scholarly journals State-of-the-art EEG artifact removal evaluation

2021 ◽  
Author(s):  
Zhenyu Jin
Keyword(s):  
Ambient Noise ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Eeg Signals ◽  
Artifact Removal ◽  
Facial Electromyography ◽  
Removal Method ◽  
Independent Components ◽  
Before And After ◽  
Unique Dataset

Object. Electroencephalography (EEG) signals suffer from a low signal-to-noise ratio and are very susceptible to muscular, ambient noise, and other artifacts. Many artifact removal algorithms have been proposed to address this problem. However, the evaluation of these algorithms is conventionally too indirect (e.g., black-box comparisons of brain-computer interface performance before and after removal) because it is unclear which part of the signal represents raw EEG and which is noise. This project objectively benchmarks popular artifact removal algorithms and evaluates the fundamental Independent Component Analysis (ICA) approach thanks to a unique dataset where EEG is recorded simultaneously with other physiological signals-facial electromyography (EMG), accelerometers, and gyroscope-while ten subjects perform several repetitions of common artifact-inflicting tasks (blinking, speaking, etc.). Approach. I have compared the correlation between EEG signals and the artifact-representing channels before and after applying an artifact removal algorithm across the different artifact-inflicting tasks. The extent to which an artifact removal method can reduce this correlation objectively quantifies its effectiveness for the different artifacts. In the same direction, I have determined to what extent ICA successfully detects artefactual components in EEG by comparing the corresponding correlations for independent components that are labeled as artifacts with those labeled as EEG. Main result. The FORCe was found to be the most effective and generic artifact removal method, cleaning almost 40% of artifacts. ICA is shown to be able to isolate almost 70% of artefactual components. Significance. This work alleviates the problem of unreliable evaluation of EEG artifact removal frameworks and provides the first reliable benchmark for the most popular algorithms in this literature.

scholarly journals Removal of Artifacts from EEG Signals: A Review

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 987 ◽  
Author(s):  
Xiao Jiang ◽  
Gui-Bin Bian ◽  
Zean Tian
Keyword(s):  
Comparative Analysis ◽  
Open Problem ◽  
State Of The Art ◽  
Brain Activity ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Artifact Removal ◽  
Eeg Data ◽  
And Behavior ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.

Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury

2020 ◽  
Vol 132 (6) ◽  
pp. 1952-1960 ◽  
Author(s):  
Seung-Bo Lee ◽  
Hakseung Kim ◽  
Young-Tak Kim ◽  
Frederick A. Zeiler ◽  
Peter Smielewski ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Neurological Status ◽  
Cerebrovascular Reactivity ◽  
Cerebral Hypoperfusion ◽  
Clinical Parameters ◽  
Artifact Removal ◽  
Clinical Events ◽  
Arterial Blood ◽  
Before And After

OBJECTIVEMonitoring intracranial and arterial blood pressure (ICP and ABP, respectively) provides crucial information regarding the neurological status of patients with traumatic brain injury (TBI). However, these signals are often heavily affected by artifacts, which may significantly reduce the reliability of the clinical determinations derived from the signals. The goal of this work was to eliminate signal artifacts from continuous ICP and ABP monitoring via deep learning techniques and to assess the changes in the prognostic capacities of clinical parameters after artifact elimination.METHODSThe first 24 hours of monitoring ICP and ABP in a total of 309 patients with TBI was retrospectively analyzed. An artifact elimination model for ICP and ABP was constructed via a stacked convolutional autoencoder (SCAE) and convolutional neural network (CNN) with 10-fold cross-validation tests. The prevalence and prognostic capacity of ICP- and ABP-related clinical events were compared before and after artifact elimination.RESULTSThe proposed SCAE-CNN model exhibited reliable accuracy in eliminating ABP and ICP artifacts (net prediction rates of 97% and 94%, respectively). The prevalence of ICP- and ABP-related clinical events (i.e., systemic hypotension, intracranial hypertension, cerebral hypoperfusion, and poor cerebrovascular reactivity) all decreased significantly after artifact removal.CONCLUSIONSThe SCAE-CNN model can be reliably used to eliminate artifacts, which significantly improves the reliability and efficacy of ICP- and ABP-derived clinical parameters for prognostic determinations after TBI.

scholarly journals EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

2019 ◽  
Vol 9 (11) ◽  
pp. 326 ◽  
Author(s):  
Hong Zeng ◽  
Zhenhua Wu ◽  
Jiaming Zhang ◽  
Chen Yang ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Speaker Recognition ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Single Subject ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Electroencephalogram Eeg ◽  
Deep Learning Model

Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.

scholarly journals Artefact Removal from EEG Signals using Total Variation De-noising

2020 ◽  
Vol 9 (5) ◽  
pp. 2357-2361
Keyword(s):  
Root Mean Square Error ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Mean Square ◽  
Health Issues ◽  
Eeg Signals ◽  
Signal To Noise ◽  
Medical Practitioners ◽  
Artefact Removal ◽  
Two Parameters

