Maximum number of input neurons to the neural network
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I have EEG data to classify, for 2 classes. The data dimesnion is 30000x512. Where 512 is the number of samples (50:50 for each class). I tried feeding the raw EEG data to neural network and tuned it's parameter with 3 hidden laeyrs [10 10 10], and it works really well in terms of the classification accuracy.
I was wondering if this is acceptable to do.? Or it's necessary that i have to feed reduced number of input neurons in the form of extracted features.?
I will highly appreciate if anyone can guide me through.
Mahesh Taparia 2020년 1월 6일
You are having the dataset which consists of 512 samples with 30000 dimensions. You have less data.
But the way you are training is not optimum. You can use PCA (Principal Component Analysis) to reduce the number of dimensions which will reduce model complexity. For more details about PCA, you can refer to this link.