In the research of non-invasive brain-computer interface (BCI), independent component analysis (ICA) has been considered as a promising method of electroencephalogram (EEG) preprocessing and feature enhancement. However, there have been few investigations and implements about online ICA-BCI system up till now. This paper reports the investigation of the ICA-based motor imagery BCI (MIBCI) system, combining the characteristics of unsupervised learning of ICA and event-related desynchronization (ERD) related to motor imagery. We constructed a simple and practical method of ICA spatial filter calculation and discriminate criterion of three-type motor imageries in the study. To validate the online performance of proposed algorithms, an ICA-MIBCI experimental system was fully established based on NeuroScan EEG amplifier and VC++ platform. Four subjects participated in the experiment of MIBCI testing and two of them took part in the online experiment. The average classification accuracies of the three-type motor imageries reached 89.78% and 89.89% in the offline and online testing, respectively. The experimental results showed that the proposed algorithm produced high classification accuracy and required less time consumption, which would have a prospect of cross platform application.
Blind source separation technique based on independent component analysis (ICA) can separate blood volume pulse (BVP) from the facial video and then realize the telemetry of heart rate, blood oxygen saturation, respiratory rate and other vital signs parameters. However, the superiority of ICA in BVP extraction has not been demonstrated in the existing researches. Some researchers suggested using traditional G-channel method for BVP extraction (G-BVP) instead of ICA method (ICA-BVP). This study investigated the applicability of ICA-BVP comparatively. To solve the inherent permutation problem of ICA, a spectral kurtosis-based method was proposed for BVP identification. The experimental results based on the facial video datasets from 9 subjects shows that ICA-BVP method has apparent advantages in motion artifacts attenuation and ambient light changes elimination. The kurtosis-based method achieved a good performance in BVP identification and dynamic heart rate (HR) estimation. In practical application, the proposed ICA-BVP method could present a better stability and accuracy in vital signs parameters extraction.