Evaluating the performance of the cognitive workload model with subjective endorsement in addition to EEG.

The aptitude-oriented exercises from almost all domains impose cognitive load on their operators. Evaluating such load poses several challenges owing to many factors like measurement mode and complexity, nature of the load, overloading conditions, etc. Nevertheless, the physiological measurement of a specific genre of cognitive load and subjective measurement have not been reported along with each other. In this study, the electroencephalography (EEG)-driven machine learning (Support Vector Machine (SVM)) model is sought along with the support of NASA's Task Load Index (NASA-TLX) rating scale for a novel purpose in workload exploration of operators. The Cognitive Load Theory (CLT) was used as the foundation to design the intrinsic stimulus (Spot the Difference task), as most workloads operators are exposed to are notably intrinsic. The SVM-based three-level classification accuracy ranged from 85.4 to 97.4% (p < 0.05), and the NASA-TLX-based three-level classification accuracy ranged from 88.33 to 97.33%. The t-test results show that the neurometric indices contributing to the classification significantly differed (p < 0.05) for every level. The NASA-TLX scale was utilised for validation in its basic form after the validity (Pearson correlation coefficients 0.338 to 0.805 (p < 0.05)) and reliability (Cronbach's α = 0.753) test. This modeling is beneficial to phase out particular-level cognitive exercises from the curriculum during under or overload workload (critical) conditions.

Towards a versatile mental workload modeling using neurometric indices.

Researchers have been working to magnify mental workload (MWL) modeling for a long time. An important aspect of its modeling is feature selection as it interprets bulky and high-dimensional EEG data and enhances the accuracy of the classification model. In this study, a feature selection technique is proposed to obtain an optimized feature set with multiple domain features that can contribute to classifying the MWL at three distinct levels. The brain signals from thirteen healthy subjects were examined while they attended an intrinsic MWL of spotting differences in a set of similar pictures. The Recursive Feature Elimination (RFE) technique selects the robust features from the feature matrix by eliminating all the least contributing features. Along with the Support Vector Machine (SVM), the overall classification accuracy with the proposed RFE reached 0.913 from 0.791 surpassing the other techniques mentioned. The results of the study also significantly display the variation in the mean values of the selected features at the three workload levels (p<0.05). This model can become the principle for defining the workload level quantification applicable to diverse fields like neuroergonomics study, intelligent assistive devices (ADs) development, blue-chip technology exploration, cognitive evaluation of students, power plant operators, traffic operators, etc.

A comparative study of classification methods for designing a pictorial P300-based authentication system.

The response of the P300-based speller is associated with factors like matrix size, inter-stimulus interval, and flashing period. This study proposes the comparison of the novel 2 × 2 image-based speller with the traditional 6 × 6 character-based speller to analyze the effects of the stimulus on the accuracy and information transfer rates. To determine the best classification methodology for the approach suggested, a comparative study was performed using linear and quadratic discrimination analysis, K-nearest neighbor, and support vector machine. In the proposed paradigm, four pictures (objects, special symbols, geometrical shapes, and colors) were randomly placed at four corners of the monitor. Subjects were asked to focus on the target image while ignoring all other images. The proposed method outperformed the traditional method, with an average accuracy of 96.99 ± 1.64% and 86.74 ± 5.19%, respectively, and information transfer rates of 33.82 ± 0.57 bits/min and 23.35 ± 0.79 bits/min, respectively. Results show that a modified speller can play a significant role in optimizing brain-computer interface-driven applications. A repeated measure ANOVA test was performed, which concluded that the improved results are obtained using QDA classifiers in terms of mean accuracy, speed, and error rates.

A novel approach for designing authentication system using a picture based P300 speller.

Due to great advances in the field of information technology, the need for a more reliable authentication system has been growing rapidly for protecting the individual or organizational assets from online frauds. In the past, many authentication techniques have been proposed like password and tokens but these techniques suffer from many shortcomings such as offline attacks (guessing) and theft. To overcome these shortcomings, in this paper brain signal based authentication system is proposed. A Brain-Computer Interface (BCI) is a tool that provides direct human-computer interaction by analyzing brain signals. In this study, a person authentication approach that can effectively recognize users by generating unique brain signal features in response to pictures of different objects is presented. This study focuses on a P300 BCI for authentication system design. Also, three classifiers were tested: Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor, and Quadratic Support Vector Machine. The results showed that the proposed visual stimuli with pictures as selection attributes obtained significantly higher classification accuracies (97%) and information transfer rates (37.14 bits/min) as compared to the conventional paradigm. The best performance was observed with the QDA as compare to other classifiers. This method is advantageous for developing brain signal based authentication application as it cannot be forged (like Shoulder surfing) and can still be used for disabled users with a brain in good running condition. The results show that reduced matrix size and modified visual stimulus typically affects the accuracy and communication speed of P300 BCI performance.

