EEG Motor Imagry Orange Py Is a space to Use data sets of eeg data labled data of imagined mucle movements and attempt use orage to do some tasks that would just have more problems in keras python tensorflow coding. Using orange could make the code smaller. But it may be more cumbersom.
Motor imaging EEG signal recognition is an important and challenging research problem in human-computer interaction.
A motor imagery-based brain-computer interface (MI-BCI) creates a path through which the brain interacts with the external environment by recording and processing electroencephalograph (EEG) signals made by imagining the movement of a particular limb.
As the relevant rhythmic signals during motor imagery are divided into μ rhythm signal of 8–12 Hz and β rhythm signal of 13–30 Hz
Deep Learning (DL) models have gained popularity for EEG classification as they provide a solution for automatic extraction of spatio-temporal features in the signals.
https://en.wikipedia.org/wiki/Convolutional_neural_network
https://www.youtube.com/watch?v=XIowcswb1Hg
EEG Modeling of data and a CNN https://keras.io/examples/timeseries/eeg_signal_classification/
This allows DL models to learn the EEG Signals directly and important spatio-temporal features of the EEG signals, leading to improved accuracy in motor imagery detection.
Electroencephalogram Signal Classification for action identification https://keras.io/examples/timeseries/eeg_signal_classification/
The data set is from 15 subjects at the same time 500 samples long or 15 or 16 channel eeg. https://keras.io/examples/timeseries/eeg_signal_classification/
Saving these biosignal models is important they can take a lot of time to train and smaller processor to use to predict. model.save('path/to/location.keras') # The file needs to end with the .keras extension
https://keras.io/guides/serialization_and_saving/
"Working with data often requires you to move between different formats. One of the most common data transformations you may find yourself needing is to convert DataFrame to CSV. The Comma Separated Values (CSV) format is universally accepted and can be opened in numerous platforms like Excel, Google Sheets, and various database management systems. In Python, the Pandas library makes this process straightforward and efficient."
pandas.DataFrame.to_csv https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_csv.html
Convert DataFrame to CSV in Python [With Best Practices] https://www.golinuxcloud.com/convert-pandas-dataframe-to-csv/
https://keras.io/examples/structured_data/structured_data_classification_from_scratch/
Evaluating Machine Learning Methods for Time Series Forecasting. https://machinelearningmastery.com/how-to-predict-whether-eyes-are-open-or-closed-using-brain-waves/
Datasets collected with labels are important for machene learning a button trigger on the ganglion labled prog can do that D17.
https://docs.openbci.com/Cyton/CytonExternal/
... Li, H., Ding, M., Zhang, R., & Xiu, C. (2022). Motor imagery EEG classification algorithm based on CNN-LSTM feature fusion network. Biomedical signal processing and control, 72, 103342.
Wang, P., Jiang, A., Liu, X., Shang, J., & Zhang, L. (2018). LSTM-based EEG classification in motor imagery tasks. IEEE transactions on neural systems and rehabilitation engineering, 26(11), 2086-2095.
Khademi, Z., Ebrahimi, F., & Kordy, H. M. (2022). A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals. Computers in biology and medicine, 143, 105288.
Garcia-Moreno, F. M., Bermudez-Edo, M., Rodríguez-Fórtiz, M. J., & Garrido, J. L. (2020, July). A CNN-LSTM deep Learning classifier for motor imagery EEG detection using a low-invasive and low-Cost BCI headband. In 2020 16th international conference on intelligent environments (IE) (pp. 84-91). IEEE.
Xu, F., Xu, X., Sun, Y., Li, J., Dong, G., Wang, Y., ... & Yin, S. (2022). A framework for motor imagery with LSTM neural network. Computer methods and programs in biomedicine, 218, 106692.