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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...
This paper proposes a novel sequential learning algorithm based on zero-density maximization and extended Kalman filter, together referred to as ZDM-EKF, for a Takagi-Sugeno-Kang fuzzy inference system. The sequential learning begins with zero rules, and rules are added/ pruned or updated based on the knowledge contained in the network and prediction error of the current sample. As each sample is...
In this paper, we propose a Projection Based Learning (PBL) algorithm for a Meta-Cognitive Neuro-Fuzzy Inference (McFIS) together referred to as PBL-McFIS. McFIS consists of a cognitive component, which is a zero-order Takagi-Sugeno-Kang adaptive neuro-fuzzy inference system, and a meta-cognitive component, which is a self-regulatory learning mechanism for the neuro-fuzzy inference system. The learning...
In this paper, we propose a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS) for accurate detection of human actions from video sequences. In this paper, we employ optical flow based features as they can represent information from local pixel level to global object level between two consecutive image planes. The functional relationship between these optical flow based features and action classes...
In this paper, we propose a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS) for recognition of emotions from facial features. Local binary patterns have been proven to effectively describe the statistical characteristics of face image as it contains information related to edges, spots, etc. The aim of McFIS is to approximate the functional relationship between the facial features and various emotions...
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