Spectral analysis and artificial neural network based classification of three mental states for brain machine interface applications

Abstract

Brain machine interface (BMI) is an emerging technology that aims to assist people with disabilities as well as the aged by allowing their users to intuitively control external devices by intent alone. This paper presents a signal processing technique for a low cost brain machine interface (BMI) that uses spectral analysis and artificial neural network (ANN) to classify three mental states from electroencephalographic (EEG) signals. In this study, a BMI system has been prototyped to classify the intention of moving an object up or down and at rest state. EEG signals are recorded using a consumer grade EEG acquisition device. The device is equipped with 14 electrodes but only 8 electrodes are used in this study. To evaluate the system performance, online classification experiments for three subjects are conducted. True positive and false positive rates are used as an evaluation index. Experiment results show that despite the high difficulty of the mental tasks, the proposed method is capable of achieving an overall true positive rate of up to 67% with 15 minutes of training time by a first time BMI user. Furthermore, offline analysis is carried out using the same EEG data to explore ways of using spectral analysis and ANN to reduce erroneous classifications. Analysis results show that by setting the classification threshold value higher, the false positive rate can be reduced. Another finding suggests that in contrast with the study results by other research teams, the use of multiple ANNs to classify three mental states do not improve the accuracy. Lastly, a hamming window size of 64 samples is found to be optimal for achieving real-time control when performing spectral analysis.