Electroencephalogram (EEG) signals reveal electrical activity of brain in a person. Brain cells interact by impulses even during sleep. Any disruptions to these impulses induce problems in the individual. Hence clinicians analyze electrical activity of the brain by EEG readings to comprehend the disruptions in the impulses. EEG and its sub bands depict electrical pattern of human brain. EEG data comprises transient components, spikes, other sorts of artifacts due to eye blinking, movement of the individual, anxiousness etc. during EEG collection. Wavelet transformations are effective mathematical technique for sampling approximation to produce clear EEG. It also assists in filtering, sampling, interpolation, noise reduction, signal approximation and signal augmentation, feature extraction. In this study, a survey is done on EEG motor imagery signals and the various classifiers to assess them machine learning methods for EEG signal categorization are studied. Conventional SVM and logistic regression methods combined with basic 2-layer Neural Network (NN) are constructed using python in keras to assess the performances.