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A Brain Computer Interface (BCI) is a communication device between the brain and an external device, usually a computer to repair or assist human motor-sensory functions. In this project, both acquisition hardware and software of a two-channel EEG brain computer interface based on motor imagery related mu and beta rhythms was designed. In order to discriminate left and right hand movement imagery, three different feature extraction methods were developed using: Discrete Wavelet Transform, Power Spectrum Analysis and Band Pass FIR filters. These features were used as inputs to a two layer feed forward back propagation neural network for classification. Designed system was trained and simulated with the data provided in BCI Competition II. With the direction of the results, a low power system with the TI MSP430 microcontroller using FIR filters and a neural network was implemented.