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CLEEGN: A Convolutional Neural Network for Plug-and-Play Automatic EEG Reconstruction

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Abstract and Figures

Human electroencephalography (EEG) is a brain monitoring modality that senses cortical neuroelectrophysiological activity in high-temporal resolution. One of the greatest challenges posed in applications of EEG is the unstable signal quality susceptible to inevitable artifacts during recordings. To date, most existing techniques for EEG artifact removal and reconstruction are applicable to offline analysis solely, or require individualized training data to facilitate online reconstruction. We have proposed CLEEGN, a novel convolutional neural network for plug-and-play automatic EEG reconstruction. CLEEGN is based on a subject-independent pre-trained model using existing data and can operate on a new user without any further calibration. The performance of CLEEGN was validated using multiple evaluations including waveform observation, reconstruction error assessment, and decoding accuracy on well-studied labeled datasets. The results of simulated online validation suggest that, even without any calibration, CLEEGN can largely preserve inherent brain activity and outperforms leading online/offline artifact removal methods in the decoding accuracy of reconstructed EEG data. In addition, visualization of model parameters and latent features exhibit the model behavior and reveal explainable insights related to existing knowledge of neuroscience. We foresee pervasive applications of CLEEGN in prospective works of online plug-and-play EEG decoding and analysis.
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Pin-Hua Lai
National Yang Ming Chiao Tung University
Hsinchu, Taiwan
Wei-Chun Yang
National Yang Ming Chiao Tung University
Hsinchu, Taiwan
Hsiang-Chieh Tsou
National Yang Ming Chiao Tung University
Hsinchu, Taiwan
Chun-Shu Wei
National Yang Ming Chiao Tung University
Hsinchu, Taiwan
Human electroencephalography (EEG) is a brain monitoring modality that senses
cortical neuroelectrophysiological activity in high-temporal resolution. One of
the greatest challenges posed in applications of EEG is the unstable signal qual-
ity susceptible to inevitable artifacts during recordings. To date, most existing
techniques for EEG artifact removal and reconstruction are applicable to offline
analysis solely, or require individualized training data to facilitate online recon-
struction. We have proposed CLEEGN, a novel convolutional neural network for
plug-and-play automatic EEG reconstruction. CLEEGN is based on a subject-
independent pre-trained model using existing data and can operate on a new user
without any further calibration. The performance of CLEEGN was validated us-
ing multiple evaluations including waveform observation, reconstruction error as-
sessment, and decoding accuracy on well-studied labeled datasets. The results of
simulated online validation suggest that, even without any calibration, CLEEGN
can largely preserve inherent brain activity and outperforms leading online/offline
artifact removal methods in the decoding accuracy of reconstructed EEG data. In
addition, visualization of model parameters and latent features exhibit the model
behavior and reveal explainable insights related to existing knowledge of neuro-
science. We foresee pervasive applications of CLEEGN in prospective works of
online plug-and-play EEG decoding and analysis.
Since the first record of human electroencephalogram (EEG) performed almost a century ago (in
1924), EEG has been one of the most widely used non-invasive neural modalities that monitors brain
activity in high temporal resolution (Koike et al., 2013; Mehta & Parasuraman, 2013; Sejnowski
et al., 2014). Among a variety of modalities, EEG has extensive use in the clinical assessment of
neurological and psychiatric conditions, as well as in the research of neuroscience, cognitive science,
psychology, and brain-computer interfacing (BCI).
EEG signals measure subtle fluctuations of the electrical field driven by local neuroelectrophysio-
logical activity of a population of neurons in the brain cortex (Cohen, 2017). While the electrodes
are placed on the surface of the scalp, undesired artifacts may introduce interruption in the mea-
arXiv:2210.05988v1 [eess.SP] 12 Oct 2022
surements and distort the signal of interest. Even in a well-controlled laboratory with a well-trained
subject who can maximally keep the body still and relaxed, the EEG signals, unfortunately, could be
contaminated by inevitable behavioral and physiological artifacts such as eye blinks, reflective mus-
cle movements, ocular activity, cardiac activity, etc (Croft & Barry, 2000; Wallstrom et al., 2004;
Romero et al., 2008). In practice, it is difficult to identify and track the sources of artifacts entirely
due to their diversity and non-stationarity. Noise cancellation and artifact removal remain major
issues in EEG signal processing and decoding.
Currently, numerous methods have been proposed to alleviate the influence of artifacts in EEG
signals. Traditional EEG denoising algorithms include filtering, regression, data separation or de-
composition (Makeig et al., 1995; Islam et al., 2016; Kothe & Jung, 2016). According to previous
meta-analyses on EEG artifact removal literature (Urig¨
uen & Garcia-Zapirain, 2015; Jiang et al.,
2019), independent component analysis (ICA) is especially popular. It is majorly used in 45% of
EEG denoising literature. ICA-based artifact removal estimates the component activity by unmix-
ing the EEG data in the channel domain. Through manual or automatic identification, one can
exclude the artifact components and then reconstruct the EEG data through back projection based
on non-artifact components (Jung et al., 2000a). The fast growth of deep learning methods has
drawn state-of-the-art performances in a variety of machine learning problems (LeCun et al., 2015).
Lately, deep-learning-based EEG reconstruction has drawn much attention (Leite et al., 2018; Sun
et al., 2020; Lopes et al., 2021; Lee et al., 2020; Chuang et al., 2022). Although these methods
can effectively remove artifacts from artificial synthetic signals, their performance in reconstructing
real EEG data has not yet been validated in terms of decoding labeled EEG data. Meanwhile, the
model design of existing deep-learning-based techniques for EEG reconstruction rarely takes the
characteristics of EEG into account.
In this work, we propose CLEEGN, a ConvoLutional neural network for EEG reconstructioN.
CLEEGN is capable of subject-independent EEG construction without any training/calibration for a
new subject. The contributions of this work are three-fold:
a light-weight autoencoder CNN, CLEEGN, with a subject-independent framework that
facilitates plug-and-play EEG reconstruction.
CLEEGN outperforms leading online/offline methods in providing reconstructed EEG data
with the best decoding performance for BCI datasets.
with a novel model design dedicated to EEG reconstruction, CLEEGN characterizes pat-
terns of EEG interpretable and provides neuroscientific insights.
Current processing techniques for EEG artifact removal are highly varied based on the context where
the algorithm may apply. Earlier attempts of EEG denoising assumed that the EEG signals and arti-
facts appear in different frequency ranges. Based on the assumption, some significant artifacts can
be eliminated by the linear filtering method during the online stage (Seifzadeh et al., 2014). Despite
the advantages of low computational time, linear filtering hardly removes artifacts that distribute in
an overlapped frequency range of EEG signals. Another approach, adaptive filtering (Schl¨
ogl et al.,
2007), estimates artifact signals through additional EOG, EMG, ECG channels and removes these
noisy signals from the recording signals by regression. Nevertheless, this approach requires addi-
tional auxiliary electrodes and raises the cost and inconvenience in practical applications. The blind
source separation (BSS) method in EEG denoising was developed by assuming that the recording
EEG signals are linear combinations of the signals from noise sources and the brain neurons. One of
the most well-known BSS method is independent component analysis (ICA) (Jung et al., 2000a;b),
which is able to separate EEG signals into independent components (ICs) (Makeig et al., 1995).
