Design and Manufacture of a Guided Mechanical Arm by EEG Signals

Nowadays, familiarity with how the brain works and the commands issued by it, has attracted the attention of researchers in various sciences. Advances in this field and the growing knowledge about the neural correlates of the commands issued by the human brain have opened new horizons to socio-cognitive robotics. Research has shown that electroencephalogram (EEG) signals can detect electrical activity in the brain. EEG signals contain useful information about brain function and its responses to various phenomena that can be interpreted and sent as control commands. In this paper, an attempt has been made to design and build a mechanical arm that can be guided by moving as well as imagining the movement of the right and left arms. For this purpose, brain signals were taken using an EEG cap. The recorded signals are pre-processed and filtered by the EEGLAB toolbox. The statistical features of these signals were extracted, their number was reduced using the t-test method and the best set was selected by the sequential forward feature selection (SFFS) method. The LDA classifier is then used to classify signals into two classes, the right hand, and the left hand. Motor activities and motor imagery were detected by this algorithm with an accuracy of over 95% and above 90%, respectively. Finally, a mechanical arm with three degrees of freedom was designed and built and the results of the machine learning algorithm were implemented on it.