Low-quality training data detection method of EEG signals for motor imagery BCI system.

BACKGROUND: The design and implementation of high-performance motor imagery-based brain computer interface (MI-BCI) requires high-quality training samples. However, fluctuation in subjects' physiological and mental states as well as artifacts can produce the low-quality motor imagery electroencephalogram (EEG) signal, which will damage the performance of MI-BCI system. NEW METHOD: In order to select high-quality MI-EEG training data, this paper proposes a low-quality training data detection method combining independent component analysis (ICA) and weak classifier cluster. we also design and implement a new online BCI system based on motor imagery to verify the online processing performance of the proposed method. RESULT: In order to verify the effectiveness of the proposed method, we conducted offline experiments on the public dataset called BCI Competition IV Data Set 2b. Furthermore, in order to verify the processing performance of the online system, we designed 60 groups of online experiments on 12 subjects. The online experimental results show that the twelve subjects can complete the system task efficiently (the best experiment is 135.6 s with 9 trials of subject S1). CONCLUSION: This paper demonstrated that the proposed low-quality training data detection method can effectively screen out low-quality training samples, so as to improve the performance of the MI-BCI system.

Simultaneous detection of 93 anabolic androgenic steroids in dietary supplements using gas chromatography tandem mass spectrometry.

In recent years, anabolic androgenic steroids (AASs) have been frequently detected as undeclared ingredients in dietary supplements, where the adverse analytical findings (AAFs) were obtained from analysis of athletes' urine samples after ingestion. In our present study, a GC-MS/MS method for simultaneous detection of 93 anabolic steroids was developed. The chromatographic and mass spectrometric conditions were optimized, and selective reaction monitoring (SRM) mode was adopted to obtain the necessary sensitivity. The whole sample analysis process was completed within 23 min, and the limit of detection (LOD) was 0.5-4 ng.g-1 for solid samples and 0.1-0.8 ng.mL-1 for liquid samples. This method was verified according to World Anti-Doping Agency (WADA) regulations. In addition, the method was found to be specific, accurate. The developed method was then applied to a routine analysis of more than 300 liquid and solid dietary supplements, and one testosterone-positive sample was found. Three suspected drugs, (4-hydroxyandrostenedione, DHEA, and 6-Br androstenedione) were found in three dietary supplements obtained from the Internet through the pretreatment method of this study. This study provides a high-throughput method for screening and monitoring the ingredients of supplements and their subsequent harm to public health.

A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface.

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.