Traditionally, the artifact components extracted by ICA are determined and removed through manual
inspection. Recently developed ICLabel can label the ICs provenance into seven different categories:
brain, eye, heart, muscle, line noise, channel noise, and other (Pion-Tonachini et al., 2019). Artifact
subspace reconstruction (ASR) is another automatic approach, which is based on the principal com-
ponent analysis (PCA) method (Kothe & Jung, 2016). The ASR method selects relatively noiseless
periods from the multi-channel EEG data as reference based on the data distribution. After project-
ing all EEG data to the principal-component domain, high-variance components projected from the
Figure 1: Illustration of the proposed CLEEGN model architecture and the model training flow.
artifacts are detected by a cutoff parameter k. The noiseless signals are reconstructed by preserving
the components without carrying artifacts and back-projected to the time domain. The ASR method
has been shown capable of improving the quality of ICA decomposition (Chang et al., 2020).
Recently, neural network-based methods have been proposed to remove artifacts for EEG data. A
variety of network structures have been applied to the framework for removing EEG artifacts and
reconstructing clean EEG. A deep convolutional autoencoder (Leite et al., 2018) can enhance the
peak-signal-to-noise ratio compared to the linear filtering method via a common CNN autoencoder
structure, which has been widely used on image denoising. Their work shows that it seems practi-
cable to transform the EEG waveform through a CNN structure. Later on, a combined framework
integrating Bayesian deep learning and ICA (Lee et al., 2020) used thresholding of the EEG data
distribution to discard ICs classified as ocular artifacts. These methods leverage the flexibility of
deep learning model design and achieve improvements in their assessments. Considering the non-
stationary property in EEG data and the degradation phenomenon during training, 1D-ResCNN (Sun
et al., 2020) was proposed, which adopted an Inception-Residual module in the network structure.
This network is able to remove EOG, ECG, EMG on single-channel synthesis EEG data. Instead
of using synthesis EEG data, IC-U-Net (Chuang et al., 2022) was created, which generated pairings
of noisy and noiseless EEG data through ICA as training data. The proposed neural network is a
one-dimensional adaptation of U-Net architecture trained with the ensemble of four loss functions
to minimize the difference between amplitude, velocity, acceleration, and frequency components
from the signals. This work showed that their reconstructed signal has higher SNR and can surely
increase the number of brain components classified by ICLabel.
CLEEGN Architecture and Model Training. CLEEGN is designed to map multi-channel noisy
EEG into a latent space and reconstruct it into noiseless EEG signals. The architecture of CLEEGN
is shown in Figure 1. The architecture of the encoder is inspired by an existing CNN model designed
for EEG recognition (Wei et al., 2019) and incorporates convolutional blocks that capture spatiotem-
poral characteristics of EEG data. The first convolution block is used to extract spatial EEG features
through a convolutional layer containing Cspatial filters whose shape is (C, 1), where Cis the num-
ber of electrodes of the EEG signals. The second convolution block is used to extract temporal EEG
features through a convolutional layer containing NFtemporal filters with shape (1,bfs×0.1c),
where fsdenotes the sample frequency of the EEG signals. As for the decoder, we design an
approximately symmetric structure to the encoder with three convolutional blocks. The first convo-
lutional block decodes the EEG feature with NFtemporal kernels with shape is (1,bfs×0.1c). The
second block decodes the EEG feature by a convolution layer with Cspatial kernels of shape (C, 1).
The last convolutional block is for projecting the feature domain back to the original time domain.
Table 1: The architecture of CLEEGN.
Block Layer #kernels Size Output shape
Encoder Input (B,C,T, 1)
Conv2D C(C, 1) (B, 1, T,C)
Permute (B,C,T, 1)
Conv2D NF(1, bfs/10c) (B,C,T,NF)
Decoder Conv2D NF(1, bfs/10c) (B,C,T,NF)
Conv2D C(C, 1) (B,C,T,C)
Conv2D 1 (C, 1) (B,C,T, 1)
C: # channels, T: # time points, fs: Sampling rate, B: Batch size
Figure 2: Schematic of the simulated online processing flow and the assessments of fitness between
the reconstructed and reference EEG signals.
To maintain the shape between the model inputs and outputs, every convolution block applies zero-
padding except the first block in the encoder. The detail of the CLEEGN architecture is available in
Table 1.
As illustrated in Figure 1, the objective of the proposed method is to minimize the difference be-
tween noiseless signals, Y, and the model output, ˆ
Y. The recording EEG data, X, is the combination
of brain signals, Y, and the signals from multiple noise sources, N. CLEEGN is a denoising au-
toencoder that is intended to perform artifact removal on EEG data by creating a mapping between
multi-channel noisy EEG signals, X, and noiseless signals, Y. The training process of CLEEGN
utilizes pairing noisy raw EEG data and noiseless reference EEG data so that the model can learn to
transform noisy EEG data into reconstructed EEG data with maximal similarity to the reference data.
To generate large-scale reference EEG data, we adopted automatic denoising methods, ICLabel and
ASR, to remove artifacts and reconstruct clean waveforms offline. The pairing EEG recording with
Cchannels of continuous noisy/noiseless data was further segmented into training sample pairs.
The input size is (C,T), where Tis the number of time points based on the sampling rate of the
EEG data in 4-second segments. The windows size and stride are set as Tand 0.5T. In the context
of plug-and-play EEG reconstruction, CLEEGN was trained as a subject-independent model. To
perform subject-independent training and testing, we grouped subjects into kdisjoint sets. During
the training process, one of the sets was left out for testing. Subjects’ EEG data in the left-alone set
were not involved in both training and validation. A complete experiment on a single dataset would
result in kdifferent models. The artifact removal performance of a model was evaluated by using
the left-alone set. The number of subjects in one set and the EEG duration available for each subject
depend on the experiment setting and the dataset used.
