Brain Signal Generation and Data Augmentation with a Single-Step Diffusion Probabilistic Model
Authors: Szabolcs Torma
Dr. Luca Szegletes
Venue: Submitted to ICLR 2023 (2023)
Keywords: diffusion probabilistic model
generative model
electroencephalograpghy
eeg
erp
motor imagery
synthesis
augmentation

Abstract: Brain-computer interfaces based on deep learning rely on large amounts of high-quality data. Finding publicly available brain signal datasets that meet all requirements is a challenge. However, brain signals synthesized with generative models may provide a solution to this problem. Our work builds on diffusion probabilistic models (DPMs) and aims to generate brain signals that have the properties needed to develop further classification models based on deep learning. We show that our DPM can generate high-quality event-related potentials (ERPs) and motor imagery (MI) signals. Furthermore, with the progressive distillation of the model, subject-specific data can be produced in a one-step reverse process. We augment publicly available datasets and demonstrate the impact of the generated signals on a deep learning classification model. DPMs are versatile models, and this work shows that brain signal processing is one of many other tasks in which these models can be useful.

Under review as a conference paper at ICLR 2023
BRAIN SIGNAL GENERATION AND DATA AUGMENTA- TION WITH A SINGLE-STEP DIFFUSION PROBABILIS- TIC MODEL
Anonymous authors Paper under double-bling review
ABSTRACT
Brain-computer interfaces based on deep learning rely on large amounts of high- quality data. Finding publicly available brain signal datasets that meet all require- ments is challenge. However, brain signals synthesized with generative models may provide solution to this problem. Our work builds on diffusion probabilistic models (DPMs) and aims generate brain signals that have the properties needed to develop further classification models based on deep learning. We show that our DPM can generate high-quality event-related potentials (ERPs) and motor im- agery (MI) signals. Furthermore, with the progressive distillation of the model, subject-specific data be produced in one-step reverse process. We augment publicly available datasets and demonstrate the impact of the generated signals on deep learning classification model. DPMs are versatile models, and this work shows that brain signal processing is one of many other tasks in which these mod- els can be useful.
INTRODUCTION
Electrrenncephalograpyy (EEG) undoubtedly one of the most popular brain mapping technologies, which is widely used in research and clinical diagnosis (de Aguiar Neto & Rosa (2019), van Mierlo al. (2020), Wang et al.] (2020). ). EEG records the neural activity of the brain in non-invasive manner. (Biasiucci et al. (2019)) EEG is less complex and cheaper than other brain imaging tech- nologies. EEG has one the best temporal resolutions. However, the spatial resolution of the technology is quite poor due to its heavy dependence on the number of electrodes used for signal recording and non- non-invasiveness. (Craik et al. (2019b))
Brain-computer interfaces (BCIs) connect the brain and external processing devices, making it pos- sible to perform tasks using only brain signals. BCIs can help in everyday life for people with limited movement and communication abilities (Pandarinath et al. (2017)). BCIs are also applied in many other fields from healthcare (Galán et al.] (2008), Vilela & Hochberg (2020)) to entertainment (Finke et al. (2009)). BCIs are often based EEG due the ability of the technology signals with only couple of milliseconds difference and its relatively low cost and more comfort. The measurements are then processed by a decoder unit in the BCI that turns the recorded temporal and frequency patterns into actions. (Lotte et al. (2018)
measure
In recent years, deep learning (DL) algorithms have become more and more commonly used in EEG signal processing (Roy et al. (2119) Craik et al. (019a), Kotowski et al. (2020)). DL models can decode brain signals with high accuracy. However, developing DL models requires a large amount of high-quality data. The size and quality of publicly available data sets are limited, also often insufficient and imbalanced. Recording a new data set can be highly resource-consuning and requires professionals to check the measurements. Another option to augment data sets is data synthesis. (Lashgari et al. (2020))
Score-based models (Tashiro et al. 2021). Song et al. (2021)) diffusion probabilistic models (DPMs) (Ho et al. (2020). Luo & Hu (2021) ) and generative adversarial networks (GANs) (Liu et al. 20221), Chan al. (202)) hold the state-of-the-an in beep-learning-barg
generative mod- elling. The recent advances show the performance and effectiveness DPMs over GANs in both
image (Dhariwal & Nichol (202T) and audio generation (Kong et al. (202T). There handful

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of works for brain signal generation with GANs (Xu al. (2022), Hartmann et al. (0118) Fahimi jet al. (2019), Panwar et al. (2020)). To the best of our knowledge. there are no works examining the capabilities of DPMs or score score-based models in multi-channel EEG signal generation tasks.
