Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address this issue by combining the single-trial-based ICA filter with zero-training classifier. We developed a two-round data selection method to identify automatically the badly corrupted EEG trials in the training set. The “high quality” training trials were utilized to optimize the ICA filter. In addition, we proposed an accuracy-matrix method to locate the artifact data segments within a single trial and investigated which types of artifacts can influence the performance of the ICA-based MIBCIs. Twenty-six EEG datasets of three-class motor imagery were used to validate the proposed methods, and the classification accuracies were compared with that obtained by frequently used common spatial pattern (CSP) spatial filtering algorithm. The experimental results demonstrated that the proposed optimizing strategy could effectively improve the stability, practicality and classification performance of ICA-based MIBCI. The study revealed that rational use of ICA method may be crucial in building a practical ICA-based MIBCI system.

Currently, many imaging photoplethysmography (IPPG) researches have reported non-contact measurements of physiological parameters, such as heart rate (HR), respiratory rate (RR), etc. However, it is accepted that only HR measurement has been mature for applications, and other estimations are relatively incapable for reliable applications. Thus, it is worth keeping on persistent studies. Besides, there are some issues commonly involved in these approaches need to be explored further. For example, motion artifact attenuation, an intractable problem, which is being attempted to be resolved by sophisticated video tracking and detection algorithms. This paper proposed a blind source separation-based method that could synchronously measure RR and HR in non-contact way. A dual region of interest on facial video image was selected to yield 6-channels Red/Green/Blue signals. By applying Second-Order Blind Identification algorithm to those signals generated above, we obtained 6-channels outputs that contain blood volume pulse (BVP) and respiratory motion artifact. We defined this motion artifact as respiratory signal (RS). For the automatic selections of the RS and BVP among these outputs, we devised a kurtosis-based identification strategy, which guarantees the dynamic RR and HR monitoring available. The experimental results indicated that, the estimation by the proposed method has an impressive performance compared with the measurement of the commercial medical sensors. The proposed method achieved dynamic measurement of RR and HR, and the extension and revision of it may have the potentials for more physiological signs detection, such as heart rate variability, eye blinking, nose wrinkling, yawn, as well as other muscular movements. Thus, it might provide a promising approach for IPPG-based applications such as emotion computation and fatigue detection, etc.

This paper is focused on the experimental approach to explore the potential of independent component analysis (ICA) in the context of motor imagery (MI)-based brain computer interface (BCI). We presented a simple and efficient algorithmic framework of ICA-based motor imagery BCI (ICA-MIBCI) for the evaluation of four classical ICA algorithms (Infomax, FastICA, Jade, and Sobi) as well as a simplified Infomax (sInfomax). Two novel performance indexes: self-test accuracy and the number of invalid ICA filters were employed to assess the performance of MIBCI based on different ICA variants. As a reference method, common spatial pattern (CSP), a commonly-used spatial filtering method, was employed for the comparative study between ICA-MIBCI and CSP- MIBCI. The experimental results showed that sInfomax- based spatial filters exhibited significantly better transfer- ability in session to session and subject to subject transfer as compared to CSP-based spatial filters. The online experiment was also introduced to demonstrate the practicability and feasibility of sInfomax-based MIBCI. However, four classical ICA variants, especially FastICA, Jade, and Sobi, performed much worse as compared to sInfomax and CSP in terms of classification accuracy and stability. We consider that conventional ICA-based spatial filtering methods tend to be overfitting while applied to real-life electroencephalogram (EEG) data. Nevertheless, the sInfomax-based experimental results indicate that ICA methods have a great space for improvement in the application of MIBCI. We believe that this work could bring forth new ideas for the practical implementation of ICA-MIBCI.

Peripheral oxygen saturation (SpO2), a vital physiological sign employed in clinical care, is commonly obtained by using a contact pulse oximeter. With the rapid popularization of ordinary red-green-blue (RGB) webcams embedded in devices such as smartphones or laptops, there are broad application prospects for exploring techniques for non-contact SpO2 extraction using RGB webcams. However, many issues remain to be solved in the traditional webcam-based SpO2 extraction methods, such as the inherent low signal-to-noise ratio (SNR) of alternating current (AC) components of RGB signals and the potential defects in using RGB signals combination for SpO2 extraction. In this study, we conducted an in-depth examination of the existing research on webcam-based SpO2 extraction techniques, analyzed the practical problems in using them, and explored new ideas to solve the problems. Rather than roughly using the standard deviations (SD) of AC components for calculations, we performed blind source separation for AC components, and then used the energy coefficients retained in the mixed matrix to replace the variables required in the algorithm. Moreover, steady data was selected to compensate for the potential defects in using RGB signals combination. Through these efforts, the anti-noise capability of the algorithm was significantly enhanced, and the related defects were compensated for. The experimental results indicated that the proposed method produced reliable SpO2 estimation that could potentially—with further research—be used in real applications.