EEG Reconstruction and Evaluation. The reconstructed EEG data of CLEEGN is generated
through an online reconstruction simulation. In the online stage, the system performs artifact re-
moval on a new subject using a pre-trained CLEEGN model without any requirement of train-
ing/calibration. Figure 2 shows the online EEG reconstruction simulated in offline. The EEG data
Figure 3: Objective EEG quality assessment of the reconstructed EEG based on the decoding per-
Figure 4: Reconstructed EEG signals by CLEEGN across training steps.
from a subject in the left-alone set would be fed into the model sequentially. The stride size of the
sliding window is set to 0.5seconds to minimize the delay in online reconstruction. The fitness of
the model can be evaluated by observing the waveform visually or measuring the similarity using
the mean square error (MSE) between reconstructed and noiseless EEG. Meanwhile, we propose to
use the decoding performance of labeled EEG data as an objective measurement of the reconstructed
EEG quality. We employed EEGNet, a compact CNN for end-to-end EEG decoding (Lawhern et al.,
2018), as the classifier to decode the labeled EEG data in our study.
Data and Model Fitting. In this work, we applied two labeled EEG datasets, the BCI-ERN dataset
1and the MAMEM-SSVEP-II dataset 2. We estimate the classification performance by calculating
the score of Area Under the Curve (AUC) in ERN binary classification and Top-1 accuracy in the
SSVEP task. Figure 3 is the schematic diagram of the process of decoding performance estimation
using the reconstructed EEG data. The segmented EEG epochs are divided into three splits within
each subject: training set, validation set, and test set. The ratio between classes remained the same
in each set. We perform 20 repeated runs with shuffled data to estimate the average performance of
Figure 5: Visualization of raw (gray), reference (red), CLEEGN (blue) EEG waveforms with of-
fline methods by ICLabel, ASR-32, ASR-32-ICLabel, ASR-16, ASR-16-ICLabel, ASR-8, ASR-8-
ICLabel, ASR-4, ASR-4-ICLabel. Each segment plots a five-second segment of signals at Fp1, T7,
Cz, T8, and O2.
Figure 6: (a) Overall fitness of the CLEEGN model across types of reference data using the ERN
dataset. (b) Decoding performance of the CLEEGN-reconstructed EEG data (blue) and the corre-
sponding reference data used for CLEEGN model training (red).
each decoding performance evaluation. Detailed descriptions of the two datasets are available in the
appendix at A.2. Figure 4 presents the loss curve and the reconstruction result at the end of three
different training iterations in a sample run using the ERN dataset. Through this exploration, we
can look into the course of model fitting. In the early stage of training, the model reduces the large
MSE loss by smoothing the input signals. To further minimize the error between reconstruction and
reference (offline denoised signal), the model compensates the intensity to better fit the reference
signals. At the convergent stage of training, we can see the reconstructed waveform is very similar
to the one in reference data.
Types of Reference Data. As ICLabel, ASR, and the hybrid ASR-ICLabel are employed to auto-
matically generate large-scale noiseless reference EEG data offline, it is of our interest to investigate
what type of reference EEG serves as the best noiseless reference data for the CLEEGN model
training. Figure 5 presents the EEG waveforms sampled from Subject 2 in the ERN dataset. The
EEG time series from top to bottom represent the recording of Fp1, T7, Cz, T8, and O2 channels.
The nine sub-figures visualize the comparison between the noisy raw data and the reference data
reconstructed by the ICLabel, ASR with or without ICLabel when the cutoff parameter kwas set to
4, 8, 16, and 32. We can see the waveform of ICLabel and the ASR-ICLabel hybrid method with
4 different cutoff parameters kare similar, and the noiseless waveform reconstructed by CLEEGN
are highly correlated to the result from these 5 methods. High-amplitude ocular artifacts such as
blinking and eye saccades are phenomenal in frontal channels (Fp1) and muscular artifacts featuring
high-frequency (around 24-30 Hz in the βband) are observed on the T7 channel. CLEEGN, ICA,
and ASR-ICLabel hybrid methods can eliminate these kinds of artifacts. Compared to the ICLabel
and ASR-ICLabel hybrid methods, ASR is unable to eliminate the high-frequency EMG artifact
for this dataset. The tolerance of large amplitude EOG artifact increases with the employment of
larger cutoff parameter k. Limited by the reference data generated by ASR, CLEEGN preserves the
high-frequency artifact. As for the large amplitude artifacts that failed to be identified by ASR-32
and ASR-16, we can see that CLEEGN is able to mitigate those EOG artifacts. As shown in Figure
6(a), the reference data prepared by ICLabel offer the lowest MSE, i.e. the best fitness for CLEEGN
training. We also compare the types of reference EEG data and their corresponding CLEEGN recon-
struction results in the decoding performance of the ERN EEG dataset to assess their data quality.
As illustrated in Figure 6(b), CLEEGN reconstruction draws better performances for all denoising
methods. Although the performance decreases with a smaller cutoff parameter k, CLEEGN can
outperform the reference data used for its training. This result suggests that our method not only
removes the artifact but also preserves informative brain activity in the EEG under our cross-subject
training scheme. As the ICLabel provides the reference data with the best decoding performance
and fitness, we chose ICLabel as the source of reference data for further experiments in this study.
Figure 7: Performance of CLEEGN against the training data length per subject evaluated by the
BCI-ERN dataset on (a) the fitness to the reference data; and (b) the decoding performance.
Training Data Size. We explored the effect of data length per subject in the training set regarding
the fitness and the decoding performance of CLEEGN. The training data were segmented from the
first 1, 2, 4, 10, 20, and 30 minutes of data in each EEG recording to investigate the trade-off be-
Table 2: Overall performance over all subjects in the BCI-ERN dataset.
Method MSE AUC Total parameters
ICLabel 0.7218±0.0197
1D-ResCNN 6.7147±0.5025 0.6697±0.0175 325891
IC-U-Net 5.2086±0.4204 0.6949±0.0140 2683192
CLEEGN 3.5984±0.2538 0.7252±0.0189 220755
Figure 8: EEG reconstruction performance of CLEEGN against the number of subjects evaluated
under the BCI-ERN dataset by (a) the fitness to the reference data; and (b) the decoding performance.
tween training time and reconstruction performance. Figure 7(a) shows the fitness of CLEEGN to
the reference data. The model trained using the first 10 minutes obtain the minimal value among
all duration configurations. Though the difference in MSE between 10, 20, and 30 minutes is not
noticeable, Figure 7(b) shows that using the 10-minute training data yields the best decoding per-
formance among all settings. Interestingly, with only one-minute training data from each subject,
CLEEGN can achieve comparable decoding performance to the reference data. This indicates that
CLEEGN retains its performance even when each subject only contributes a short recording for
training. In addition, we explore the effect of the number of subjects included in the training set
on the fitness of CLEEGN model training and the decoding performance of the reconstructed EEG
data. We randomly reduced the number of subjects for training from 12 to 6, 4, and 2. Since the
subset of subjects may influence the performance, we tested multiple combinations and averaged
the results. With the decrease in the number of subjects, the MSE value and the standard error (light
span area) increase in Figure 8(a), which indicates that the generalization ability of the CLEEGN
model reduces when fewer subjects are included for training. In Figure 8(b), we can observe a slight
decrease in decoding performance. We consider the number of subjects as an essential factor in the
performance of CLEEGN, yet it requires only a few subjects to achieve a satisfactory performance
compared to the reference data.
