With commercially available hardware and supporting software, different electrical potential brain waves are measured via a headset with a collection of electrodes. Out of the different types of brain signals, the proposed brain-computer interface (BCI) controller utilizes non-task related signals, i.e. squeezing left/right hand or tapping left/right foot, due to their responsive behavior and general signal feature similarity among patients. In addition, motor imagery related signals, such as imagining left/right foot or hand movement are also examined. The main goal of the paper is to demonstrate the performance of machine learning algorithms based on classification accuracy. The performances are evaluated on BCI dataset of three male subjects to extract the most significant features. Each subject undergoes a 30-minute session composed of four experiments: two non-task related signals and two motor imagery signals. Each experiment records fifteen trials of two classes (i.e. left/right hand movement). The raw data is then pre-processed using a MatLab plugin, EEGLAB, where standard processes of cleaning and epoching the signals is performed. The paper discusses machine learning for robotic application and the common flaws when validating machine learning methods in the context of BCI to provide a brief overview on biologically (using brain waves) controlled devices.

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