The structure of this work is as follows: in Section 2 we present the background on the DPM framework that used in this paper, followed by brief description of the progressive distillation process in Section 3 Our EEGWave architecture presented in Section 4 The description of the experiments with the used datasets and procedures given in Section Finally, we conclude our
work and thoughts in Section 6
2 CONTINUOUS-TIE TIME DIFFUSION MODELS
The distribution of the training data set is given as p(x). Let x E REXL where is the number of electrodes (or EEG channels) and L is the length of the recorded sequence. In continuous-time diffusion framework (Kingma et al. (202I). in the forward and reverse diffusion processes, there are latent variables that are denoted by 2.. For every time step, where E [0. 1]. the latent variables have the same shape as the training data samples (c+ E IR Ex)).
The forward diffusion process, which Gaussian process in continuous time can be given as:
< ((2 0,2, I
.weee 0r and of are smooth, differentiable, positive scalar- valued functions. With ag and of the log signal-to-noise ratio given as: A, log (a//o?) which decreases strictly monotonically as t + 1. For any S 8 S 1 < 1, the following Gaussian conditional distribution can be given:
(<<lZz)) = N( an 2. I
न.. = (1 (1-el -1--AA--A A
The reverse process is based on the posterioni distribution:
(3)
where S t and
()) = eAc cxc- 2i + (1 e e^1-A=Jaa,, Jaya, 2 =0 -1 -
(4)
this framework, x in the reverse process is predicted by the neural network (o 2²) with parameter set 0.
Data inference is done by sampling a latent white noise variable 21 at t = 1, setting a noise control- ling y factor and iteratively applying the following, until 0 (Salimans & Ho (022
2a (2)e(2s, Ac)) + (
N(0,1
(5)
During training, the model is aimed to maximize the variational lower bound (ELBO) on the log- likelihood of the data. However, with re- paraammtterrzttion, the weighted ELBO can be given as the weighted mean squared error objective:
min L(O) = E. [o(A)))) - 5a(z, Xo11 (
(6)
where w/ (Xx) weighting is choosable, however it w(te) max S 1) in our approach.
Salimans
& Ho 2022
PROGRESSIVE DISTILLATION
Diffusion models need many iterations during sampling to synthesize data, making them signifi- cantly slower than GANs. Recent studies (Luhman & Luhman (2021), Kong & Ping (2021) pre- sented multiple ways to fasten the inference from which we applied progressive distillation (aal- found this approach the most efficient one.
mans & Hol (2022))
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Progressive distillation is based on teacher-student setup, where the student model approximates the teacher with halved sampling steps. At first, a teacher model is trained as a continuous- time diffusion model. Then a number of finite discrete sampling steps is given for the teacher and the student, denote it as 7, and T. = T2/2. The distillation process is iterative, where each iteration starts with weight initialization of the student model with the teacher's parameters. The target & is then calculated from the latent variable 2; by sampling in 2 DDIM steps using the teacher model to get the predictions at time step (t 0.5)/TI, and the (t - I.)/T. The student has denoise the same 2n in reverse DDIM step to get the approximate result for ž. When the student converges, T, halved, the student becomes the teacher, and the process repeated.
Fully Connected
Connected
Fully Connected
Ysubject
Swish
Vdass
tencoded
diffusion step
Conu20 (cb,
Xyes
residual block, where the size of the dilation is 2"
Swish
Residual blacks
Xinput
Xskip
geraas Oenuos Sonag fr ( Connaen
IE Tl
Add and Scale
Xoutput
Figure 1: EEGWave architecture.
4
ARCHITECTURE
Our architecture aims to synthesize multi-channel EEG signals as many works on brain signal pro- cessing show the benefits of processing signals recorded on multiple electrodes. Therefore, our model is Fp REX R Ex that builds non-causal bi-directional dilated convolutions.
Many works on multi-channel brain signal processing handle the measurements as 2-dimensional samples, similarly to images, to make use of spatio-temporal features. In GANs and VAEs, this approach means that EEG signals are synthesized through up- up-samplnng interpolations or transposed convolutions. Data synthesis in this approach with time-series signals be ill-posed due to the heavy influence of these operations' temporal and spectral artifacts that occur in the generated data.