Performance Comparison. We compare the performance of CLEEGN against three baseline meth-
ods, ICLabel, 1D-ResCNN, and IC-U-Net. Except for the ICLabel that operates offline, CLEEGN
and the other two neural network-based methods perform a simulated online reconstruction based
on the same training process with the reference data generated by ICLabel. Results of the ERN EEG
dataset are shown in Table 2. CLEEGN has the best fitness to the reference data with the minimum
MSE, the highest AUC score in the decoding performance, and the least parameters. For the SSVEP
EEG data, although not having the minimum MSE, CLEEGN outperforms other methods in the
decoding accuracy and the model size. The evaluation across the two datasets suggests an over-
all superiority of our proposed CLEEGN model over other existing neural network-based methods
in online reconstruction. CLEEGN even provides a better reconstruction than the offline ICLabel.
These promising results indicate the usability of CLEEGN in online training/calibration-free EEG
reconstruction that truly meets the need for real-world applications of EEG-based BCI.
Table 3: Overall performance over all subjects in the MAMEM-SSVEP-II dataset.
Method MSE Accuracy Total parameters
ICLabel 0.4940±0.0667
1D-ResCNN 0.2047±0.0524 0.2908±0.0316 313735
IC-U-Net 0.0698±0.0085 0.3422±0.0253 2664724
CLEEGN 0.0777±0.0111 0.5159±0.0622 14043
Figure 9: PCA visualization of latent features in CLEEGN for a single subject in the ERN dataset.
(a) The noisy EEG data projected on the PCA space of the noisy EEG data. (b)-(e) Latent features
in the first to fourth convolutional layers projected on the original noisy PCA space. (f) CLEEGN
reconstructed EEG data projected on the original noisy PCA space.
Visualization. On account of the interpretability of the CLEEGN model, we visualized its inter-
mediate latent features by mapping onto a 2-dimensional domain based on the principal component
analysis (PCA) of the noisy raw EEG data (Wold et al., 1987). Figure 9(a) shows the principal
component space and the noisy EEG data channel projections. We observe that the scatters of noisy
EEG data retain the spatial relationship of the actual EEG electrodes. The arrangement of frontal
electrodes (prefix in Fp, P) to the posterior (prefix in P, O) are projected along the x-axis from right
to left. The projection from top to bottom matches the left-side electrodes (suffixed in odd numbers),
central electrode (suffixed in z), and right-side electrodes (suffixed in even numbers). The projec-
tion suggests that EEG data of adjacent channels tend to show similar waveforms. The blue dots in
Figure 9(b), (c) are the projection of latent features resulting from the first and second convolutional
layers, which make up the encoder design of CLEEGN. The projection of latent features from the
next two convolutional layers is presented in Figure 9(d), (e). From Figure 9(b) to (e), we can see
the distribution range of the latent features shrink in the encoder design and expand in the decoder
design of CLEEGN. Figure 9(f) compares the distribution of noisy data and CLEEGN reconstructed
data, showing that the CLEEGN reconstructed data is a more compact cluster than the noisy EEG
in the PCA space. This implies that the process within CLEEGN includes a projection of the orig-
inal EEG data in the upstream layer, complex temporal filtering and combination in the midstream
layers, and a final projection that converts the noiseless latent features back to the channel domain.
In this work, we have proposed CLEEGN, a novel convolutional neural network for plug-and-play
automatic EEG reconstruction. The training of CLEEGN leverages the conventional offline de-
noising methods, ASR and ICLabel, with automatic component classification to generate abundant
noiseless EEG data. The performance of CLEEGN was objectively validated using multiple eval-
uations including waveform observation; reconstruction error assessment; and decoding accuracy
on well-studied, labeled datasets. The experiment results suggest that, even without any calibra-
tion, CLEEGN can predominantly preserve inherent brain activity. According to the decoding per-
formance of reconstructed EEG data, CLEEGN outperforms other neural network-based denois-
ing methods on both ERN and SSVEP EEG decoding. From the visualization of the waveform,
CLEEGN can remove artifacts from different sources and the waveform is highly correlated to the
reference data. Through the visualization of model parameters and latent features, we exhibit model
behavior and reveal explainable insights related to existing knowledge of neuroscience. We foresee
pervasive applications of CLEEGN in prospective works of EEG decoding and analysis.
This work was supported in part by the National Science and Technology Council (109-2222-E-
009-006-MY3, 110-2221-E-A49-130-MY2, and 110-2314-B-037-061) and in part by the Higher
Education Sprout Project of the National Yang Ming Chiao Tung University and Ministry of Educa-
tion of Taiwan.
Chi-Yuan Chang, Sheng-Hsiou Hsu, Luca Pion-Tonachini, and Tzyy-Ping Jung. Evaluation of
artifact subspace reconstruction for automatic artifact components removal in multi-channel
eeg recordings. IEEE Transactions on Biomedical Engineering, 67(4):1114–1121, 2020. doi:
Chun-Hsiang Chuang, Kong-Yi Chang, Chih-Sheng Huang, and Tzyy-Ping Jung. Ic-u-net: A u-
net-based denoising autoencoder using mixtures of independent components for automatic eeg
artifact removal. NeuroImage, 263:119586, 2022. ISSN 1053-8119.
Michael X Cohen. Where does eeg come from and what does it mean? Trends in neurosciences, 40
(4):208–218, 2017.
Rodney J Croft and Robert J Barry. Removal of ocular artifact from the eeg: a review. Neurophysi-
ologie Clinique/Clinical Neurophysiology, 30(1):5–19, 2000.
Md Kafiul Islam, Amir Rastegarnia, and Zhi Yang. Methods for artifact detection and removal from
scalp eeg: A review. Neurophysiologie Clinique/Clinical Neurophysiology, 46(4):287–305, 2016.
ISSN 0987-7053. doi:
Xiao Jiang, Gui-Bin Bian, and Zean Tian. Removal of artifacts from eeg signals: a review. Sensors,
19(5):987, 2019.
Tzyy-Ping Jung, Scott Makeig, Colin Humphries, Te-Won Lee, Martin J Mckeown, Vicente Iragui,
and Terrence J Sejnowski. Removing electroencephalographic artifacts by blind source separa-
tion. Psychophysiology, 37(2):163–178, 2000a.
Tzyy-Ping Jung, Scott Makeig, Marissa Westerfield, Jeanne Townsend, Eric Courchesne, and Ter-
rence J Sejnowski. Removal of eye activity artifacts from visual event-related potentials in normal
and clinical subjects. Clinical Neurophysiology, 111(10):1745–1758, 2000b.
