Summary The brain computer interface (BCI) can provide a direct channel of communication between an external device and the brain without including any type of muscular activity. Various applications of BCI have been stated in literature, and one of the most exciting areas which have not been extensively investigated is the field of multimedia. BCI can find huge potential in the area of command and control of video games. Video games have found huge potential for improving the gait and balance of people struggling with Parkinson's disease. Such systems make use of the brain signals which are captured through electrodes on scalp as electroencephalogram (EEG) or through implanted electrodes as electrocorticography (ECoG). The signals are then used as input commands to get desired output in an external device. To address the memory management issue arising due to the huge volume of ECoG data and to achieve distributed computation for improving the efficiency of BCI, the computations are implemented in Cloud. In this work, ECoG signals are pre‐processed, and all features are extracted by using the wavelet packet transform (WPT), common spatial patterns (CSPs), Laplacian filter, and the Kalman filter. The Adaboost classifier was employed for ECoG signal classification.