Our approach builds on the work of Kong al. (2021). We omit up- and down-sampling layers to avoid temporal and spectral artifacts in the synthesized data. We build on the residual layers of DiffWave. Although EEG epochs convolutions with smaller dilations as they can help maintain global context and consistency through the epochs. We realize the architecture with 2-dimensional convolutions. Our input convolution layer maps the multi-channel data into a single-channel by applying (E, 1) sized filters, where E is the number of EEG channels. We use kernels with size (1, K) in the dilated convolution layers as we process I-dimensional data in these blocks. The output wisn convolution layer produces the 1-dimensional signals with the number EBG channels (N E x 1 x L). Then this data reshaped to match the dimensions of the input (N x L). (We implemented the architecture in Py Torch.)
long as audio samples, keep bi-directional dilated
A is embedded through two global and one residual-local linear layers and added to the residual features. The class and subject conditions are handled the same way, the only difference being that they not encoded in any way. The architecture is shown in Figure
In the experiments given in this work, we use a rather deep than wide neural network with 32 residual layers and 64 channels in each convolution layer. X one-hot encoded class, and subject conditions are embedded into 512 dimensions in the global linear layers. We use kernel size of (1,3) in the dilated convolutions and dilation cycle of [1, 2,.,
........ 64] because empirically, we found it to be
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beneficial to dilation eycle that with there are kernels that cover the whole data sample. X is embedded through two global and one residual-loca linear layers and added to the residual features. The class and subject conditions are handled the same way, the only difference being that they are not encoded in any way.
5 EXPERIMENTS
5.1 DATASETS
VEPESS dataset: the set contains visually evoked potentials from 18 subjects recorded by the authors of Robbins et al. (2018). The measurements were done by following the oddball paradigm. Subjects were presented with sequences of images that consisted of target and non-target types. Based on the type of the shown image, the subjects had to push the corresponding buttons. Data were recorded with an EEG device with 64 electrodes in the 10-20 standard configuration and sampled at 512 Hz. In this work, we use raw measurements. Signals were band-pass filtered from 1 40 Hz with a zero-piir pero-phase filter, and epochs were extracted between [0, seconds from the onset of the peaect//oo-tareeee image. The epochs were normalized by channel-wis -wiee mean subtraction and deviation division into the range [-1., 1.].
BCT Competition IV Dataset Ha (BCIC4D2a): the set contains motor imagery signals from participants. The subjects were asked to imagine the movement of their left and right hand, also their feet and tongue, for couple of seconds after the instruction cue were presented on their screen. Data was recorded from 22 EEG and 3 EOG channels following the 10-20 standard system. The measurements were sampled at 250 Hz and band-pan -asss filtered from 0.5 100 Hz. Furthermore, notch filter at 50 Hz was applied to eliminate the line noise. We further band pass filtered the signals between 4 38 Hz with zero-phase filter and down-samples them to 128 Hz. Following the work of, we extracted epochs from the recordings between [0.5, 4] seconds from the onset of the cue and normalized them by channel-wise mean subtraction and deviation division. We excluded samples marked as rejected due to artifacts by the publishers of the set.
5.2 CLASS-CONDITIONAL SIGNAL GENERATION
The current experiment aims to generate EEG data of good quality from samples from a simple Gaussian noise distribution. We condition the generation process based on classes in the data sets. We use the VEPESS and the BCIC4D2a data sets to examine whether the model can capture the features of the sets that contain different types of signals. For both data sets, EEGWave has to model characteristics in both the time and frequency domain, but in slightly different way. The signals from the VEPESS set are characterized mainly by their amplitude deviation and the corre- sponding latency in the time domain. On the other hand, the essential features of the samples from the BCIC4D2a set are the frequency domain, specifically in the theta, alpha, and beta bands. Although the effect of progressive distillation on the quality of the generated data is examined in the following subsection, we also present the results from the distilled models, which generated the signals in single step.
We chose only the RWGAN (Panwar et al. 2020 as a baseline model because other deep learning models in the literature were either incompletely documented, designed only for single-channel EEG generation, or did not work based on the given information in the published work. We trained RWGAN exactly the publishers did in their work.