Shinsuke Koike, Yukika Nishimura, Ryu Takizawa, Noriaki Yahata, and Kiyoto Kasai. Near-infrared
spectroscopy in schizophrenia: a possible biomarker for predicting clinical outcome and treatment
response. Frontiers in psychiatry, 4:145, 2013.
Christian Andreas Edgar Kothe and Tzyy-Ping Jung. Artifact removal techniques with signal recon-
struction, 2016. US Patent 14/895,440.
Vernon J Lawhern, Amelia J Solon, Nicholas R Waytowich, Stephen M Gordon, Chou P Hung, and
Brent J Lance. Eegnet: a compact convolutional neural network for eeg-based brain–computer
interfaces. Journal of neural engineering, 15(5):056013, 2018.
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. nature, 521(7553):436–444,
Sangmin S Lee, Kiwon Lee, and Guiyeom Kang. Eeg artifact removal by bayesian deep learning
& ica. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine &
Biology Society (EMBC), pp. 932–935. IEEE, 2020.
Niago Moreira Nobre Leite, Eanes Torres Pereira, Edmar Candeia Gurj˜
ao, and Luciana Ribeiro
Veloso. Deep convolutional autoencoder for eeg noise filtering. In 2018 IEEE International
Conference on Bioinformatics and Biomedicine (BIBM), pp. 2605–2612. IEEE, 2018.
abio Lopes, Adriana Leal, J´
ulio Medeiros, Mauro F. Pinto, Ant ´
onio Dourado, Matthias
umpelmann, and C´
esar Teixeira. Automatic electroencephalogram artifact removal using deep
convolutional neural networks. IEEE Access, 9:149955–149970, 2021.
Scott Makeig, Anthony Bell, Tzyy-Ping Jung, and Terrence J Sejnowski. Independent component
analysis of electroencephalographic data. In D. Touretzky, M.C. Mozer, and M. Hasselmo (eds.),
Advances in Neural Information Processing Systems, volume 8. MIT Press, 1995.
Ranjana K Mehta and Raja Parasuraman. Neuroergonomics: a review of applications to physical
and cognitive work. Frontiers in human neuroscience, 7:889, 2013.
Luca Pion-Tonachini, Ken Kreutz-Delgado, and Scott Makeig. Iclabel: An automated electroen-
cephalographic independent component classifier, dataset, and website. NeuroImage, 198:181–
197, 2019.
Sergio Romero, Miguel A Ma˜
nanas, and Manel J Barbanoj. A comparative study of automatic tech-
niques for ocular artifact reduction in spontaneous eeg signals based on clinical target variables:
a simulation case. Computers in biology and medicine, 38(3):348–360, 2008.
A. Schl¨
ogl, C. Keinrath, D. Zimmermann, R. Scherer, R. Leeb, and G. Pfurtscheller. A fully au-
tomated correction method of eog artifacts in eeg recordings. Clinical Neurophysiology, 118(1):
98–104, 2007. ISSN 1388-2457.
Sahar Seifzadeh, Karim Faez, and Mahmood Amiri. Comparison of different linear filter design
methods for handling ocular artifacts in brain computer interface system. Journal of Computer &
Robotics, 7:51–56, 2014. ISSN 2345-6582.
Terrence J Sejnowski, Patricia S Churchland, and J Anthony Movshon. Putting big data to good use
in neuroscience. Nature neuroscience, 17(11):1440, 2014.
Weitong Sun, Yuping Su, Xia Wu, and Xiaojun Wu. A novel end-to-end 1d-rescnn model to remove
artifact from eeg signals. Neurocomputing, 404:108–121, 2020. ISSN 0925-2312.
Jose Antonio Urig¨
uen and Bego˜
na Garcia-Zapirain. Eeg artifact removal—state-of-the-art and
guidelines. Journal of neural engineering, 12(3):031001, 2015.
Garrick L Wallstrom, Robert E Kass, Anita Miller, Jeffrey F Cohn, and Nathan A Fox. Automatic
correction of ocular artifacts in the eeg: a comparison of regression-based and component-based
methods. International journal of psychophysiology, 53(2):105–119, 2004.
Chun-Shu Wei, Toshiaki Koike-Akino, and Ye Wang. Spatial component-wise convolutional net-
work (sccnet) for motor-imagery eeg classification. In 2019 9th International IEEE/EMBS Con-
ference on Neural Engineering (NER), pp. 328–331. IEEE, 2019.
Svante Wold, Kim Esbensen, and Paul Geladi. Principal component analysis. Chemometrics and
intelligent laboratory systems, 2(1-3):37–52, 1987.
For training CLEEGN, we used the Adam optimizer with an initial learning rate of 1e-3 without
weight decay. Besides, the exponential learning rate scheduler is applied with a gamma of 0.8. As
for the loss function, we used Mean Squared Error (MSE). The batch size is set to 64 and the total
training epoch is 40. During the training procedure, the model is evaluated using the validation
subset at the end of every epoch with the purpose of saving the weights that achieved the lowest
validation loss.
A.1.2 IC-U-NET
The optimizer adopted in IC-U-Net (Chuang et al., 2022) training is SGD with an initial learning rate
of 1e-2, momentum of 0.9, and weight decay of 5e-4. The learning rate scheduler used in the training
procedure is the multistep scheduler. As for the loss function, a novel ensemble loss proposed in IC-
U-Net is adopted. This ensemble is a simple linear combination of the Mean Squared Error (MSE)
in amplitude, velocity, acceleration, and frequency components of EEG signals. Each term in the
loss function has same weight. The batch size is set to 64 and the total training epoch is 200. The
weight saving strategy in IC-U-Net training is the same as CLEEGN.
A.1.3 1D-RES CNN
The Adam optimizer with an initial learning rate of 1e-3 and the Mean Squared Error (MSE) loss
function is adopted in 1D-ResCNN (Sun et al., 2020) training. Since the 1D-ResCNN is developed
under one-dimensional synthesized EEG data and no explicit instruction of using multi-channel
EEG provided, we trained 1D-ResCNN using two different method. For one method, we trained
multiple models for each EEG channel respectively. For the other method, we viewed each multi-
channel EEG segment as a batch and the channel arrangement within a batch is fixed. Under our
experiment, the result showed that the second method not only provides an efficient training process,
but also results in a better reconstructed performance. The same weight saving strategy in CLEEGN
is adopted in 1D-ResCNN training.
EEGNet (Lawhern et al., 2018) is a famous EEG decoding model and widely used in EEG literature.
