I have been given a Brain-computer-interface BCI dataset, namely AVI_SSVEP_Dataset.
https://www.setzner.com/avi-ssvep-dataset/
The dataset has 7 different BCI target frequencies for each target on screen.
I need to preprocess digital signal with digital filters and classify each EEG epoch or each EEG trial. For this purpose I am asked to divide the data into two groups train and test data as 66% train and 33% test for each frequency. Afterwards I am required to create and train a machine learning model to predict the unseen test data correctly.
First you can extract 7 SNR at target frequencies as your features. SNR is measured as the signal at the frequency to the noise energy at the neighboring frequencies.
Training the model with these features you can obtain a machine learning model (using Matlab classification learner) for prediction of the test signals. Once you predict these signal labels, you can check predictions with actual true labels and compute accuracy of the model.

How can i perform this task can anyone help meI have been given a Brain-computer-interface BCI dataset, namely AVI_SSVEP_Dataset.
https://www.setzner.com/avi-ssvep-dataset/
The dataset has 7 different BCI target frequencies for each target on screen.
I need to preprocess digital signal with digital filters and classify each EEG epoch or each EEG trial. For this purpose I am asked to divide the data into two groups train and test data as 66% train and 33% test for each frequency. Afterwards I am required to create and train a machine learning model to predict the unseen test data correctly.
First you can extract 7 SNR at target frequencies as your features. SNR is measured as the signal at the frequency to the noise energy at the neighboring frequencies.
Training the model with these features you can obtain a machine learning model (using Matlab classification learner) for prediction of the test signals. Once you predict these signal labels, you can check predictions with actual true labels and compute accuracy of the model.

How can i perform this task can anyone help me I have been given a Brain-computer-interface BCI dataset, namely AVI_SSVEP_Dataset.
https://www.setzner.com/avi-ssvep-dataset/
The dataset has 7 different BCI target frequencies for each target on screen.
I need to preprocess digital signal with digital filters and classify each EEG epoch or each EEG trial. For this purpose I am asked to divide the data into two groups train and test data as 66% train and 33% test for each frequency. Afterwards I am required to create and train a machine learning model to predict the unseen test data correctly.
First you can extract 7 SNR at target frequencies as your features. SNR is measured as the signal at the frequency to the noise energy at the neighboring frequencies.
Training the model with these features you can obtain a machine learning model (using Matlab classification learner) for prediction of the test signals. Once you predict these signal labels, you can check predictions with actual true labels and compute accuracy of the model.

How can i perform this task can anyone help me digital signal processing, filtering, eeg, machine learning, machine training MATLAB Answers — New Questions

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