EEG-Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

The paper aims at detecting on-line cognitive failures in driving by decoding the EEG signals acquired during visual alertness, motor-planning and motor-execution phases of the driver. Visual alertness of the driver is detected by classifying the pre-processed EEG signals obtained from his pre-frontal and frontal lobes into two classes: alert and non-alert. Motor-planning performed by the driver using the pre-processed parietal signals is classified into four classes: braking, acceleration, steering control and no operation. Cognitive failures in motor-planning are determined by comparing the classified motor-planning class of the driver with the ground truth class obtained from the co-pilot through a hand-held rotary switch. Lastly, failure in motor execution is detected, when the time-delay between the onset of motor imagination and the EMG response exceeds a predefined duration. The most important aspect of the present research lies in cognitive failure classification during the planning phase. The complexity in subjective plan classification arises due to possible overlap of signal features involved in braking, acceleration and steering control. A specialized interval/general type-2 fuzzy set induced neural classifier is employed to eliminate the uncertainty in classification of motor-planning. Experiments undertaken reveal that the proposed neuro-fuzzy classifier outperforms traditional techniques in presence of external disturbances to the driver. Decoding of visual alertness and motor-execution are performed with kernelized support vector machine classifiers. An analysis reveals that at a driving speed of 64 km/hr, the lead-time is over 600 milliseconds, which offer a safe distance of 10.66 meters

Distance-related Properties of Corona of Certain Graphs

A graph G is called a m−eccentric point graph if each point of G has exactly m ≥ 1 eccentric points. When m = 1, G is called a unique eccentric point (u.e.p) graph. Using the notion of corona of graphs, we show that there exists a m−eccentric point graph for every m ≥ 1. Also, the eccentric graph Ge of a graph G is a graph with the same points as those of G and in which two points u and v are adjacent if and only if either u is an eccentric point of v or v is an eccentric point of u in G. We obtain the structure of the eccentric graph of corona G ◦ H of self-centered or non-self-centered u.e.p graph G with any other graph H and obtain its domination number

On Real-valued Visual Cryptographic Basis Matrices

Visual cryptography (VC) encodes an image into noise-like shares, which can be stacked to reveal a reduced quality version of the original. The problem with encrypting colour images is that they must undergo heavy pre-processing to reduce them to binary, entailing significant quality loss. This paper proposes VC that works directly on intermediate grayscale values per colour channel and demonstrates real-valued basis matrices for this purpose. The resulting stacked shares produce a clearer reconstruction than in binary VC, and to the best of the authors’ knowledge, is the first method posing no restrictions on colour values while maintaining the ability to decrypt with human vision. Grayscale and colour images of differing entropies are encrypted using fuzzy OR and XOR, and their PSNR and structural similarities are compared with binary VC to demonstrate improved quality. It is compared with previous research and its advantages highlighted, notably in high quality reconstructions with minimal processing

Positional Non-Cooperative Equilibrium

This paper presents and analyses a game theoretic model for resource allocation, where agents are status-seeking and consuming positional goods. We propose a unified framework to study the competition for resources where agents’ preferences are not necessarily ordered according to the absolute amount of goods they consume, but may depend on the consumption of others as well as on individual valuation of the goods at stake. Our model explicits the relation between absolute good distribution, individual evaluation and the level of consumption adopted by the opponents; such relation has the form of a status function.We show that given a certain set of properties, there exists only one possible status function. The competition mechanism implemented to maximise one own’s status is central in this work. As a result of the mathematical formulation, we show that the standard utility-maximisation paradigm emerges as a special case (non-positional competition). We then define a new class of games where the individual evaluations are negotiable and serve only the purpose of maximising one own’s status

A Low-Cost Portable Health Platform for the Monitoring of Human Physiological Signals

