Recognition Classification And BCI Experiments For Mental EEG Of Imaging Left-right Hands Movement

Background Recent years, a novel kind of human-computer interface technology using EEG signal of different mental tasks as to achieve Brain-computer interface (BCI) has been explored widely. BCI is a direct and fire-new system of information exchange and control channels between brain and computer or other electronic equipment, which do not depend on the brain's normal output channels (peripheral nerves and muscles). One of the most important purposes for the BCI comes mainly from the hope that the system can provide language communication and environmental control for those with severe motor disabilities but normal thoughts, and improve the quality of their lives. What's more, the BCI technology has possible applications in other fields, such as special workover and military affairs, and it will be a new way for exchange or control of information and entertainment. BCI research has drawn attention of scientists in brain-science research, rehabilitation engineering, and biomedical engineering or human machine automatic control. Currently, the BCI technology is still under development. The key point technique need to be solved for the current BCI,such as correct rate and speed of recognition, stability of performance, et al.BCI essentially recognize the specific pattern of EEG, and translate the EEG into the external information according to the regulation in advance. In practical application, it is very important that we decide to carry out the experimental design, the acquisition of signal (collection of datum) and the arithmetic selection (feature extraction and classification of EEG signal), which also affect the final results. Therefore, the design of experiment and the acquisition of signal are the first step in the establishment of entire BCI and the premise of getting the good result. At present, non-invasive way is the primary way for the acquisition of EEG, which have three types:â‘ EEG was recorded by the scalp electrode under the condition of no stimulation during the mental tasks;â‘¡EEG was gotten by the single visual evoked potential stimulus;â‘¢EEG was obtained by Event-Related Desynchronizations and Event-Related Synchronization ofμrhythm in the region of sensorimotor cortex. The first type of recording method was taken in our study to acquire the control signal for BCI's construction, that is, EEG of mental tasks was obtained under the thoughts activity of imaging left-right hands movement.In present research, collection, feature extraction and recognization classification of control signal based on EEG under the imaging left-right hands movement have been carried out for the study on BCI system construction. The purpose of our study is to explore a practical way of BCI based on mental EEG by imagining movement, find a suitable method of signal processing to extract different EEG feature, which can get precise external control command of BCI system, accordingly, to enhance the correct rate of recognition for BCI system, all of which establish a substantial theory and experiment foundation for the final application of BCI.Methods The principle of BCI construction based on the mental EEG under the thoughts activity of imaging left-right hands movement was explained according to the basic features in the different thoughts. The experimental system of signal collection for BCI based on mental EEG of imaging left-right hand movement was established by the utilizing double-computer and Active One (biopotential measurement system).In our study, scalp electrode was adopted to record EEG in cerebral cortex. This recording skill is no traumatic and the users don't need to be trained. The leftward or rightward arrow occurred randomly on screen according to software programming produced the command mode of experimental cue. Then, the subjects gave a homologous selection so that they can press the key. In this paper, the different mental tasks for imaging left-right hands movement from 6 subjects were studied in the experiment of signal collection at three different time section (hint keying after arrow 2s, 1s and 0s). Then, the off-line experimental data were processed and analyzed: By studying several processing methods of mental EEG signal and comparing of each other, the extraction method of EEG features, which can reflect different mental tasks by utilizing the method of combining wavelet multitude resolution analysis with statistics characteristics analysis, was introduced. Meanwhile, several basic wavelet functions that can be used to extract signal were proposed in our study. On the base of the pattern recognition methods for signal deeply compared and systematically studied, these features were recognized and classified by using Feed-forward Back-propagation Neural Network (BP-NN). Results Average delay timeΔt₂,Δt₁ andΔt₀ for all subjects in three different time section were analyzed, we discovered that there is significant difference (p<0.05) betweenΔt₀ andΔt₂ , betweenΔt₀ andΔt₁, but there is no significant difference (p>0.05) betweenΔt₂ andΔt₁. At three circumstances (at t=1s particularly), there are obviously different features for imaging left-right hands movement about 0.5¹s before practical movement, these features have significant difference.The main results about recognization and classification were obtained as the fellow: Under the condition of three different time section, when the train sample and test sample come from the same subject (that is, some sample of a subject are the train, the others of the subject are the test), for example, feature vectors to be recognized and classified all come from the C3 passage, the average correct rate of classification for the test sample at three conditions are 65.00%,86.67% and 72.00% respectively, the maximum is 90.00%. These results suggest that ideal classification effect relatively for the same subject were obtained. However, the train sample and the test sample come from different subjects (that is, one subject is the sample of train, the others are the sample of test), the correct rate for others subjects'the test are about 60.00%. These results indicate that there is still some difference among the subjects.Under the condition of the same time section (as hinting keying after arrow 1s), the C3,C4,CZ and the incorporation of three electrodes were selected as feature vectors, the average correct rate of classification for the test sample are 86.67%,76.67%,70.00%and 65.00% respectively. These results showed that the classification rate obtained from feature vectors selecting certain an electrode was high more than the incorporation of these electrodes.Conclusion In this paper, it was feasible to design experimental project for acquiring signals of BCI at three circumstances. The extractive method of feature vectors in the domain of wavelet transform, which can effectively remove noise, decrease dimension and extract feature of signal,was proposed and confirmed as a effective and practical technique. The ideal correct rate of classification relatively and the external control signal of BCI system were obtained through using the pattern recognition classification method based Feed-forward Back-propagation Neural Network (BP-NN) to recognize features of mental EEG for imaging left-right hands movement. It is an available method of signal pattern recognition, which is come true easily.On the base of selecting suitable passage to construct feature vectors, we got higher correct rate of recognization and classification under hinting keying after arrow about 1s. This shows it was helpful to increase the correct rate by reasonable experimental design. The feature extracting method proposed in this study has been proven feasible to be used as external control signals for BCI system. This study has provided new ideas and methods for feature extraction and classification of different mental tasks for BCI.