We give both qualitative and quantitative results. The Inception Score (IS) (Salimans et al. 2016 Frechet Inception Distance (FID) (Heusel et al. (2017)) spatial FID (Nash al. (2021) (). Precision, Recall (Kynkäänniemi et al. 2019)) scores are computed based on EEGNet (Lawhern et al. (2018). which was trained on the data sets separately the same way as the authors of BEGNet did. We also use Sliced Wasserstein Distance (SWD) (Wu et al. (2019) and Gaussian Mixture Model (GMM) differences (Panwar al. (2020)) measure the differences between real and generated distribu- tions directly. We also found that qualitative results are a good way to have a greater understanding and a deeper interpretation of results.
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Table I: The quality of the generated EEG signals the VEPESS measured indirectly (with the feature maps of EEGNet) and directly (through distribution measures).
Origin
IS 1
FID +
sFID +.
Prec t
Rec
SWD
dGMM +
Train set
1.2522
0
1.0
1.0
0
Test set
1.2360
0.0315
1.3264
0.9199
0.9369
0.9851
49.8316
RWGAN
1.1761
2.2708
3.0530
0.8865
0.9087
1.1769
119.0032
EEGWave
1.2284
0.1479
1.5266
0.9238
0.8740
1.0915
81.1503
EEGWave 1x
1.2146
1.4212
5.6885
0.8063
0.8284
1.7785
141.4728
FARGAN SENEIINGG #OOCTS BAAERII
0-120
0.200
5.3325
0.000
0.600
0.680
0.100
0.330
ESGANVE
0.120
0.00s
0.590
0-400:
0.600
0,800
0.100
0.200
0.390
0-400
0.600
0.580
1.0
02
0.5 03
00
0.
0.5
Figure 2: Comparison of averaged real and generated samples by different models on the VEPESS set. The averaging was done across the epochs, and the results are plotted all 64 channels.
VEPESS results: first, we present the results on the VEPESS set. The quantitative metrics given in Table We also give scores measured on the test understand the generated signals' sults better. Figure presents the averaged target class samples from the original and the generated sets. Based on the given results, it can be said that
Theta 4.8 Hz
Apha B HZ
Beta 12:38 HE
EEGWave captured the ERPs' main features in the VEPESS set. RWGAN also generated good signals, although these samples are heavily contaminated by frequency artifacts, which, in our hypothesis, are
JAAME
mostly the results of the up-sampling layers (also mentioned frequently in the literature). The single- step EEGWave has slightly poorer scores in the ta- ble, but the generated signals seem to have more fi- delity than the ones from the RWGAN.
RINAH
BCIC4D2a results: for the BCIC4D2a set, the quantitative results are given in Table 2 Figure presents the power spectral densities distributed over the scalp in the theta, alpha, and beta frequency bands for the left-hand class. Generated signals from the EEGWaae 1024 step, FEGWave 1 step, and RW-
Figure 3: Real and generated signal PSD top plots from the BCIC4D2a set.
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Table 2:
The quality of the generated EEG signals the BCIC4D2a set measured indirectly
(with the feature maps of EEGNet) and directly (through distribution measures).
Origin
IS 1
FID +
sFID +
Prec
Rec f
SWD 4
dGMM +
Train set Test set
1.3998
0
1.0
1.0
0
1.3594
0.1798
80.4392
0.9040
0.9172
0.4781
2,4963
RWGAN
1.2949
7.2315
142.8659
0.4740
0.4123
1.6983
751.2371
EEGWave
1.1881
3.1708
51.9159
0.9668
0.5724
1.3566
597.6406
EEGWave 1x
1.2136
3.3100
42.8244
0.9282
0.5245
1.0874
430.7368
GAN models are compared to the real signals from the dataset. The results show that the single-step EEGWave model could perform slightly better than RWGAN in this experiment. The top plots show that all models were able to learn the main frequency features. The two HEGWave models outperformed the GAN. The figures corresponding the rest of the classes given in Appendix A.
