneous void initiation followed by void growth via capillary agglomeration. For the single-layer of Au and Pt films, the void growth started at a temperature right above the Hüttig temperature, at which the atoms at the surface or at defects become mobile. Uniformly distributed Au (7 ± 1 nm to 33 ± 8 nm) and Pt (7 ± 1 nm) NPs with monodispersed size distributions were produced from complete dewetting achieved for thinner 1.7−5.5 nm thick Au and 1.4 nm thick Pt films, respectively. The NP size is strongly dependent on the initial thin film thickness, but less so on temperature and time. Thermal dewetting of Au–Pt bilayer films resulted in partial dewetting only, forming isolated nano-islands or large particles, regardless of sputtering order and total thin film thickness. The increased resistance to thermal dewetting shown in the Au–Pt bilayer films as compared to the individual Au or Pt layer is a reflection of the stabilizing effect that occurs upon adding Pt to Au in the bimetallic system. Energy dispersive x-ray spectroscopic analysis showed that the two metals in the bilayer films broke up together instead of dewetting individually. According to the x-ray diffraction analysis, the produced Au–Pt nanostructures are phase-segregated, consisting of an Au-rich phase and a Pt-rich phase.

Formation of Au, Pt, and bimetallic Au–Pt nanostructures from thermal dewetting of single-layer or bilayer thin films

β-Ga2O3 nanostructures, including nanowires (NWs), nanosheets (NSHs), and nanorods (NRs), were synthesized using thermally dewetted Au nanoparticles as catalyst in a chemical vapor deposition process. The morphology of the as-grown β-Ga2O3 nanostructures depends strongly on the growth temperature and time. Successful growth of β-Ga2O3 NWs with lengths of 7–25 μm, NSHs, and NRs was achieved. It has been demonstrated that the vapor–liquid–solid mechanism governs the NW growth, and the vapor–solid mechanism occurs in the growth of NSHs and NRs. The X-ray diffraction analysis showed that the as-grown nanostructures were highly pure single-phase β-Ga2O3. The bandgap of the β-Ga2O3 nanostructures was determined to lie in the range of 4.68–4.74 eV. Characteristic Raman peaks were observed with a small blue and red shift, both of 1–3 cm−1, as compared with those from the bulk, indicating the presence of internal strain and defects in the as-grown β-Ga2O3 nanostructures. Strong photoluminescence emission in the UV-blue spectral region was obtained in the β-Ga2O3 nanostructures, regardless of their morphology. The UV (374–377 nm) emission is due to the intrinsic radiative recombination of self-trapped excitons present at the band edge. The strong blue (404–490 nm) emissions, consisting of five bands, are attributed to the presence of the complex defect states in the donor (VO) and acceptor (VGa or VGa–O). These β-Ga2O3 nanostructures are expected to have potential applications in optoelectronic devices such as tunable UV–Vis photodetectors.

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β-Ga2O3 Nanostructures: Chemical Vapor Deposition Growth Using Thermally Dewetted Au Nanoparticles as Catalyst and Characterization

Formation and Removal of Alloyed Bimetallic Au–Ag Nanoparticles from Silicon Substrates for Tunable Surface Plasmon Resonance

The gas-phase decomposition kinetics and thermochemistry of 1,1,1,3,3,3-hexamethyldisilazane (HMDSZ) and 1,1,3,3-tetramethyldisilazane (TMDSZ), two potential single-source precursors for the chemical vapor deposition of silicon carbonitride thin films, were systematically investigated using ab initio calculations at the B3LYP/6-311++G(d,p)//CCSD(T)/6-311++G(d,p) level of theory. Both concerted and stepwise decomposition routes for each molecule were examined, allowing for a comparison of the reactions involving the cleavages of common bonds of Si-C, Si-N, and N-H for the two molecules. A new set of reaction pathways open to TMDSZ due to the presence of a Si-H bond was also explored. It was found that all three bonds of Si-N, Si-C, and N-H could be broken more easily in TMDSZ than HMDSZ. Both HMDSZ and TMDSZ are capable of producing silene and silanimine species upon decomposition. In fact, the most kinetically and thermodynamically favorable pathways fall in the formation of these species. The concerted formation of 1-dimethylsilylaminosilene via the elimination of methane from TMDSZ is the most kinetically and thermodynamically favorable route between the two molecules with an activation barrier (ΔH0⧧) of 48.5 kcal mol-1 and reaction enthalpy (ΔH0) of 11.6 kcal mol-1, respectively. These values are lower than the corresponding lowest values in HMDSZ of ΔH0⧧ = 66.4 kcal mol-1 for the concerted production of 1,1-dimethylsilene and trimethylsilylamine and ΔH0 = 41.7 kcal mol-1 for the formation of CH4 and N-trimethylsilyl-1,1-dimethylsilanimine. Overall, this work has provided insights into the reactivity of the two molecules. It has been shown that TMDSZ is more reactive than its analog HMDSZ due to the presence of the Si-H bonds and reduced steric hindrance.

Theoretical Study on the Decomposition Kinetics and Thermochemistry of Tetramethyldisilazane and Hexamethyldisilazane─Formation of Silanimine and Silene Species.

Yujun Shi

Papers

Automatic Identification and Removal of Ocular Artifacts in EEG—Improved Adaptive Predictor Filtering for Portable Applications

Formation of Au, Pt, and bimetallic Au–Pt nanostructures from thermal dewetting of single-layer or bilayer thin films

β-Ga2O3 Nanostructures: Chemical Vapor Deposition Growth Using Thermally Dewetted Au Nanoparticles as Catalyst and Characterization

Formation and Removal of Alloyed Bimetallic Au–Ag Nanoparticles from Silicon Substrates for Tunable Surface Plasmon Resonance

Theoretical Study on the Decomposition Kinetics and Thermochemistry of Tetramethyldisilazane and Hexamethyldisilazane─Formation of Silanimine and Silene Species.