In the original EEGNet paper, they investigated their proposed model with a different number of
kernels and denoted the model with F1temporal filters and Dspatial filters as EEGNet-F1,D. We
use two different settings to train the two datasets used. In ERN classification, we use the EEGNet-
8,2 structure suggested by the EEGNet paper. As for the SSVEP classification, an experimental
result showed that EEGNet-100,8 can draw the best performance. We trained and evaluated the
decoding performance individually for each subject. We divided the collection of event epochs into
three splits within each subject: training set, validation set, and test set with a ratio of 3:1:1. The
ratio between classes remained the same in each set. The loss function is categorical cross entropy
(CCE) and the Adam optimizer is adopted with a learning rate of 103and zero weight decay. The
batch size is set to 32 and the total training epoch is 200.
Error-related negativity (ERN) can be categorized as a kind of event-related potential (ERP), which
occurs after an erroneous or abnormal event perceived by the subject. One characteristic of the feed-
back ERN is a relatively large negative amplitude approximately 350 ms and a positive amplitude
approximately 500 ms after visual feedback triggered by the error event. In this work, we mainly
use a well-studied EEG dataset from the BCI Challenge competition hosted by Kaggle to evaluate
the artifact removal effectiveness. This dataset includes EEG recordings of 26 subjects (16 subjects
labeled and 10 subjects unlabeled) that participated in a P300 speller task. P300 speller is a well-
known BCI system that develops a typing application through P300 response evoked potential. The
ERN experiment was conducted under the assumption that the ERN occurred if the subject received
incorrect prediction (feedback) from the P300 speller. The objective of the competition was to im-
prove the P300 speller performance by implementing error correction through ERN potentials. We
used the 16 subjects with labeled data of which the sampling rate is 200 Hz initially with 56 passive
Ag/AgCl EEG sensors.
In the interest of increasing the usability of EEG data, we applied some pre-processing procedures
to each EEG recording. The EEG data were down-sampled to 128 Hz and re-referenced by the
common average reference (CAR) method to eliminate common-mode noise and to zero-center the
data. Each recording was band-pass filtered to 1-40 Hz through the FIR filter implemented by
EEGLAB to remove DC drifting. During EEG decoding evaluation, we epoch EEG signals in [0,
1.25] second interval to obtain correct and erroneous feedback.
Steady state visually evoked potential (SSVEP) is another kind of ERP that is characterized as peri-
odic potential induced by rapidly repetitive visual stimulation. The SSVEP is composed of several
discrete frequency components, which consist of the fundamental frequency of the visual stimulus
as well as its harmonics. To investigate the generalization ability of the model, we use ”EEG SSVEP
Dataset II” from Multimedia Authoring & Management using your Eyes & Mind (MAMEM). The
dataset includes EEG data from 11 subjects and consists of five different frequencies (6.66, 7.50,
8.57, 10.00, and 12.00 Hz). Each subject was recorded in five sessions and each session included
25 trials (5 trials for each class). The data used a 256-channel HydroCel Geodesic Sensor Net
(HCGSN) and captured the signals with a sampling rate of 250 Hz. Since there are several different
bad channels in each subject’s EEG recording, we preserved 20 common channels from each sub-
ject to train the CLEEGN model. Every recording was down-sampled to 125 Hz, re-referenced by
the common average reference (CAR) method, and band-pass filtered to 1-40 Hz. We epoch EEG
signals in [1, 5] second interval for each event recorded timestamp. The first second was discarded
under the consideration of a reaction delay of the subject.
In this work, we mainly use decoding performance as the assessment of different artifact removal
methods. We provide an interpretation of the relationship between EEG quality and decoding per-
formance. In Section 4 SSVEP result, IC-U-Net can optimize the weight with minimal MSE value
among all compared artifact removal networks. However, the decoding performance is worse than
CLEEGN and even the reference method (ICLabel), which implies that the average error over data
points (MSE) is not the best assessment of EEG quality.
Figure 10: Power spectrum density of EEG data on different methods to each class
In the SSVEP experiment, the fundamental frequency of the external rapidly repetitive visual stim-
ulation and its harmonics can be an important feature in classification. Hence, higher quality in
the frequency component of EEG data is required. We use the power spectrum density (PSD) to
interpret the result of decoding performance. Figure 10 shows the PSD of EEG event data in each
class denoised by different methods. We can see the reconstructed EEG spectrum from CLEEGN in
each class is similar to the reference. As for IC-U-Net, it seems that the model can not completely
reconstruct in several frequency bands (15-20Hz, 30-40Hz), which leads to the low decoding per-
formance. The PSD result shows that there is great distortion in the power density of reconstructed
data generated by 1D-ResCNN. Since 1D-ResCNN is a one-dimensional structure, we hypothesize
that spatial information is important in EEG artifact removal.
Method ICLabel ASR-32 ASR-32-ICA
S02 3.2835 0.8432 9.4341 0.8102 3.6285 0.8398
S06 2.5723 0.6609 10.7221 0.6234 2.7348 0.6093
S07 4.7804 0.7835 17.2428 0.7155 4.6049 0.6889
S11 7.2317 0.6214 9.2276 0.5939 6.7069 0.6243
S12 3.2090 0.7126 16.5107 0.6789 3.0982 0.6794
S13 4.3666 0.7860 9.7730 0.7937 4.0034 0.7849
S14 2.0056 0.8256 2.6510 0.7951 1.9174 0.8030
S16 6.3664 0.6758 15.2617 0.6437 5.9677 0.6542
S17 4.5280 0.8068 10.1852 0.8080 8.1861 0.8023
S18 4.3724 0.7629 9.2606 0.6835 3.6407 0.7331
S20 4.6405 0.7022 11.5464 0.6898 3.7116 0.6669
S21 5.0383 0.7219 21.3344 0.6827 5.0986 0.6569
S22 4.5625 0.7300 20.2808 0.6334 4.8429 0.6850