5.3 SUBJECT SPECIFICITY
Brain signals vary not just between classes but subjects. In many cases, it could be beneficial to generate data for only specific subjects, e.g., imbalanced sets, fine-tuning. We use the VEPESS set to show that subject-specific features can be learned and reproduced by model. Subject information is one-hot encoded and injected into the network similarly to the signal class labels. The model is trained the same way as in the class-conditional case. During inference, the signal generation process is conditioned on the class labels and the subjects. We then compare the generated signals from each subject the real signals from all subjects to examine whether the model could learn each subject's features. The generated signals are visualized in top plots. Furthermore, we measure the SWD and sFID metrics between the subject-specific distributions. For the sFID calculation, the same EEGNet model is used as in the previous experiment.
the metrics in Figure 4 jmply that the distributions of the real and generated signal corre- sponding the same subjects closer each other than other subject data. Figure 5 visually supports this implication. The averaged ERP epochs of each subject are visually easily distinguish- able from each other. Although we only present few examples here, we include the rest of the top plots in Appendix A.2
Results:
Spatial Frechet inception Distances
Sliced Wasserstein Distances
22
2.0
5
40
6
30
Weel
1.2
1.0
15
0.8
10
1011 12 14
16
10 14 1718
Figure 4: Two types of distance metrics were measured between the distributions the real and generated subjects from the VEPESS set to examine the ability of the model to learn subject-specific features. (The lower, the better.)

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VEROSO pet Subrcc trrpetspect enrraed
EDCIMII, Subeet LIsE Sirget spects eesraged
VErESS Senject epochs swarged
EEOllww Sunject opoch overged
0.330
0.400
6.120
0200
0.330
0.400
0.60
Figure 5: Comparison of the averaged real and generated ERP epochs from the VEPESS set for Subjects 1 and 15.
5.4 AUGMENTATION
In the augmentation task, we try to improve the performance of EEGNet on the BCI4D2a dataset. A generated dataset is created with the single-step EEGWave model with the same amount of sig- nals as the original set. The original dataset split into train, validation, and test subsets with 0.7%, 0.15%, 0.15% ratios. The subsets created contain the same number of signals from each subject. We try to improve the accuracy the EEGNet model with two approaches:
1. we double the size of the training subset by mixing the same amount of generated signals the number in the original subset (6)
2. pre-train EEGNet on the generated data and then re-train this initialized model with the real signals
In both cases, only stop the training if fitt-iit is detected via monitoring the validation loss.
We use Adam optimizer with learning rate le
Confusion Matrix
Confusion Matrix
60
11
-50
31
38
3
0.47
24
45
12
0.15
14
10
0.62
Thw
57
25
0.58
-30
23
41
0.1
-30
0.47
Prosson
236
0.02
0.54
0.54
recal
Prodictd lood
Prodictdd leel
Figure 6: The effect of training EEGNet with the real-generated mixed training subset. The left confusion matrix shows the test results without augmentation, while the right one shows the training results with the mixed-set augmentation.

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Confusion Matrix
Confusion Mabrix
12
>0
11
0.67
-60
61
10
33
0.64
30
-3
31
47
38
0.47
60
25
0.61
18
0.6
39
-20
Reesion
-20
Recip
Figure 7: The effect of pre-training EEGNet with the generated signals. The left confusion matrix shows the test results without pre-training, while the right one shows the results with pre-training.
Results: although the mixed training gave slightly better accuracy the validation subset during training, it did not improve the accuracy of the test set. On the contrary, with the pre-training approach, the model converged faster and achieved better accuracy on the These observations imply that although EEGWave was able to learn the main characteristics of the real signals, it could not produce signals that are diverse enough to regularize EEGNet.
5.5 DISTILLATION
By progressive distillation, aim to attain DPM model that synthesize EEG signals in single step. We distill EEGWave on the VEPESS and BCIC4D2a data sets to examine the effect of the process. The distillation is started 1024 steps, and continue it until the single-step generation is reached. The number of training iterations in steps 2 and I is doubled, following the work of The distilled models with different inference steps are saved and compared to the initial model generating signals through the same number of steps as the distilled ones. We evaluate the generated signals these steps and present results in Figures and
10-
50-
27
27
31
20
Figure 8: Effect of the distillation of EEGWave trained on the VEPESS data set. F-Score caleu- lated from the Precision and Recall scores with 3 = 2.
while at higher number of steps (> 8), distillation did not result in a better-performing model. At steps < 8, the metries show much better models than the ones without distillation. In the case of the VEPESS set, the scores achieved with the single-step distilled model are much closer to the scores of the initial model than without distillation. This shows that distillation option for achieving a DPM that can effectively balance the trade-off between fast sampling and high-quality samples. Interestingly, in the case of the BCIC4D2a set, the scores of the distilled mod-
Results:
be good
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SWU
802
300
403
202
169
27
9000
St00
Figure 9: Effect of the distillation of EEGWave trained the BCIC4DZa data F-Score is calculated from the Precision and Recall scores with 3 = 2.
els show better performance than those of the initial model. This implies that the distilled models more likely generate EEG signals that are copies of the ones from the train set, resulting in better metrics due to higher fidelity but a narrower learned distribution.
