Objective . Task-related component analysis (TRCA) is a representative subject-specific training algorithm in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces. Task-related components (TRCs), extracted by the TRCA-based spatial filtering from electroencephalogram (EEG) signals through maximizing the reproducibility across trials, may contain some task-related inherent noise that is still trial-reproducible. Approach . To address this problem, this study proposed a similarity-constrained TRCA (scTRCA) algorithm to remove the task-related noise and extract TRCs maximally correlated with SSVEPs for enhancing SSVEP detection. Similarity constraints, which were created by introducing covariance matrices between EEG training data and an artificial SSVEP template, were added to the objective function of TRCA. Therefore, a better spatial filter was obtained by maximizing not only the reproducibility across trials but also the similarity between TRCs and SSVEPs. The proposed scTRCA was compared with TRCA, multi-stimulus TRCA, and sine–cosine reference signal based on two public datasets. Main results . The performance of TRCA in target identification of SSVEPs is improved by introducing similarity constraints. The proposed scTRCA significantly outperformed the other three methods, and the improvement was more significant especially with insufficient training data. Significance . The proposed scTRCA algorithm is promising for enhancing SSVEP detection considering its better performance and robustness against insufficient calibration.

Objective. Designing an effective classifier with high classification accuracy and strong generalization capability is essential for brain-computer interface (BCI) research. In this study, an extreme learning machine (ELM) based method is proposed to improve the classification accuracy of motor imagery electroencephalogram (EEG). Approach. The proposed method constructs an ensemble classifier based on optimized ELMs. Particle swarm optimization is used to simultaneously optimize the input weights and hidden biases of ELM to avoid the randomness and instability of classification result when ELM uses randomly generated parameters, and majority voting strategy is used to fuse the classification results of multiple base classifiers to avoid the negative impact of ELM with local optimal parameters on classification result. The proposed method was compared with four competing methods in experiments based on two public EEG datasets and some existing methods reported in the literature using the same datasets as well. Main results. The results indicate that the proposed method achieved significant higher classification accuracies than those of the competing methods on both two-class and four-class motor imagery data. Moreover, compared to the existing methods, it still obtained superior average accuracies of two-class classification and performed better for the subjects with relatively poor accuracies on both two-class and four-class classifications. Significance. The significant accuracy improvement demonstrates the superiority of the proposed method. It can be a promising candidate for accurate classification of motor imagery EEG in BCI systems.

Internal defects inside power cable joints due to unqualified construction is the main issue of power cable failures, hence in this paper a method based on thermal probability density function to detect the internal defects of power cable joints is presented. First, the model to calculate the thermal distribution of power cable joints is set up and the thermal distribution is calculated. Then a thermal probability density (TPD)-based method that gives the statistics of isothermal points is presented. The TPD characteristics of normal power cable joints and those with internal defects, including insulation eccentricity and unqualified connection of conductors, are analyzed. The results indicate that TPD differs with the internal state of cable joints. Finally, experiments were conducted in which surface thermal distribution was measured by FLIR SC7000, and the corresponding TPDs are discussed.