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

Ability of arbuscular mycorrhiza to promote growth of maize plant and enzymatic activity of an alluvial soil

A pot experiment was conducted to evaluate the response of selected species of mycorrhizae for root colonization and phosphorus uptake by maize in an alluvial soil. Of all the species of mycorrhizae taken under consideration, Glomus mosseae was found to perform better in terms of root colonization, number of spores, grain yield and phosphorus uptake. The maximum plant height (28.5 cm), shoot dry weight (19.45 g plant-1) and root dry weight (4.77 g plant-1) was also found with the application of G. mosseae. Its application significantly increased the root dry weight by 99.58 and 72.82% over application of G. intraradices and control respectively, and was at par with the application of G. coronatum and Gigaspora decipiens. Application of G. decipiens reported the highest bacterial (39.11 cfu g-1 soil) and fungal count (30.68 cfu g-1 soil) that was found to be at par with application of G. mosseae. Application of G. mosseae significantly increased the actinomycetes population by 44.71 and 55.97% over application of a local mycorrhizal strain and control. Maximum dehydrogenase activity (56.00 g-1 TPF g-1 24 h-1) and acid phosphatase activity (0.299 mg PNP g-1 h-1) and was also observed with application of G. mosseae, which in turn resulted in higher yield which was 27.28%, 28.52%, 9.35 and 11.7% more than G. intraradices, G. coronatum, G. decipiens and the local species respectively. G. mosseae inoculation proved to be effective in modifying the soil microbe population and community structure and also in enhancing the soil enzymatic activities and phosphorus uptake of the crop

A Hybrid Brain-Computer Interface for Closed- Loop Position Control of a Robot Arm

Brain-Computer Interfacing has currently added a new dimension in assistive robotics. Existing brain-computer interfaces designed for position control applications suffer from two fundamental limitations. First, most of the existing schemes employ open-loop control, and thus are unable to track the positional errors, resulting in failures in taking necessary online corrective actions. There are traces of one or fewer works dealing with closed-loop EEG-based position control. The existing closed-loop brain-induced position control schemes employ a fixed order link selection rule, which often creates a bottleneck for time-efficient control. Second, the existing brain-induced position controllers are designed to generate the position response like a traditional first-order system, resulting in a large steady-state error. This paper overcomes the above two limitations by keeping provisions for (Steady-State Visual Evoked Potential induced) link-selection in an arbitrary order as required for efficient control and also to generate a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors. Besides the above, the third novelty is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin by speed reversal at the zero-crossings of positional errors. Experiments undertaken reveal that the steady-state error is reduced to 0.2%. The paper also provides a thorough analysis of stability of the closed-loop system performance using Root Locus technique

Hemodynamic Analysis for Olfactory Perceptual Degradation Assessment Using Generalized Type-2 Fuzzy Regression

Olfactory perceptual degradation refers to the inability of people to recognize the variation in concentration levels of olfactory stimuli. The paper attempts to assess the degree of olfactory perceptual degradation of subjects from their hemodynamic response to olfactory stimuli. This is done in 2 phases. In the first (training) phase, a regression model is developed to assess the degree of concentration levels of an olfactory stimulus by a subject from her hemodynamic response to the stimulus. In the second (test) phase, the model is employed to predict the possible concentration level experienced by the subject in [0, 100] scale. The difference between the model-predicted response and the oral response (the center value of the qualitative grades) of the subject about her perceived concentration level is regarded as the quantitative measure of the degree of subject's olfactory degradation. The novelty of the present research lies in the design of a General Type-2 fuzzy regression model, which is capable of handling uncertainty due to the presence of intra- and inter-session variations in the brain responses to olfactory stimuli. The attractive feature of the paper lies in adaptive tuning of secondary membership functions to reduce model prediction error in an evolutionary optimization setting. The effect of such adaptation in secondary measures is utilized to adjust the corresponding primary memberships in order to reduce the uncertainty involved in the regression process. The proposed regression model has good prediction accuracy and high time-efficiency as evident from average percentage success rate (PSR) and run-time complexity analysis respectively. The Friedman test undertaken also confirms the superior performance of the proposed technique with other competitive techniques at 95% confidence level

Mimicking Short-Term Memory in Shape-Reconstruction Task Using an EEG-Induced Type-2 Fuzzy Deep Brain Learning Network

The paper attempts to model short-term memory (STM) for shape-reconstruction tasks by employing a 4-stage deep brain leaning network (DBLN), where the first 2 stages are built with Hebbian learning and the last 2 stages with Type-2 Fuzzy logic. The model is trained stage-wise independently with visual stimulus of the object-geometry as the input of the first stage, EEG acquired from different cortical regions as input and output of respective intermediate stages, and recalled object-geometry as the output of the last stage. Two error feedback loops are employed to train the proposed DBLN. The inner loop adapts the weights of the STM based on a measure of error in model-predicted response with respect to the object-shape recalled by the subject. The outer loop adapts the weights of the iconic (visual) memory based on a measure of error of the model predicted response with respect to the desired object-shape. In the test phase, the DBLN model reproduces the recalled object shape from the given input object geometry. The motivation of the paper is to test the consistency in STM encoding (in terms of similarity in network weights) for repeated visual stimulation with the same geometric object. Experiments undertaken on healthy subjects, yield high similarity in network weights, whereas patients with pre-frontal lobe Amnesia yield significant discrepancy in the trained weights for any two trials with the same training object. This justifies the importance of the proposed DBLN model in automated diagnosis of patients with learning difficulty. The novelty of the paper lies in the overall design of the DBLN model with special emphasis to the last 2 stages of the network, built with vertical slice based type-2 fuzzy logic, to handle uncertainty in function approximation (with noisy EEG data). The proposed technique outperforms the state-of-the-art functional mapping algorithms with respect to the (pre-defined outer loop) error metric, computational complexity and runtime

Hemodynamic Analysis for Cognitive Load Assessment and Classification in Motor Learning Tasks Using Type-2 Fuzzy Sets

The paper addresses a novel approach to assess and classify the cognitive load of subjects from their hemodynamic response while engaged in motor learning tasks, such as vehicle-driving. A set of complex motor-activity-learning stimuli for braking, steering-control and acceleration is prepared to experimentally measure and classify the cognitive load of the car-drivers in three distinct classes: High, Medium and Low. New models of General and Interval Type-2 Fuzzy classifiers are proposed to reduce the scope of uncertainty in cognitive load classification due to the fluctuation of the hemodynamic features within and across sessions. The proposed classifiers offer high classification accuracy over 96%, leaving behind the traditional type-1/type-2 fuzzy and other standard classifiers. Experiments undertaken also offer a deep biological insight concerning the shift of brain-activations from the orbito-frontal to the ventro-lateral prefrontal cortex during high-to-low transition in cognitive load. Further, the activation of the dorsolateral prefrontal cortex is also reduced during low cognitive load of subjects. The proposed research outcome may directly be utilized to identify driving learners with low cognitive load for difficult motor learning tasks, such as taking a U-turn in a narrow space and motion control on the top of a bridge to avoid possible collision with the car ahead