eeyhsong / eeg-transformer Goto Github PK

Hello
After running the transformer a couple of times, I'm receiving significantly smaller results compared to the study's results. I've attached an image of one of my confusion matrices which in comparison with the study, is much worse.

The only code I've edited was for the purpose of using it in google collaborate, otherwise, all the code is the same as how it was here on GitHub. What would be the cause of this and am I missing something quite obvious that is missing?
Best
Merlin

'D:\MI\true_labels\A0',num2str(subject_index),session_type,'.mat' I don't have this in my dataset

It's an excellent work that inspired me a lot. Now I am appling it to a 2-class task for DatasetIV-2b. Which parameter should I change?
I've found some potential code, Could you help to locate the para that needs modifing? Thank you!
In Trans.py:
Line 130: forward_expansion=4
Line 167: emb_size=10, depth=3, n_classes=4
LIne 253: self.c_dim=4
In common_spatial_pattern.py:
Line 15: idx_3=... (comment this line?)
Line 18: in range(4) and following elif for n_class==3
Line 70: Wb = np.concatenate((W[0][:, 0:4], W[1][:, 0:4], W[2][:, 0:4], W[3][:, 0:4]), axis=1)
Thanks again.

代码中是将所有576个trial合起来然后选516个用于训练。
如果改为按照A01_T用于训练，A01E用于测试这样的数据划分的话，即288个trial用于训练,288个trial用于测试，模型的结果看上去不是很理想。
请问大佬针对这种情况有什么可以优化的手段吗

thank you very much

作者您好，非常感谢您发表的这篇论文，在拜读了您的文章后对我产生了非常大的启发。
但是我在复现您的文章时，始终不能达到论文所诉的2a数据集上的82%的accuracy，我这边感觉可能是调参的问题，希望能得到您技术上的帮助。非常感谢！

Hi there,
I'm trying to implement your code on the BCI IV 2a dataset. However, I'm wondering how is the structure of the label and data in your mat file since I couldn't rebuild the mat file based on the "getData.m" function. If you can provide a tree structure of the data regarding trials and labels, it will be clearer.

Hello
I have been trying to get your getData.m function working but I have run into an issue with labelling the evaluation datasets. Each time I run the code, the labels for all of the subject's evaluation data are labelled as NaN, preventing the Trans.py code to have a test accuracy score other than 0.

I looked at the BCI competition IV website and subject 1 from dataset 2a should have class labels but when I run
[s, HDR] = sload('dir of A01E');
The HDR.ClassLabels are all NaN so am unsure where I am going wrong.
Any help would be appreciated.
Best
Merlin

The slice size is 10? How should I understand the slicing of the data

Would it be possible to share the pre-trained weights of the transformer, I would like to transfer learn the transformer to a BCI task