Ahmed-Habashy/Dataset-BCI-competition-iv-2b
This is a python code for extracting EEG signals from dataset 2b from competition iv, then it converts the data to spectrogram images to classify them using a CNN classifier.
This tool helps researchers working with brain-computer interface (BCI) data analyze specific types of EEG signals. It takes raw EEG recordings from Dataset 2b of BCI Competition IV, processes them into visual spectrograms, and then classifies these images into two distinct categories using a pre-trained model. It's intended for neuroscientists or BCI developers focused on motor imagery classification.
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Use this if you need to process and classify EEG motor imagery data from BCI Competition IV Dataset 2b to identify specific mental states.
Not ideal if you are working with different types of EEG data, other BCI datasets, or need to train a classification model from scratch.
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Python
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Last pushed
Apr 11, 2023
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