Generating a fuzzy rule-based brain-state-drift detector by riemann-metric-based clustering

Abstract

Brain-state drifts could significantly impact on the performance of machine-learning algorithms in brain computer interface (BCI). However, less is understood with regard to how brain transition states influence a model and how it can be represented for a system. Herein we are interested in the hidden information of brain state-drift occurring in both simulated and real-world human-system interaction. This research introduced the Riemann metric to categorize EEG data, and visualized the clustering result so that the distribution of the data can be observable. Moreover, to defeat subjective uncertainty of electroencephalography (EEG) signals, fuzzy theory was employed. In this study, we built a fuzzy rule-based brain-statedrift detector to observe the brain state and imported data from different subjects to testify the performance. The result of the detection is acceptable and shown in this paper. In the future, we expect that brain-state drifting can be connected with human behaviors via the proposed fuzzy rule-based classification. We also will develop a new structure for a fuzzy rule-based brain-statedrift detector to improve the detection accuracy.

EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

“…By integrating fuzzy sets with EEG-based BCI domain adaptation, Wu et al [85] proposed an online weighted adaptation regularisation for regression (OwARR) algorithm to reduce the amount of subject-specific calibration of the EEG data. Furthermore, by integrating fuzzy rules with domain adaptation, Chang et al [86] generated a fuzzy rule-based brain-state-drift detector using Riemann-metricbased clustering, allowing the data distribution to be observable. By adopting fuzzy integrals [87], a motor-imagerybased BCI exhibited robust performance for offline singletrial classification and real-time control of a robotic arm.…”

EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications

“…By integrating fuzzy sets with domain adaptation, [85] proposed an online weighted adaptation regularization for regression (OwARR) algorithm to reduce the amount of subject-specific calibration EEG data. Furthermore, by integrating fuzzy rules with domain adaptation, [86] generated a fuzzy rule-based brain-state-drift detector by Riemannmetric-based clustering, allowing that the distribution of the data can be observable. By adopting fuzzy integrals [87], motor-imagery-based BCI exhibited robust performance for offline single-trial classification and real-time control of a robotic arm.…”