The Matlab/ToolboxFeatureExtraction contains methods to plot the recorded signals in the time- and frequency-domain and calculation functions for the respective transformations.
With this, one can perform a time-frequency analysis and extract custom features.
Moreover, the common spatial patterns algorithm for automated feature extraction is implemented here.
The Matlab/ToolboxClassification is applied to perform cross-validations and to train, evaluate, and apply the Machine Learning models "stepwise linear regression" and "Fisher discriminant analysis".
The contents of this repository were created in the scope of the lecture "Brain Machine Interfacing" in summer term 2021 at Ulm University.
Hence, the repository also contains a project report and the respective BMI application use-cases for the toolboxes, namely "SSVEP", "P300 Speller", and "Motor Imagery".
Due to licensing, the datasets cannot be provided. Nevertheless, I hope that the use-cases are helpful in demonstrating the usage of the toolboxes.