# Python-For-EEG
Here is a simple repository in which I demonstrate basic analysis on EEG signal. Topics covered:
Credits: I got help from my Ph.D. student - Mr. Akraradet (https://github.com/akraradets) on asymmetry and my Master student - Mr. Nutapol (https://github.com/nutapol97) on motor imagery.
## Topics
**Time-Based** [Datasets: P300 signal]
1. Event-related potentials and 1-dimensional convolution
2. Long short-term memory
**Frequency-Based** [Datasets: DEAP and SSVEP]
3. Spectral analysis and alpha asymmetry
4. Canonical correlation analysis
**Component-Based** [Datasets: Motor Imagery]
1. Event-related desynchronization
## Datasets
All datasets can be downloaded at: [Google Drive](https://drive.google.com/drive/folders/1q_UbAIP1yPCkIjYCMIaJWG2cBn0K4nfa?usp=sharing) (except the DEAP dataset)
1. P300: This is my personal P300 signal I acquired through my OpenBCI headset while visually attending to 36 alphabetical targets. When the flash flickers at the target I am looking at, there will be a signal rise at 300ms after the stimulus onset. In the dataset, the columns are timestamps (128 samples per second), 16 channels of 'Fp1', 'Fp2', 'F7', 'F3', 'F4', 'F8', 'C3', 'Cz', 'C4', 'T5', 'P3', 'P4', 'T6', 'POz', 'O1', 'O2', and the marker indicating the events. The marker format will be explained more in the notebook.
2. DEAP dataset: It's basically a dataset regarding participants watching some 1-min emotional videos while wearing 32 EEG channels. For more details, read https://www.eecs.qmul.ac.uk/mmv/datasets/deap/
3. SSVEP: Here we record users looking at three different circles flickering at 6, 10, and 15Hz respectively. We will be classifying the signals using filterbank canonical correlation analysis.
4. Motor Imagery: Here we record one user performing imagined left and right movements. We shall explore event-related desynchronization for decoding the classes.
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一份很基础讲解的EEG资料
共24个文件
fif:10个
ipynb:5个
tar:2个
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2023-06-04
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包含着最基础EEG名词的解释,数据的处理,数据转换。 因为它为实现这些任务提供了许多有用的方法。首先,我们要把 pandas类型 数据 转换成 mne 类型。这是将df转换成raw的函数。 伪影是需要消除的噪音。频率受限伪影的两个例子是缓慢漂移和电源线噪声。下面我们将说明如何通过过滤来修复这些缺陷。 电源噪声是由电网产生的噪声。它由50Hz(或60Hz,取决于你的地理位置)的尖峰组成。一些峰值也可能出现在谐波频率,即电力线频率的整数倍,例如100Hz, 150Hz,…(或120Hz, 180Hz,…)。 01-ERP+LSTM +P300.ipynb 2022/12/6 14:0802- CNN1D + P300.ipynb 2022/12/6 14:0803 - Asymmetry + DEAP.ipynb 2022/12/6 14:0804 - CCA + SSVEP.ipynb 2022/12/6 14:0805 - ERD + Motor Imagery.ipynb
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Python-for-EEG.zip (24个子文件)
Python-for-EEG
ssvep-10trials-3s-chaky-bigsquare.csv 3.17MB
p300-6trials-12rep-chaky.csv 4.68MB
Python-for-EEG-main
01 - ERP + LSTM + P300.ipynb 1.17MB
02 - CNN1D + P300.ipynb 1.17MB
05 - ERD + Motor Imagery.ipynb 306KB
03 - Asymmetry + DEAP.ipynb 162KB
figures
exp.png 36KB
models
p300LSTM.pth.tar 49KB
motorLSTM.pth.tar 45KB
.gitignore 994B
04 - CCA + SSVEP.ipynb 269KB
README.md 2KB
Python-for-EEG-main.zip 2.23MB
motorimagery.zip 12.95MB
motorimagery
S020
S020R03.fif 1.85MB
S020R02.fif 582KB
S020R04.fif 1.89MB
S020R07.fif 7.02MB
S020R01.fif 582KB
S020R09.fif 1.85MB
S020R05.fif 1.85MB
S020R10.fif 1.88MB
S020R08.fif 1.88MB
S020R06.fif 1.88MB
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资源评论
- weixin_434126152024-04-15资源有一定的参考价值,与资源描述一致,很实用,能够借鉴的部分挺多的,值得下载。
- m0_748127422024-04-01终于找到了超赞的宝藏资源,果断冲冲冲,支持!
- 南漂行者2023-07-29资源内容详尽,对我有使用价值,谢谢资源主的分享。
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