This work reports the integration and preliminary testing of a miniature commercial health platform based on the combination of a set of platforms that can be merged in hardware and software to measure and monitor many physiological parameters of the human body. The system is very portable, has a clear economic benefit in terms of cost and it has been well integrated with a customized and intuitive graphical user interface. Detailed about the materials used for preparation of this platform and the methods used for data collection are reported. Preliminary data has been collected and reported. Explanations are shown about the data in relation to the sensors behaviors and performance

Stability Analysis of Artificial Bee Colony Optimization Algorithm

Theoretical analysis of swarm intelligence and evolutionary algorithms is relatively less explored area of research. Stability and convergence analysis of swarm intelligence and evolutionary algorithms can help the researchers to fine tune the parameter values. This paper presents the stability analysis of a famous Artificial Bee Colony (ABC) optimization algorithm using von Neumann stability criterion for two-level finite difference scheme. Parameter selection for the ABC algorithm is recommended based on the obtained stability conditions. The findings are also validated through numerical experiments on test problems

Exploration of Subjective Color Perceptual-Ability by EEG-Induced Type-2 Fuzzy Classifiers

Perceptual-ability informally refers to the ability of a person to recognize a stimulus. This paper deals with color perceptual-ability measurement of subjects using brain response to basic color (red, green and blue) stimuli. It also attempts to determine subjective ability to recognize the base colors in presence of noise tolerance of the base colors, referred to as recognition tolerance. Because of intra- and inter-session variations in subjective brain signal features for a given color stimulus, there exists uncertainty in perceptual-ability. In addition, small variations in the color stimulus result in wide variations in brain signal features, introducing uncertainty in perceptual-ability of the subject. Type-2 fuzzy logic has been employed to handle the uncertainty in color perceptual-ability measurements due to a) variations in brain signal features for a given color, and b) the presence of colored noise on the base colors. Because of limited power of uncertainty management of interval type-2 fuzzy sets and high computational overhead of its general type-2 counterpart, we developed a semi-general type-2 fuzzy classifier to recognize the base color. It is important to note that the proposed technique transforms a vertical slice based general type-2 fuzzy set into an equivalent interval type-2 counterpart to reduce the computational overhead, without losing the contributions of the secondary memberships. The proposed semi-general type-2 fuzzy sets induced classifier yields superior performance in classification accuracy with respect to existing type-1, type-2 and other well-known classifiers. The brain-understanding of a perceived base or noisy base colors is also obtained by exact low resolution electromagnetic topographic analysis (e-LORETA) software. This is used as the reference for our experimental results of the semi-general type-2 classifier in color perceptual-ability detection. Statistical tests undertaken confirm the superiority of the proposed classifier over its competitors. The proposed technique is expected to have interesting applications in identifying people with excellent color perceptual-ability for chemical, pharmaceutical and textile industries

Two-Dimensional Digitized Picture Arrays and Parikh Matrices

Parikh matrix mapping or Parikh matrix of a word has been introduced in the literature to count the scattered subwords in the word. Several properties of a Parikh matrix have been extensively investigated. A picture array is a two-dimensional connected digitized rectangular array consisting of a finite number of pixels with each pixel in a cell having a label from a finite alphabet. Here we extend the notion of Parikh matrix of a word to a picture array and associate with it two kinds of Parikh matrices, called row Parikh matrix and column Parikh matrix. Two picture arrays A and B are defined to be M-equivalent if their row Parikh matrices are the same and their column Parikh matrices are the same. This enables to extend the notion of M-ambiguity to a picture array. In the binary and ternary cases, conditions that ensure M-ambiguity are then obtained

Applications of Signed Graphs to Portfolio Turnover Analysis

AbstractPortfolio turnover is an important area for portfolio managers and investors, since it significantly impacts returns through higher trading costs and taxes. Currently, methods for assessing the possibility of portfolio turnover are practically non-existent. Using the concept of signed graphs one can assess the stability of portfolios and thereby the likelihood of portfolio turnover. We demonstrate our method using empirical data from the Indian Stock Exchange and show that portfolios focusing on risk alone can result in higher portfolio turnover, causing misleading portfolio management