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Electroencephalographic (EEG) activity of brain function could provide a new non-muscular channel for giving messages and commands to the external world is known as brain computer interface (BCI). In a BCI system, humans can control many devices by using his or her imaginations. Today with the provision of affordable electronic systems and powerful processing tools has eased the bio-signal processing...
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 use of large number of channels in EEG based Motor-imagery Brain Computer Interfaces (BCI) may cause long preparation time and redundancy of data. In this paper, we propose a Cohen's d effect-size based channel selection algorithm which eliminates the redundant channels while improving the classification performance. This method (referred to as Effect-size based CSP (E-CSP)) eliminates the channels...
In this paper, an automatic seizure detection technique using multichannel EEG is proposed based on Metacognitive Complex-valued Interval Type-2 Fuzzy Inference System (McCIT2FIS). A wavelet chaos theory based feature extraction is employed to extract the features from EEG signal as it can handle the non stationarity in data and Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation...
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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