Analysis of Electroencephalographic Signal Acquisition using EPOC Emotiv for use in Robotic Arm Moveentm
In recent years, new research has brought the field of electroencephalograph (EEG)-based brain–computer
interfacing (BCI) out of its early stages into a phase of relative maturity through many demonstrated prototypes to assist the
periphery compromised patients. It is a worth- while technology for disabled people enabling them to reinstate a damaged
motor nerve or any neural pathway. This study introduces the development of Brain computer interface by means of a
non-invasive wireless electroencephalograph (Emotiv EPOC sy ste m) and its application to control a prototy pe
robotic arm. After the pre-processing and spectral analysis of raw EEG signals, an averaged peak value for selected
channels were obtained as a feature vector for each movement. Two algorithms n a m e l y , linear discriminant
analysis (LDA) and quadratic discriminant analysis (QDA) were used to differentiate the raw EEG data into their
associative movements. Performance of a classification algorithms were assessed, which revealed that the accuracy was
71.77 (±0.76) % and 86.57(±0.79) % of LDA and QDA respectively. Feature vector resulted in superior performance of
86.57 (±0.79) % wi t h QDA. The averaged peak value of PSD for selected seven channels were then used to move a
robotic arm successfully i n the two d i r e c t i o n s i . e . “Up (elbow flexion)” and “Down (elbow extension)”.
Index Terms- Electroencephalogram (EEG), brain-controlled interface (BCI), Power spectrum d e n s i t y (PSD).