Relation of canonical correlation analysis and multivariate synchronization index in SSVEP detection

Effect of 3D paradigm synchronous motion for SSVEP-based hybrid BCI-VR system

A brain-computer interface (BCI) system and virtual reality (VR) are integrated as a more interactive hybrid system (BCI-VR) that allows the user to manipulate the car. A virtual scene in the VR system that is the same as the physical environment is built, and the object's movement can be observed in the VR scene. The four-class three-dimensional (3D) paradigm is designed and moves synchronously in virtual reality. The dynamic paradigm may affect their attention according to the experimenters' feedback. Fifteen subjects in our experiment steered the car according to a specified motion trajectory. According to our online experimental result, different motion trajectories of the paradigm have various effects on the system's performance, and training can mitigate this adverse effect. Moreover, the hybrid system using frequencies between 5 and 10 Hz indicates better performance than those using lower or higher stimulation frequencies. The experiment results show a maximum average accuracy of 0.956 and a maximum information transfer rate (ITR) of 41.033 bits/min. It suggests that a hybrid system provides a high-performance way of brain-computer interaction. This research could encourage more interesting applications involving BCI and VR technologies.

Detecting Trivariate Associations in High-Dimensional Datasets

Detecting correlations in high-dimensional datasets plays an important role in data mining and knowledge discovery. While recent works achieve promising results, detecting multivariable correlations especially trivariate associations still remains a challenge. For example, maximal information coefficient (MIC) introduces generality and equitability to detect bivariate correlations but fails to detect multivariable correlation. To solve the problem mentioned above, we proposed quadratic optimized trivariate information coefficient (QOTIC). Specifically, QOTIC equitably measures dependence among three variables. Our contributions are three-fold: (1) we present a novel quadratic optimization procedure to approach the correlation with high accuracy; (2) QOTIC exceeds existing methods in generality and equitability as QOTIC has general test functions and is applicable in detecting multivariable correlation in datasets of various sample sizes and noise levels; (3) QOTIC achieved both higher accuracy and higher time-efficiency than previous methods. Extensive experiments demonstrate the excellent performance of QOTIC.