S23 3.4311 0.7086 3.8892 0.6991 2.5044 0.7163
S24 3.2064 0.7310 4.7346 0.7297 3.6940 0.7045
S26 5.3556 0.5614 11.9691 0.5661 6.4727 0.5475
Table 4: Mean square error between reference denoising method and CLEEGN (MSE), AUC score
of CLEEGN (AUC) in ”BCI-Challenge” ERN dataset - Part1
Method ASR-16 ASR-16-ICA ASR-8
S02 4.2217 0.8205 3.5384 0.8425 4.0614 0.8205
S06 4.4036 0.5513 3.2639 0.6388 3.9688 0.6388
S07 11.4515 0.7116 5.1372 0.6588 8.2052 0.6717
S11 8.4986 0.5803 4.9902 0.6061 7.0836 0.5983
S12 7.1270 0.6980 4.1351 0.6690 4.6099 0.7005
S13 5.0222 0.7870 6.5591 0.7680 4.5217 0.7778
S14 2.2373 0.8137 2.5573 0.7723 2.6699 0.7982
S16 9.6206 0.6680 4.9104 0.6554 6.8716 0.6660
S17 18.8896 0.8076 10.9239 0.8041 27.6019 0.8141
S18 5.2366 0.7139 3.6065 0.7264 4.3582 0.7006
S20 6.7533 0.6843 5.5760 0.6668 4.3853 0.6776
S21 8.5629 0.6501 6.6647 0.6338 6.4900 0.6674
S22 9.3490 0.6395 5.1241 0.6460 6.7729 0.6419
S23 3.1867 0.6795 4.4331 0.6874 2.4365 0.7022
S24 2.7711 0.7191 3.7558 0.7130 3.7320 0.7117
S26 7.6073 0.5861 6.4456 0.5648 9.1152 0.5652
Table 5: Mean square error between reference denoising method and CLEEGN (MSE), AUC score
of CLEEGN (AUC) in ”BCI-Challenge” ERN dataset - Part2
Method ASR-8-ICA ASR-4 ASR-4-ICA
S02 3.4726 0.8277 4.0551 0.8204 3.3401 0.8249
S06 2.9740 0.6257 4.4043 0.5893 4.1949 0.6526
S07 5.4224 0.6423 8.4744 0.6592 4.7362 0.6969
S11 4.3832 0.6035 9.2528 0.5868 4.9227 0.5972
S12 3.5382 0.6800 3.9801 0.6969 3.8259 0.6732
S13 5.4227 0.7921 5.5527 0.7833 6.5429 0.7628
S14 2.1820 0.8004 3.6646 0.7947 2.9259 0.8057
S16 4.8361 0.6685 8.4332 0.6379 4.0452 0.6757
S17 11.1061 0.8014 24.9330 0.8102 16.4403 0.8141
S18 3.4891 0.7191 4.0793 0.7468 3.2979 0.7322
S20 4.6687 0.6625 4.9512 0.6444 4.6338 0.6505
S21 6.1690 0.6518 5.0372 0.6479 4.5595 0.6507
S22 5.1410 0.6399 6.7985 0.6566 5.5497 0.6801
S23 3.5369 0.6935 3.6311 0.6993 3.9354 0.6870
S24 2.8299 0.7215 4.2418 0.7231 3.7892 0.7075
S26 6.8030 0.5495 12.7965 0.5704 7.7062 0.5595
Table 6: Mean square error between reference denoising method and CLEEGN (MSE), AUC score
of CLEEGN (AUC) in ”BCI-Challenge” ERN dataset - Part3
DataLength (min) ICLabel 30 20 10
S02 0.8114 3.4818 0.8290 3.6152 0.8359 3.5139 0.8191
S06 0.6281 2.4449 0.6117 2.4496 0.6195 2.3353 0.6215
S07 0.7298 4.1740 0.7177 4.3742 0.6744 4.2118 0.7166
S11 0.5971 7.0916 0.6050 6.8226 0.6107 7.6580 0.6037
S12 0.6916 2.6238 0.6700 2.7464 0.6724 2.3652 0.6949
S13 0.7859 3.7257 0.7855 3.9133 0.7774 3.5441 0.7926
S14 0.7888 1.7715 0.7766 1.7445 0.7959 1.6376 0.8086
S16 0.6503 5.5278 0.6585 5.5853 0.6811 5.2618 0.6878
S17 0.8198 4.6574 0.8162 4.4208 0.8069 4.4521 0.8066
S18 0.6997 3.3821 0.6965 3.4677 0.6899 3.7442 0.7045
S20 0.6829 3.4097 0.6873 3.8692 0.6669 3.4116 0.6960
S21 0.6198 4.0377 0.6760 3.7572 0.6918 3.7251 0.6733
S22 0.6372 3.2582 0.6920 3.4192 0.6383 3.0667 0.6600
S23 0.6985 2.3770 0.6994 2.6990 0.7050 2.3144 0.7213
S24 0.7339 2.1982 0.7108 2.1782 0.7317 2.0520 0.7396
S26 0.5867 3.9663 0.5701 3.9898 0.5524 4.0011 0.5566
Table 7: Mean square error between reference denoising method and CLEEGN (MSE), AUC score
of CLEEGN (AUC) in ”BCI-Challenge” ERN dataset trained by different data length - Part1
DataLength (min) ICLabel 4 2 1
S02 0.8114 3.2609 0.8101 3.1745 0.8324 3.3467 0.8213
S06 0.6281 2.2442 0.6093 3.0822 0.6135 3.3718 0.6302
S07 0.7298 4.2698 0.6655 4.6784 0.6774 3.8478 0.7180
S11 0.5971 6.5970 0.6135 7.6816 0.6406 4.5129 0.6193
S12 0.6916 2.7294 0.6948 2.7830 0.6733 3.7798 0.6717
S13 0.7859 3.4267 0.7793 5.7493 0.8007 6.3488 0.7760
S14 0.7888 1.6493 0.7934 2.2690 0.7937 2.5422 0.7980
S16 0.6503 5.9354 0.6681 7.9410 0.6594 5.2486 0.6584
S17 0.8198 4.5170 0.7939 4.7344 0.7965 5.8205 0.7986
S18 0.6997 3.8781 0.7546 4.6024 0.7202 4.7593 0.7239
S20 0.6829 4.1724 0.6721 4.3053 0.6837 5.9014 0.6707
S21 0.6198 4.5480 0.6905 4.8047 0.6494 5.2452 0.6497
S22 0.6372 3.3430 0.6629 4.0498 0.6772 5.7725 0.6739
S23 0.6985 2.9178 0.6939 3.1362 0.6936 4.3290 0.6796
S24 0.7339 2.3747 0.7173 2.8420 0.7468 4.5555 0.7275
S26 0.5867 4.2618 0.5666 4.9736 0.5705 6.8117 0.5682
Table 8: Mean square error between reference denoising method and CLEEGN (MSE), AUC score
of CLEEGN (AUC) in ”BCI-Challenge” ERN dataset trained by different data length - Part2
Method ICLabel CLEEGN IC-U-Net 1D-ResCNN
S02 - 0.8238 3.5139 0.8274 4.4294 0.7816 5.7621 0.7598
S06 - 0.6616 2.3353 0.6926 4.1464 0.6132 5.4486 0.5601
S07 - 0.8263 4.2118 0.7410 4.3550 0.7003 6.5909 0.6848
S11 - 0.6083 7.6580 0.6324 5.0279 0.6153 5.1880 0.5729
S12 - 0.6947 2.3652 0.6756 3.8519 0.6587 7.0038 0.6283
S13 - 0.7913 3.5441 0.7835 8.1448 0.7469 8.1590 0.7509
S14 - 0.8128 1.6376 0.8135 2.9057 0.7674 3.7053 0.7679
S16 - 0.6402 5.2618 0.6615 4.0351 0.6405 6.1420 0.6485
S17 - 0.8177 4.4521 0.8153 8.2195 0.7612 7.8305 0.7564
S18 - 0.7419 3.7442 0.7667 4.7798 0.7082 6.4393 0.6729
S20 - 0.6962 3.4116 0.6770 5.7497 0.6802 8.9663 0.6359
S21 - 0.7130 3.7251 0.7742 5.1049 0.7132 7.4864 0.6718
S22 - 0.7166 3.0667 0.7435 6.1630 0.7199 10.6583 0.6749
S23 - 0.7016 2.3144 0.7316 4.3579 0.6848 3.6919 0.6524
S24 - 0.7366 2.0520 0.7228 3.7706 0.7186 4.7246 0.7245
S26 - 0.5663 4.0011 0.5445 8.2955 0.6077 9.6387 0.5539
Table 9: ERN mean square error and AUC score of each subject on different network structure
Method ICLabel CLEEGN IC-U-Net 1D-ResCNN
Subject MSE Top-1 Acc MSE Top-1 Acc MSE Top-1 Acc MSE Top-1 Acc
S001 - 0.6930 0.0859 0.6620 0.0852 0.4570 0.1228 0.2335
S002 - 0.7350 0.1379 0.8000 0.0609 0.3235 0.0608 0.2315
S003 - 0.3175 0.1113 0.3645 0.1013 0.2830 0.1798 0.2325
S004 - 0.2610 0.1040 0.2770 0.0984 0.2380 0.1896 0.2330
S005 - 0.2870 0.0387 0.2985 0.0475 0.2685 0.0539 0.2310
S006 - 0.5300 0.0237 0.5595 0.0322 0.3910 0.0583 0.2290
S007 - 0.5110 0.0819 0.4935 0.0768 0.4035 0.1503 0.2340
S008 - 0.2020 0.1143 0.2270 0.1169 0.2015 0.1205 0.2345
S009 - 0.9050 0.0492 0.8005 0.0460 0.4070 0.3755 0.4070
S010 - 0.5560 0.0405 0.5055 0.0379 0.3690 0.3125 0.5260
S011 - 0.4360 0.0668 0.6870 0.0651 0.4225 0.6281 0.4075
Table 10: SSVEP mean square error and top-1 accuracy of each subject on different network struc-
