Feature Extraction Technique for Classification methods of EEG based dataset

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A feature represents a distinguishing property and a functional component obtained from a section of patterns Extracted features are meant to minimize the loss of important information embedded in the signal More recently a variety of methods have been widely used to extract the features from EEG signals among these methods are Fast Fourier Transform(FFT), Wavelet Transform(WT) and Auto-Regressive Method(ARM).

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thank you for video, can you please share some tutorial about how to extract the features from eeg dataset using python

wassimdiai

can feature extraction of motor imagery signals from the 8 channels dataset be done using FFT and DWT???? if not please suggest any other Feature extraction method for this.

Moonwalkerrabhi

if you extract the features from EEG by FFT, DFT, why you are not using the extracted features. I mean you have used 14 channel features + 5 features (alpha, bita, gama). So, which features you extract by FFT and DFT?

niloymridha

How the feature can minimize the loss of important information which is embedded in the signal?

mohammedkareem

Thanks very much for such video. Could you publish tutorials about eeg processing and classification it will be great thing.

Mohamm-ed

Nice video. If you want to get more details then you can visit CSForum for image processing.

mutiullahjamil

nice try, keep it up, you can do it!!

learntogetherTV

Can you share python code for this project?

ashwinkumar

Feature Extraction Technique for Classification methods of EEG based dataset

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