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This paper presents a BCI system which addresses the key problems of robust feature extraction, non-stationarity and subject-specific spectral filter selection. It employs the Robust Common Spatial pattern (RoCSP) feature extraction algorithm which eliminates trials affected by artifacts and discards redundant channels to improve the robustness of the CSP algorithm. Next, it handles the non-stationarity...
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...
This paper presents a method for a hands-off noninvasive brain computer interface (BCI) to control the movement of a quadcopter. The quadcopter model used in this paper is the AR.drone 2.0 and the non-invasive BCI device used is the Emotiv EPOC. A framework is developed to convert the raw EEG signals into commands to control the flight of quadcopter, AR.drone 2.0 through a wireless interface. The...
The Common Spatial Pattern (CSP) is an effective algorithm used in EEG based Brain Computer Interface (BCI) to extract discriminative features, however, its effectiveness depends upon the subject-specific frequency bands. Also, the generated features using CSP are non-stationary in nature. In this paper, we propose a Meta-cognitive Interval type-2 Neuro-Fuzzy Inference System to handle non-stationarity...
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