Development Prototype System of Arm's Motor Imagery Utilizing Electroencephalography Signals (EEG) from Emotiv with Probabilistic Neural Network (PNN) as Signal Analysis

A modeling arms post-stroke therapy used command brain signals generated by Electroencephalography (EEG) has been designed. EEG signals used to provide motoric information. The unique form of signal EEG describe commands to move the limbs. On condition paralyzed, motoric information on the EEG signals will still be found when someone tried to move his limbs. In this research, we aim used the motoric information on the EEG signals as neuron-feedback with combine 4 input electrode (F3, F4, FC5, FC6). EEG signal acquisition using the Emotiv EPOC+ portable. Probabilistic Neural Network (PNN) function as signal processing. This function was applied to the recognition research of motor imagery EEG signals (imagining arms movement). The parallel computing characteristic of PNN not only improved the generation ability for network, but also shorted the operation time. The result of PNN are maximum mu power, maximum beta power, mu frequency and beta frequency that provided value to calculate classification accuracy. The experimental results show that the accuracy for training on average is 85.49% - 91.32% while the value for testing is 82.6% - 87.6%. Therapy tool mimics nBETTER Upper Limb Feedback. The therapeutic tool will be active, when the value of the EEG signal testing is greater than 80%. In the future, this modeling post-stroke therapy can reduce dependency from physiotherapist.