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Artifact removal is important for EEG signal processing because artifacts adversely affect analysis results. To preserve normal EEG signal, several methods based on independent component analysis (ICA) have been studied and artifacts are removed by discarding independent components (ICs) classified as artifacts. In this study, a method using Bayesian deep learning and attention module is presented to improve performance of the classifier for ICs. Probability value is computed through the method to predict if a component is artifact and to treat ambiguous inputs. The attention module achieves increasing classification accuracy and shows the map of the region where the classifier concentrates on.
Electroencephalography (EEG) signals are an important tool in the field of clinical medicine, brain research and the study of neurological diseases. EEG is very susceptible to a variety of physiological signals, which brings great difficulties to the research and analysis of EEG signals. Therefore, removing noise from EEG signals is a key prerequisite for analyzing EEG signals. In this paper, a one-dimensional residual Convolutional Neural Networks (1D-ResCNN) model for raw waveform-based EEG denoising is proposed to solve the above problem. An end-to-end (i.e. waveform in and waveform out) manner is used to map a noisy EEG signal to a clean EEG signal. In the training stage, an objective function is often adopted to optimize the model parameters and in the test stage, the trained 1D-ResCNN model is used as a filter to automatically remove noise from the contaminated EEG signal. The proposed model is evaluated on the EEG signal from the CHB-MIT Scalp EEG Database, and the added noise signals are obtained from the database. We compared the proposed model with the independent of the composite analysis (ICA), the fast independent composite analysis (FICA),Recursive least squares(RLS) filter,Wavelet Transform (WT) and Deep neural network(DNN) models. Experimental Results show that the proposed model can yield cleaner waveforms and achieve significant improvement in SNR and RMSE.Meanwhile, the proposed model can also preserve the nonlinear characteristics of EEG signals.
Objective: Artifact subspace reconstruction (ASR) is an automatic, online-capable, component-based method that can effectively remove transient or large-amplitude artifacts contaminating electroencephalographic (EEG) data. However, the effectiveness of ASR and the optimal choice of its parameter have not been systematically evaluated and reported, especially on actual EEG data. Methods: This study systematically evaluates ASR on twenty EEG recordings taken during simulated driving experiments. Independent component analysis (ICA) and an independent component classifier are applied to separate artifacts from brain signals to quantitatively assess the effectiveness of the ASR. Results: ASR removes more eye and muscle components than brain components. Moreover, even though some eye and muscle components retain after ASR cleaning, the power of their activities are reduced. Study results also showed that ASR cleaning improved the quality of a subsequent ICA decomposition. Conclusions: Empirical results show that the optimal ASR parameter is between 20 and 30, balancing between removing non-brain signals and retaining brain activities. Significance: With an appropriate choice of parameter, ASR can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain-computer interfaces.
The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low-cost measure of mesoscale brain dynamics with high temporal resolution. Although signals recorded in parallel by multiple, near-adjacent EEG scalp electrode channels are highly-correlated and combine signals from many different sources, biological and non-biological, independent component analysis (ICA) has been shown to isolate the various source generator processes underlying those recordings. Independent components (IC) found by ICA decomposition can be manually inspected, selected, and interpreted, but doing so requires both time and practice as ICs have no order or intrinsic interpretations and therefore require further study of their properties. Alternatively, sufficiently-accurate automated IC classifiers can be used to classify ICs into broad source categories, speeding the analysis of EEG studies with many subjects and enabling the use of ICA decomposition in near-real-time applications. While many such classifiers have been proposed recently, this work presents the ICLabel project comprised of (1) the ICLabel dataset containing spatiotemporal measures for over 200,000 ICs from more than 6000 EEG recordings and matching component labels for over 6000 of those ICs, all using common average reference, (2) the ICLabel website for collecting crowdsourced IC labels and educating EEG researchers and practitioners about IC interpretation, and (3) the automated ICLabel classifier, freely available for MATLAB. The ICLabel classifier improves upon existing methods in two ways: by improving the accuracy of the computed label estimates and by enhancing its computational efficiency. The classifier outperforms or performs comparably to the previous best publicly available automated IC component classification method for all measured IC categories while computing those labels ten times faster than that classifier as shown by a systematic comparison against other publicly available EEG IC classifiers.
Electroencephalography (EEG) has been instrumental in making discoveries about cognition, brain function, and dysfunction. However, where do EEG signals come from and what do they mean? The purpose of this paper is to argue that we know shockingly little about the answer to this question, to highlight what we do know, how important the answers are, and how modern neuroscience technologies that allow us to measure and manipulate neural circuits with high spatiotemporal accuracy might finally bring us some answers. Neural oscillations are perhaps the best feature of EEG to use as anchors because oscillations are observed and are studied at multiple spatiotemporal scales of the brain, in multiple species, and are widely implicated in cognition and in neural computations.