WEEGNET: an wavelet based Convnet for Brain-computer interfaces
Brain-computer interfaces (BCI) are systems that link the brain with machines using brainwaves as a medium of communication using electroencephalography to explore the brain activity which is an affordable solution, noninvasive, easy setup, and portability. However, the neural signals are noisy, non-stationary, and nonlinear where the processing of those signals in a pattern recognition problem needs a complex pipeline of preprocessing, feature extraction, and classification algorithms that need an apriori knowledge to avoid compatibility issues and a deep understanding of the studied signals. Moreover, some techniques need a huge computational power on the CPU and a huge size of RAM. Therefore, several papers proposed to use Deep Learning to get state of the art performance and visualization of the learned features to have more understanding about the neural signals. But, the convolutional neural network (Convnet) are not used properly and the results are often random when we reproduced the works. Hence, we propose a combination of the discrete wavelet transform (DWT) and a Convnet that processes raw EEG data. The DWT will be used to reduce the size of the data without losing useful information. Also, a modified version of EEGNET will be used to extract the features and classification.
The paper applies CNN architectures and wavelet decomposition of some moderate-dimensional EEG signals from a recent EEG-BCI competition with the aim of using representational compactness to increase classification efficiency while retaining computational efficiency. The motivation is good and the position in the (vast) EEG-BCI literature is appropriate. However, I see neither a convincing case for computational efficiency (it is still a net requiring large training as opposed to simpler frequency binning / generalized svd methods), nor a convincing case for a classification accuracy breakthrough (results are similar to many other known methods). Furthermore the pictures showing *activation* patterns are not necessarily revealing nor are they convincingly artifact - filtering. Overall the decision is to reject based on insufficient significance and impact of result - although the paper is otherwise fairly well written and organized.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
WEEGNET: an wavelet based Convnet for Brain-computer interfaces
Brain-computer interfaces (BCI) are systems that link the brain with machines using brainwaves as a medium of communication using electroencephalography to explore the brain activity which is an affordable solution, noninvasive, easy setup, and portability. However, the neural signals are noisy, non-stationary, and nonlinear where the processing of those signals in a pattern recognition problem needs a complex pipeline of preprocessing, feature extraction, and classification algorithms that need an apriori knowledge to avoid compatibility issues and a deep understanding of the studied signals. Moreover, some techniques need a huge computational power on the CPU and a huge size of RAM. Therefore, several papers proposed to use Deep Learning to get state of the art performance and visualization of the learned features to have more understanding about the neural signals. But, the convolutional neural network (Convnet) are not used properly and the results are often random when we reproduced the works. Hence, we propose a combination of the discrete wavelet transform (DWT) and a Convnet that processes raw EEG data. The DWT will be used to reduce the size of the data without losing useful information. Also, a modified version of EEGNET will be used to extract the features and classification.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper applies CNN architectures and wavelet decomposition of some moderate-dimensional EEG signals from a recent EEG-BCI competition with the aim of using representational compactness to increase classification efficiency while retaining computational efficiency. The motivation is good and the position in the (vast) EEG-BCI literature is appropriate. However, I see neither a convincing case for computational efficiency (it is still a net requiring large training as opposed to simpler frequency binning / generalized svd methods), nor a convincing case for a classification accuracy breakthrough (results are similar to many other known methods). Furthermore the pictures showing *activation* patterns are not necessarily revealing nor are they convincingly artifact - filtering. Overall the decision is to reject based on insufficient significance and impact of result - although the paper is otherwise fairly well written and organized.
assistant
The paper applies CNN architectures and wavelet decomposition of some moderate-dimensional EEG signals from a recent EEG-BCI competition with the aim of using representational compactness to increase classification efficiency while retaining computational efficiency. The motivation is good and the position in the (vast) EEG-BCI literature is appropriate. However, I see neither a convincing case for computational efficiency (it is still a net requiring large training as opposed to simpler frequency binning / generalized svd methods), nor a convincing case for a classification accuracy breakthrough (results are similar to many other known methods). Furthermore the pictures showing *activation* patterns are not necessarily revealing nor are they convincingly artifact - filtering. Overall the decision is to reject based on insufficient significance and impact of result - although the paper is otherwise fairly well written and organized.