Identification of three mental states using a motor imagery based brain machine interface

The realization of robotic systems that understands human intentions and produces accordingly complex behaviors is needed particularly for disabled persons, and would consequently benefit the aged. For this purpose, a control technique that recognizes human intentions from neural responses called brain machine interface (BMI) have been suggested. The unique ability to communicate with machines by brain signals opens a wide area of applications for BMI. Recently, combination of BMI capabilities with assistive technology has provided solutions that can benefit patients with disabilities and many others. This paper proposes a BMI system that uses a consumer grade electroencephalograph (EEG) acquisition device. The aim is to develop a low cost BMI system suitable for households and daily applications. As a preliminary study, an experimental system has been prototyped to classify user intentions of moving an object up or down, which are basic instructions needed for controlling most electronic devices by using only EEG signals. In this study, an EEG headset equipped with 14 electrodes is used to acquire EEG signals but only 8 electrodes are used to identify user intentions. The features of EEG signals are extracted based on power spectrum and artificial neural network are used as classifiers. To evaluate the system performance, online identification experiments for three subjects are conducted. Experiment results show that the proposed system has worked well and could achieve an overall correct identification rate of up to 72 % with 15 minutes of training time by a user with no prior experience in BMI.