CONCLUSION
Our work shows novel way of generating brain signals that can be useful in augmentation tasks. Although this work aimed to examine as many aspects of brain signal generation as possible, there much yet explore. We believe the current work shows that DPM- based brain signal generation is very feasible task and can be used to create datasets that help improve deep learning models in classification tasks.
This work is mainly limited in that the quality and diversity of EEG signals can not be measured the same way as in the case of images. The metrics commonly used in image synthesis tasks often give contrary results in brain signal generation.
ERP and MI signals were generated conditioned on class labels. The performance our single-step DPM was close to 1024-step DPM and the RWGAN, We also showed that EEGWave could learn subject-specific features. the augmentation task, the generated signals were the most useful for the pre-training of EEGNet, before re-training the original set. The distillation results show that progressive distillation an excellent approach to obtaining a DPM with low number of inference steps that generate signals of good quality. The diversity of the generated signals is still open question, as well as the metrics that can measure the realness of the generated data. We hope that DPM-based signal generation will be much more explored in the future, and we eager the development of this field.
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A APPENDIX
A.1
GENERATED MOTOR IMAGERY SIGNALS
Thets 64
Agha 6-12
Data 12-30 12
Figure 10: PSD top plots of the generated signals from the BCIC4D2a dataset. The top, middle, and bottom figures visualize the right hand, feet, and tongue classes.
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A.2
GENERATED DIBJECT-SPECIFE ERP SIGNALS
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-30
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VERESS Subeet
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0.220
0.200
0.330
0
0.000
0,880
6:100
0,00s
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0.333
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0.332
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VOPESS Suppet
CEGONA Suppect
0.320 5
0.200
0.330
0
0.000
9,880
0,00s
0.3355
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0.0
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0.320 5 0
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0.120
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VEPESS
EEGOONA
0.120
0.200
0.333
0.400:
0.000
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00
17


Reviews:
[{"review_id": "QzxG0d9T-l", "reviewer": "Reviewer_bBMm", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The authors proposed the use of diffusion probabilistic models (DPMs) to generate EEG data in order to increase the size of datasets for training brain-computer interface classifiers. The qualitative and quantitative characteristics of the generated dataset were compared to the RWGAN model. The DPM model turned out to be comparable with RWGAN. The dataset of evoked potentials and the task of separating imaginary movements were taken as initial datasets. The authors note that DPM is able to generate EEG recordings that preserve the individual characteristics of subjects. The possibility of increasing the performance of EEGNet on the BCI4D2a dataset (classification of imaginary movements) was considered by expanding the dataset with generated data and by pretraining the dataset on these data. The second approach showed better results.", "summary_of_the_review": "A paper applying diffusion models to the task of EEG data generation. Although the idea is interesting and the results are potentially promising, as mostly an application, the paper lacks in rigorous evaluation and empirical demonstration.", "strength_and_weaknesses": "# Strengths\n\n-   The authors have shown that diffusion probabilistic models (DPMs) are promising for generating brain data.\n-   The problem to be solved is sufficiently substantiated in the introduction.\n-   Two different datasets with different tasks are considered\n\n# Weaknesses\n\n-   It is not clear how the data was divided into train and test sets in section 5.2\n-   a more detailed analysis of the specificity of subjects is missing (Section 5.3):\n    \n    1.  it would be good to supplement with a comparison\n    \n    of the specificity of subjects with other methods,\n    \n    1.  It is interesting to see the class of problems where such a property of the method is an advantage, and in which it is a limitation. Does this mean that this method cannot generate new subject data?\n-   In section 5.4, the conclusion that pretraining on the generated data gives a gain is not obvious from the form of the matrix. Perhaps a general statistic would help.\n\nDDIM - no decryption of the abbreviation One-hot - typo The number of training iterations in steps 2 and 1 is doubled, following the work of . - link missing", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "Nj8kGm2Mtns", "reviewer": "Reviewer_e92U", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper applies recent generative model, diffusion probabilistic model (DPM), to generate synthetic electroencephalography (EEG) data with utility demonstrated in two BCI applications (event-related potentials and motor imagery) using publicly available BCI datasets.", "summary_of_the_review": "Recommendation is based mainly on the weaknesses outlined above. \n\nOther comments\n-\t“Finding publicly available brain signal datasets that meet all requirements is a challenge.”