In this study we explore the idea of direct brain to brain communication using what is known as Brain to Brain Interface. Brain to Brain Interface is an integration between a Brain to Computer Interface and a Computer to Brain Interface. For the Brain to Computer interface, we have used Motor Imagery signals extracted via EEG. Computer to Brain Interface is achieved by Transcranial Magnetic Stimulation. Therefore a Transcranial Magnetic Device is developed as part of the project. Motor Imagery based Brain to Computer Interface is intended to classify between two motor imagery tasks. That is the closing of left vs. right fist. Real time EEG is acquired from the subject who is performing the motor imagery task and pre-processed. Different classifiers which use different features are being tested to decide on the best classifier. A wavelet based Neural Network has shown to classify considerably well compared to the other classifiers tested. Input to this classifier is feature vector composed of different measures calculated using wavelet transformed signal. A maximum classification accuracy of 71% was recorded with Coiflet wavelets for a network with 20 neurons in the hidden layer. Transcranial Magnetic Stimulation Device is intended to cause a muscle twitch in a second subject. The device creates a strong localized magnetic field over the motor cortex of the subject. The Figure 8 coil that would create intended magnetic field with maximum intensities and focality was designed. The intended high current pulse is obtained by discharging a capacitor bank. A silicon controlled rectifier is used for switching of pulse generator circuit. By discharging the capacitor bank with capacitance of 1800μF charged to 610V a monophasic current pulse with peak current of 7kA can be obtained.