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Spectrogram of EEG Signal

11 vues (au cours des 30 derniers jours)
Joana le 22 Déc 2019
I have EEG signals which i need to classify, for that i need to change the EEG signals to the Images, i.e by taking spectrogram of the signal.
The data is recorded at 960hz, and it is in the format of number of channels x sampling ffrequency x iterations = 62 x 960 x 200. (for reference i am attaching the signal for 2 channels)
Can anyone please guide me how i can do that. ?
I tried spectrogram command but it only takes 1D data.
Thanks in advance.
  6 commentaires
Joana le 24 Déc 2019
Hi Jesus,
Regarding the ocular movement data, did you train NN with raw data or you extracted some features.?
I tried with feeding the NN with raw EEG data, after pre-processinf of course, but without any feature extraction. I applied data in the form of 30,000 x 200. Where 30000 were the input neurons. It works really well for the classification. But i haven't found any documentation if we can do it like this.? Do you any material or something to add on regarding NN number of input neurons.?
Jesus Sanchez
Jesus Sanchez le 26 Déc 2019
Modifié(e) : Jesus Sanchez le 3 Jan 2020
It was around 3 years ago in a university project, but I think it should be like that. Each neurone is able to accept only one data as input. Therefore, you need as many neurons as the size of your signal (30000). Currently I do not have access to my lecture notes (cheers for holydays!) so I cannot tell you more in depth. However, I can give you several remarks:
  • I guess you already classified the 200 signals. Meaning, you already knew which " EEG signal" they are. When training the NN, did you input all your signals? If you did that, what ou have is an overtrained and skewed NN, since it has been trained with the same data sample as the one used to check if it works.
  • In order to train it properly, do not use all signals. As an example, if you want to train the NN to recognize 5 different patterns, and you have around 50 signals for each pattern, I would use 10 or 15 signals and use the rest to check whether it is working properly or not.
  • Remember the different factors that affect the NN performance. Your can play with the numer of layers of the NN, the training data, etc.
To do the ocular recognition, we pre-recorded around 10 or 20 signals for each ocular movement (raw data), cleaned them from the 50 Hz noise and other noises and then trained the NN. We had some problems derived of working in real time, but it worked quite OK.
Regarding feature extraction, you could consider that the NN does that itself during the training phase. It is done in the hidden layers of the NN. If I remember properly, it calculates a weight to each neurone of the hidden layers and the combination of all of the neurones and the hidden layers allows the NN to rcognize the different signals. I will be able to access my slides in a couple of weeks if you want further info. I remember the decision process was explained quite well.
EDIT: I have remembered that I processed the signals in time domain, instead of frequency domain, as the time waveforms are different depending of the ocular movement. So what I did was to transform them to freq. domain, clean them and then transform them back to time doamin. Then I classified them. Maybe the same happens for EEG signals?

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