\n         o\tWhat do authors mean by “all requirements”?\n\n-\t“However, the spatial resolution of the technology is quite poor due to its heavy dependence on the number of electrodes used for signal recording and non-invasiveness.” \n         o\tAuthors should expand on relevance of this to the proposed work. \n\n-\t“The size and quality of publicly available data sets are limited, also often insufficient and imbalanced.”\n         o\tThere are several publicly available BCI datasets. Authors should provide evidence (references, characteristics of publicly available datasets, etc.) to support this claim.\n         o\tThe traditional approach to training machine learning models for BCI use is to rely on user-specific data. How are publicly available datasets relevant/useful within this training paradigm?\n\n-\t“We chose only the RWGAN (Panwar et al. (2020)) as a baseline model because other deep learning models in the literature were either incompletely documented, designed only for single-channel EEG generation, or did not work based on the given information in the published work.”\n          o\tAuthors should reference the other models that were considered. \n          o    What do authors mean by “did not work”? Needs to be more specific.\n\n-\tDiederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho. On density estimation with diffusion models.\n         o\tUpdate title of paper in reference\n\n-\tMore informative caption for Figure 1.\n\n-\tWhat are EEGWave, EEGWave 1x, EEGWave 1024 step, EEGWave 1 step? Figure 1 caption needs to be more description/informative.\n\n-\t“We give both qualitative and quantitative results”\n         o\tDistinguish between the qualitative and quantitative results listed, and include a brief description of each measure.\n\n- The value of the subject-specific result section (section 5.3), especially to the classification task is not clear. One limitation is that the model is restricted to the subjects in the training dataset (subject labels are one-hot encoded), so needs to be retrained each time a new subject is added. Based on section 5.4, data from several subjects are combined to create the training/validation sets. \n\n-\tAre results presented in the figures/tables from one subject? Mean aggregate over all subjects? This needs to be specified in the captions. Preferably, subject-specific results are desirable, especially with relatively few subjects (18 and 9). Waveforms can be illustrated with data from a few subjects. If presented in aggregate, what are the number of subjects and the standard deviation for the measures presented in the table? What does bold mean in each table?\n\n-\tWhat do labels 0, 1, 2, 3 in Figures 6 and 7 mean. Label y-axes in Figures 8 and 9. What is \"Base\" in figures 8 and 9?\n\n-\t“Although the mixed training gave slightly better accuracy on the validation subset during training, it did not improve the accuracy of the test set. On the contrary, with the pre-training approach, the model converged faster and achieved better accuracy on the test.”\n      o    Reference the relevant figures in the text. Are these aggregate results?\n\n-\t“The number of training iterations in steps 2 and 1 is doubled, following the work of _.”\n\n-\t“We believe the current work shows that DPM-based brain signal generation is a very feasible task and can be used to create datasets that help improve deep learning models in classification tasks.”\n\n-   “The metrics commonly used in image synthesis tasks often give contrary results in brain signal generation.”\n       o  Can authors explain what this means, especially \"contrary results\"? How is this relevant in interpreting results. What measures do the authors propose that are more relevant to brain signal generation?\n", "strength_and_weaknesses": " \nStrengths: \n\n- Authors present qualitative and quantitative results on the generated EEG signals, generating user-specific data, the impact of data augmentation on the downstream task (EEG signal classification) and progressive distillation to improve inference time. \n\n- Results presented from two BCI applications (event related potential, motor imagery).\n\nWeaknesses: \n\n-    The results that are most relevant to data augmentation (section 5.4) do not really support the authors claims. \"These observations\nimply that although EEGWave was able to learn the main characteristics of the real signals, it could not produce signals that are diverse enough to regularize EEGNet.