A fully tuned sequential interval type-2 fuzzy inference system for motor-imagery task classification

Abstract

In this paper, we propose an interval type-2 fuzzy inference system using Extended Kalman Filter based learning algorithm. It is referred to as IT2FIS-EKF. This algorithm realizes the Takagi-Sugeno-Kang inference mechanism in a five layered architecture. It starts with no rules and evolves the structure automatically. The sequential learning algorithm regulates the learning process by selecting appropriate learning strategies to evolve the architecture and estimate the antecedent/consequent parameters. The performance of IT2FIS-EKF is evaluated on a set of benchmark classification problems from the UCI machine learning repository. Results show superior performance of IT2FIS-EKF in comparison to other fuzzy neural networks due fully adaptive nature of learning algorithm. Further, IT2FIS-EKF is applied to a practical problem of classification of motor-imagery tasks in motor-imagery based brain computer interface (BCI). Performance is evaluated using the publicly available BCI competition dataset. Results indicate superior performance of IT2FIS-EKF making it suitable for BCI.