Paper: Decompose Time and Frequency Dependencies: Multivariate Time Series Physiological Signal Emotion Recognition (ICLR-2024)

Reviewer comment (Novelty):
The originality of the model's architecture is somewhat limited. Despite its focus on temporal considerations, it relies on a frequency domain model, and its fundamental structure closely resembles that of FED-Former.

Author response:
Thank you for point this out. We agree with your point that our model is smiliar with FEDformer in overall model architecture. The results of ablation study (please refer to Table1 in gerneral reponse) have proved the modification we made in our proposed model highly improve the model performance. We will revise our result section with the result of ablation study to emphasize the effectiveness of the module we proposed in our final version.

Reviewer comment (Experiments):
The model's real-world applicability raises questions. While it exhibits strong performance in just four tasks from a single competition, it lacks comprehensive comparisons with other existing models, making its effectiveness less persuasive. To bolster your argument, I recommend introducing additional datasets for comparative analysis, thereby reinforcing the model's practical utility.

Author response:
Thank you for your suggestion. Due to most of the physiological signal emotion recognition dataset and benchmark are foucsing on the classification task, there are limited existing works that we can make the comparison. To make our results more persuasive, we add another transformer based solution which is also working on EPiC challenge dataset for comparison. We will also add our results on DECAF (https://decaf-dataset.github.io/DECAF/Description.html), which is also a physiological signal emotion recognition dataset with continous label, in our final version.

Reviewer comment (Reproducibility):
In the EXPERIMENT SETTINGS section, there is a need to provide specific details about your experiment's hyperparameters to facilitate model reproducibility for readers.

Author response:
Thank you for point this out. We will make our codes open soure after the paper get accept.

Reviewer comment (Experiments):
The paper would benefit from conducting experiments on a broader spectrum of datasets to evaluate the model's performance and establish its feasibility.

Author response:
Thank you for your suggestion. Due to most of the physiological signal emotion recognition dataset and benchmark are foucsing on the classification task, there are limited existing works that we can make the comparison. To make our results more persuasive, we add another transformer based solution which is also working on EPiC challenge dataset for comparison. We will also add our results on DECAF (https://decaf-dataset.github.io/DECAF/Description.html), which is also a physiological signal emotion recognition dataset with continous label, in our final version.

Reviewer comment (Experiments):
Comparative analyses with other existing models should be included wherever applicable.

Author response:
Thank you for your suggestion. Due to most of the physiological signal emotion recognition dataset and benchmark are foucsing on the classification task, there are limited existing works that we can make the comparison. To make our results more persuasive, we add another transformer based solution which is also working on EPiC challenge dataset for comparison. We will also add our results on DECAF (https://decaf-dataset.github.io/DECAF/Description.html), which is also a physiological signal emotion recognition dataset with continous label, in our final version.

Reviewer comment (Presentation):
The paper could be enhanced by incorporating more visual aids to offer a more lucid representation of your concepts.

Author response:
Thank you for your suggestion. We add prediction visualization in the appendix (in our current uploaded revised version) to better demostrate the rationale behind the module we proposed in this paper.

Reviewer comment (Novelty):
I am having trouble seeing the contributions of this paper. It seems like the authors have used an existing model and fitted it on a new dataset. The authors have not tested whether the model is generalizable to other affective datasets. If authors could point out some key aspects of the paper's contributions a bit more clearly it would be greatly appreciated.

Author response:
1. As Wavelet and Fourier transform are widely applied on physiological signal emotion recognition task for preprocessing. We want to investigate whether Wavelet and Fourier attentions have adavantages on physiological signal emotion recognition task. Our experiment resulst proves the Wavelet and Fourier attentions do have adavantages on predicting seasonal of target signal but has some limitation of predicition the trend of target signal. We address this problem via utilizing the time domain attention, such as Autocorrelation attention. 
2. We redesigned the vanilla Frequency domain attention and Time domain attention by applying Two-Stage Attention (TSA) on them. Therefore, the refering frequency domain attention and time domain attention are not only calculating on the time-step wise but also on channel wise. Our experiments proves the effectiveness of the proposed attention.
3.  The tranditional time series prediction task is working domain consistent data, where the input and target data are in the same domain. However, for physiological signal emotion recognition task, the input and target signals has different domain and sample rate, which turns physiological signal emotion recognition task to be relatively more challenging. In this paper, we successfully apply the SOTA methods from multivariate time series prediction task to physiological signal emotion recognition task. Moreover, the proposed method achieves SOTA result in physiological signal emotion recognition task. We believe our solution and results could inspire the future research in the field.

Reviewer comment (Evaluation):
The interpretability of the results seem a bit lacking. Why does the author's proposed method outperform other participant's methods? Additionally, table 1 has a row labeled "Scenario level." Could the authors clarify what this means?

Author response:
The major resons are:

1. The refered technique individually has been proved their effectiveness in time series prediction tasks and achieves SOTA results when published.
2. We redesign the refered methods to make them can be applied to emotion recognition task.
3. The wavelet and fourier transform technique utilized in frequency domain attention has been widely applied in physiological signal emotion recognition task and proves their effectiveness.

Reviewer comment (Novelty):
This paper was submitted to the "Primary Area: applications to neuroscience & cognitive science." I am having a bit of trouble seeing this paper's contribution to this area. Could the authors clarify this?

Author response:
1. We explore and examine the SOTA methods from time series prediction task on physiological signal emotion recognition task.
2. We redesign the refering methods and propose a new solution for physiological signal emotion recognition task. 
3. The proposed solution achieves SOTA reuslt in physiological signal emotion recognition task.
4. We believe our results and solution could inspire the future reseach in the field.

Reviewer comment (Experiments):
There should be more ablation studies to prove each component in the proposed method works.

Author response:
Thank you for point this out. We add the result of ablation study in the general response. We will revise our result section in our final version.

Reviewer comment (Writing):
The related work section introduces some references and attention mechanisms that the authors used, without the information about the following compared works.

Author response:
The refered model and attention mechanism are mostly designed for time series prediction, where the input and target data are in the same domain. For physiological signal emotion recognition, the input and target signal are in different domain and have different sample rate. The models proposed for time series prediction couldn't be directly applied on physiological signal emotion recognition. One contribution of our paper is that we proposed a solution for applying the SOTA time series prediction model on physiological signal emotion recognition, which we believe could inspire future research in the field.

Reviewer comment (Writing):
Many simple formula notations are cumbersome, for example, we have long been familiar with the attention mechanisms.

Author response:
Thank you for point this out. We will revise our methodology section and fix the notation problems in our final version.