A Multiuser EEG Based Imaginary Motion Classification Using Neural Networks

Using Electroencephalography (EEG) to detect imaginary motions from brain waves to interface human and computer is a very nascent and challenging field that started developing rapidly in the past few decades. This technique is termed as Brain Computer Interface (BCI). BCI is extremely important in case of people who are incapable of communicating due to spinal cord injury. This technique uses the brain signals to make decisions, control and communicate with the world using brain integration with peripheral devices and systems. In this paper, in order to classify imaginary motions, raw data are used to train a system of neural networks with a majority vote output. EEG data for 3 subjects are used from the BCI Competition III dataset V. Each subject has data collected in three sessions representing three different types of imaginary motions. Using an optimized set of electrodes, classification accuracy was optimized for the three users as a group. A cross validation method is applied to improve the reliability of the generated results. The optimization resulted in an electrode structure consisting of 15 electrodes with a relatively high classification accuracy of almost 80%.