Need a deep learning program on python to predict anxiety based on feature extraction from raw eeg signal and classifying using different classifiers based on dataset I provide
Budget: $100 – $150 USD
I need a deep learning program to extract features, namely:
a) Time Domain features: qEEG, Hjorth Features, Fractal Dimension,
b) Frequency domain features: Band power, Hilbert-Huang Spectrum
c) Time - frequency domain feature: DWT based RMS, DWT based band power
d) Electrode combination feature: STFT based diff./ rational asymetry
e) All feature
Then feature selection and classify using:
MLPNN, RBFNN, RF, DT, SVM (WITH BEST KERNER FUNCTION), BAGGING SVM, BAGGING DT, XG BOOSTING, GRADIENT BOOSTING AND EXTENDED ANN.
a) Time Domain features: qEEG, Hjorth Features, Fractal Dimension,
b) Frequency domain features: Band power, Hilbert-Huang Spectrum
c) Time - frequency domain feature: DWT based RMS, DWT based band power
d) Electrode combination feature: STFT based diff./ rational asymetry
e) All feature
Then feature selection and classify using:
MLPNN, RBFNN, RF, DT, SVM (WITH BEST KERNER FUNCTION), BAGGING SVM, BAGGING DT, XG BOOSTING, GRADIENT BOOSTING AND EXTENDED ANN.