Researchers are eager to explore Brain-Computer Interface (BCI) systems in terms of their potential clinical applications. These systems, often integrated with Electroencephalography (EEG), have been developed to assist individuals with disabilities in their daily activities. EEG can detect auditory Steady-State Evoked Potentials (SSEPs); entrained neural responses produced by auditory stimulation, that are typically strongest for amplitude modulations around 40 Hz. This research explored whether neural activity could control a UR-5 robotic arm. During the initial phase, participants attended to auditory stimuli (35 Hz & 40 Hz) presented separately to each ear, whilst a dry electrode EEG system recorded brain signals. This data was used to train a classifier for the main experiment. In this experiment, participants attended to either their left or right ear whilst wearing a dry EEG, prompting a binary response to command the UR-5 robotic arm to move either left or right. Further development of BCI systems in conjunction with EEG systems is necessary to facilitate the execution of more intricate movements of the UR-5 robotic arm, with potential applications in clinical contexts.