Artefacts removing (de-noising) from EEG signals has been an important aspect for medical practitioners for diagnosis of health issues related to brain. Several methods have been used in last few decades. Wavelet and total variation based de-noising have attracted the attention of engineers and scientists due to their de-noising efficiency. In this article, EEG signals have been de-noised using total variation based method and results obtained have been compared with the results obtained from the celebrated wavelet based methods . The performance of methods is measured using two parameters: signal-to-noise ratio and root mean square error. It has been observed that total variation based de-noising methods produce better results than the wavelet based methods.

scholarly journals Wind Turbine Noise Reduction from Seismological Data

2021 ◽  
Author(s):  
Janis Heuel ◽  
Wolfgang Friederich
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
Computational Cost ◽  
Noise Signal ◽  
Noise Model ◽  
Threshold Function ◽  
High Signal ◽  
Time Data ◽  
Negative Effects ◽  
Before And After

<p>Over the last years, installations of wind turbines (WTs) increased worldwide. Owing to<br>negative effects on humans, WTs are often installed in areas with low population density.<br>Because of low anthropogenic noise, these areas are also well suited for sites of<br>seismological stations. As a consequence, WTs are often installed in the same areas as<br>seismological stations. By comparing the noise in recorded data before and after<br>installation of WTs, seismologists noticed a substantial worsening of station quality leading<br>to conflicts between the operators of WTs and earthquake services.</p><p>In this study, we compare different techniques to reduce or eliminate the disturbing signal<br>from WTs at seismological stations. For this purpose, we selected a seismological station<br>that shows a significant correlation between the power spectral density and the hourly<br>windspeed measurements. Usually, spectral filtering is used to suppress noise in seismic<br>data processing. However, this approach is not effective when noise and signal have<br>overlapping frequency bands which is the case for WT noise. As a first method, we applied<br>the continuous wavelet transform (CWT) on our data to obtain a time-scale representation.<br>From this representation, we estimated a noise threshold function (Langston & Mousavi,<br>2019) either from noise before the theoretical P-arrival (pre-noise) or using a noise signal<br>from the past with similar ground velocity conditions at the surrounding WTs. Therefore, we<br>installed low cost seismometers at the surrounding WTs to find similar signals at each WT.<br>From these similar signals, we obtain a noise model at the seismological station, which is<br>used to estimate the threshold function. As a second method, we used a denoising<br>autoencoder (DAE) that learns mapping functions to distinguish between noise and signal<br>(Zhu et al., 2019).</p><p>In our tests, the threshold function performs well when the event is visible in the raw or<br>spectral filtered data, but it fails when WT noise dominates and the event is hidden. In<br>these cases, the DAE removes the WT noise from the data. However, the DAE must be<br>trained with typical noise samples and high signal-to-noise ratio events to distinguish<br>between signal and interfering noise. Using the threshold function and pre-noise can be<br>applied immediately on real-time data and has a low computational cost. Using a noise<br>model from our prerecorded database at the seismological station does not improve the<br>result and it is more time consuming to find similar ground velocity conditions at the<br>surrounding WTs.</p>

scholarly journals Discovery of allenyl acetylene, H2CCCHCCH, in TMC-1

2021 ◽  
Vol 647 ◽  
pp. L3 ◽  
Author(s):  
J. Cernicharo ◽  
C. Cabezas ◽  
M. Agúndez ◽  
B. Tercero ◽  
N. Marcelino ◽  
...  
Keyword(s):  
Column Density ◽  
State Of The Art ◽  
Rotational Temperature ◽  
Signal To Noise Ratio ◽  
Laboratory Data ◽  
The Other ◽  
Signal To Noise ◽  
Chemical Models ◽  
Upper Limit

We present the discovery in TMC-1 of allenyl acetylene, H2CCCHCCH, through the observation of nineteen lines with a signal-to-noise ratio ∼4–15. For this species, we derived a rotational temperature of 7 ± 1 K and a column density of 1.2 ± 0.2 × 1013 cm−2. The other well known isomer of this molecule, methyl diacetylene (CH3C4H), has also been observed and we derived a similar rotational temperature, Tr = 7.0 ± 0.3 K, and a column density for its two states (A and E) of 6.5 ± 0.3 × 1012 cm−2. Hence, allenyl acetylene and methyl diacetylene have a similar abundance. Remarkably, their abundances are close to that of vinyl acetylene (CH2CHCCH). We also searched for the other isomer of C5H4, HCCCH2CCH (1.4-Pentadiyne), but only a 3σ upper limit of 2.5 × 1012 cm−2 to the column density can be established. These results have been compared to state-of-the-art chemical models for TMC-1, indicating the important role of these hydrocarbons in its chemistry. The rotational parameters of allenyl acetylene have been improved by fitting the existing laboratory data together with the frequencies of the transitions observed in TMC-1.

scholarly journals Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data

2010 ◽  
Vol 189 (2) ◽  
pp. 233-245 ◽  
Author(s):  
Robert E. Kelly ◽  
George S. Alexopoulos ◽  
Zhishun Wang ◽  
Faith M. Gunning ◽  
Christopher F. Murphy ◽  
...  
Keyword(s):  
Visual Inspection ◽  
Fmri Data ◽  
Artifact Removal ◽  
Independent Components
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