NeuroTechnology Laboratory Tecnologico de Monterrey
Research Paper This project retrieved Electromyographic signals or EMG signals from 6 different hand movements. In order to obtain the best results, 9 different classifier were used to train the model. To increase the accuracy of the model, Warm Start and Partial Fit are used to readjust the model's performance.
Each classifier uses 5 K-Folds, Cross validation and 36 time-domain features. These classifiers are: LDA, SVM, MLP and NNET. Some classifiers have different hyperparameters to obtain the 9 mentioned.
The dataset consists of 30 subjects, and 240 trials.
- Single Subject Independent (SSI)
- This studie trains the model using just one subject from the 30.
- Leave One Out Participant (LOOP)
- This studies trains the model using 29 subjects from the dataset and tests with the remaining subject.
- Warm Start and Partial Fit
- Readjusting the model's performance by using WS and PF
- ANOVA and Tukey's HSD
- Obtain models performances' statistics.
- main.ipynb
- This script is the source of the results, it obtains the models of the training phase and tests.
- ANOVA_TUKEY.ipynb
- This script computes the statistical tests from the classifiers
- PFvsWSInstro.ipynb
- To comprehend how partial fit and warm start is computed using classifiers
- LOOP_OL_Tables.ipynb, SSI_OL_Tables.ipynb and WSvsPF_OL_Tables.ipynb
- Create overleaf LATEX tables of the results.
- SSInLOOP.ipynb
- Test the studies SSI and LOOP
- Matlab/src/main2.mlx
- First approach to EMG data analysis and classifiers.
Jose Miguel Figarola Prado - [email protected] - [email protected]
Yelile Iga Valdes - [email protected]
Victor Andrés Gonzalez Saldaña - [email protected]