\" \n\n- Figure and table captions need to be more informative to better interpret presented results. Are they subject-specific or presented in aggregate? Within-subject performance is more relevant in BCI applications as trends may sometimes be linked to EEG signal quality (signal-to-noise ratio). If presenting results in aggregate, an analysis comparing within-subject performance across all conditions needs to be included. \n\n-    Since the authors contrasts DPM with RWGAN, classification results (not just assessment of the generated EEG signals) with synthetic data generated with GAN need to be included given that DPMs are slower (inference time also needs to be included for comparison). This is relevant in assessing the trade-off. Signal fidelity may not necessarily be as relevant if it has minimal/no impact on the performance of the classification task (the model only learns the most relevant features to distinguish between classes).\n\n\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "KQD6jWcKQN", "reviewer": "Reviewer_fUSb", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The work shows a way of generating brain signals that can be useful in augmentation tasks. The current work shows that DPM-based brain signal generation is a very feasible task and can be used to create datasets that help improve deep learning models in classification tasks.\n", "summary_of_the_review": "This is a typical application study. The diffusion probabilistic is simply applied to EEG. Some details still need to be clarified by the authors.", "strength_and_weaknesses": "*Strength \n\n-This work explored the application of the diffusion probabilistic model in EEG analysis.\n\n-A large number of experiments were carried out on the manuscript.\n\n*Weaknesses\n\n- Technological innovation is limited. It is just a simple application of diffusion probabilistic model in specific fields.\n\n-Although some evaluation indicators are effective. But I am worried about the value of practical application. As the authors mentioned, the classification performance on EEGNet cannot be improved.\n\n-The details of the model are too simple. For example, what is Swish in the model diagram.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "1FHeTlhIIX", "reviewer": "Reviewer_UL6z", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This work attempts to apply diffusion models to synthesize EEG data as data augmentation. Specifically, the work uses the continuous-time diffusion framework with progressive distillation as their methodology. The synthesized data is validated based on training on the VEPESS dataset and the BCIC4D2a dataset, where a set of model validation tasks are performed including (1) quality evaluation on the generated class-conditioned data, (2) quality evaluation on the generated subject-conditioned data, and (3) how the generated data could help classification performance.", "summary_of_the_review": "I suggest rejection. The paper lacks methodology novelty, and the evaluation procedure lacks benchmarking and quantitative evaluations.", "strength_and_weaknesses": "Strength\n\n1. The paper presented a set of interesting application tasks/experiments on EEG datasets.\n2. Many tasks are considered, including class-conditioned generation, subject-conditioned generation, and classification. \n\nWeaknesses\n1. Major concern 1: the method lacks novelty\n- The presented work is a simple application of existing diffusion model + progressive distillation on EEG datasets. No novel method is presented.\n\n2. Major concern 2: experimental evaluation\n- There exists only one benchmark model throughout the paper. The authors give their reason as “We chose only the RWGAN (Panwar et al. (2020)) as a baseline model because other deep learning models in the literature were either incompletely documented, designed only for single-channel EEG generation, or did not work based on the given information in the published work.” However, I found it hardly a valid reason for not implementing enough benchmarks. (1) If the model isn’t completely documented, one should try to implement it based on customized design and model choice that is fairly selected. (2) One could always try to implement classic models such as naive VAE or GAN based on the presented setting. (3) “The model does not work” also is an unvalidatable claim as it is unclear if it is reviewer’s coding issue or if it is the models’ issue.\n- Due to the lack of benchmarking models, both Table 1 and Table 2 are not really informative and do not tell if the model perform good enough. First of all, for many scores the test-set score is better than the model’s performance, which only give a controversial conclusion because it either indicates that (1) the dataset is distributed in a certain diverse way and the model does not overfit (which indicates in certain case the lower the score the better), (2) the model does not perform well enough to mimic the training set structure. That is why most existing works under this setting would just train many models on the whole dataset and compare different models’ performance.\n- The augmentation experiments is not complete. There are no benchmarking models. One thing that can be done is to use the generated data as augmentation and compare it with traditional augmentation (e.g. noise). Also one could benchmark the generated data from this model to the generated images from other models (the GAN that is implemented beforehand).\n- Overall, there isn’t sufficient quantitative evaluation as well.\n\n3. The writing isn’t clear.\n- There are many grammar errors and wording issues throughout.\n\n4. Related works are not summarized and compared properly.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject