The Brain Computer Interfaces (BCI) are devices allowing direct communication between the brain of a user and a machine. This technology can be used by disabled people in order to improve their independence and maximize their capabilities such as finding an object in the environment. Such devices can be realized by the non-invasive measurement of information from the cortex by electroencephalography (EEG).

Our work proposes a novel BCI system that consists of controlling a robot arm based on the user's thought. Four subjects (1 female and 3 males) aged between 20 and 29 years have participated to our experiment. They have been instructed to imagine the execution of movements of the right hand, the left hand, both right and left hands or the movement of the feet depending on the protocol established.

EMOTIV EPOC headset was used to record neuronal electrical activities from the subject's scalp, these activities were then sent to the computer for analysis. Feature extraction was performed using the Principal Component Analysis (PCA) method combined with the Fast Fourier transform (FFT) spectrum within the frequency band responsible for sensorimotor rhythms (8 Hz–22 Hz).

These features were then fed into a Support Vector Machine (SVM) classifier based on a Radial Base Function (RBF) whose outputs were translated into commands to control the robot arm.

The proposed BCI enabled the control of the robot arm in the four directions: right, left, up and down, achieving an averaged accuracy of 85.45% across all the subjects.

The results obtained would encourage, with further developments, the use of the proposed BCI to perform more complex tasks such as execution of successive movements or stopping the execution once a searched object is detected. This would provide a useful assistance means for people with motor impairment.