EEG-based hybrid QWERTY mental speller with high information transfer rate.

BACKGROUND: Brain-computer interface (BCI) spellers detect variations in brain waves to help subjects communicate with the world. This study introduces a P300-SSVEP hybrid BCI-based QWERTY speller. METHODS: The proposed hybrid speller, combines SSVEP and P300 features using a hybrid paradigm. P300 was used as time division multiplexing index which results in the use of lesser number of assumed frequencies for SSVEP elicitation. Each flickering frequency was also assigned a unique colour, to enhance system accuracy. RESULTS: On the basis of 20 subjects, an average accuracy of classification of 96.42% and a mean information transfer rate (ITR) of 131.0 bits per min. (BPM) was achieved during the free spelling trial (trial-F). COMPARISON: The t test results revealed that the hybrid QWERTY speller performed significantly better (on the basis of mean classification accuracy and ITR) as compared to the traditional P300 speller) and the QWERTY SSVEP speller. Also, the amount of time taken to spell a word was significantly lesser in the case of hybrid QWERTY speller in contrast to traditional P300 speller while it was almost the same as compared to QWERTY SSVEP speller. CONCLUSION: QWERTY speller outperformed the stereotypical P300 speller as well as QWERTY SSVEP speller.

Efficacy of Bacopa Monnieri (Brahmi) and Donepezil in Alzheimer's Disease and Mild Cognitive Impairment: A Randomized Double-Blind Parallel Phase 2b Study.

OBJECTIVES: Alzheimer's disease (AD) is the most common cause of dementia worldwide in the older population. There is no disease-modifying therapy available for AD. The current standard of care drug therapy for AD is cholinesterase inhibitors, including donepezil. Bacopa monnieri or brahmi is used in traditional Indian medicine for memory loss. We conducted a phase 2b randomized controlled trial (RCT) to find out the efficacy of brahmi and donepezil in AD and mild cognitive impairment (MCI). PATIENTS AND METHODS: The study was planned as a 52 week, randomized, double-blind, parallel-group, phase-2 single-center clinical trial comparing the efficacy and safety of Bacopa monnieri (brahmi) 300 mg OD and donepezil 10 mg OD for 12 months in 48 patients with AD and MCI-AD including cognitive and quality of life outcomes. The primary outcome was differences in the change from baseline of the neuropsychological tests [Alzheimer's disease assessment scale-cognitive subscale (ADAS-Cog) and postgraduate institute (PGI) memory scale] at 12 months between the intervention group (brahmi) and active comparison group (donepezil). RESULTS: The study was terminated after 3 years and 9 months, after recruiting 34 patients, because of slow recruitment and a high dropout rate. Intention to treat analysis after adjusting for baseline confounders showed no difference in the rate of change in ADAS-Cog score from baseline at any time point, including the last follow-up. There was no difference in the rate of change in PGI Memory scale (PGIMS) at 3, 6, and 9 months. In the last follow-up, there was a significant difference in the change in total PGIMS score between brahmi and donepezil, while there was no difference in individual scores of the PGI memory scale. CONCLUSION: This phase-2 RCT on the efficacy of brahmi vs. donepezil showed no significant difference between them after 1 year of treatment. Larger phase-3 trials, preferably multicentric, are required to find the superiority of brahmi over donepezil.

Foreign Body causing Displacement of Immature Fractured Apical Root Fragment: An Unusual Case Report.

Trauma is a common cause of fractured teeth with exposed canals in growing children. These children use foreign bodies like stapler pin, lead pencil, nail, etc., to explore the canal of fractured tooth. Sometimes, these foreign objects may get stuck in the canal, which the children do not reveal to their parents because of fear. These foreign objects may act as a potential source of infection. We herewith present a case of a 12-year-old boy who presented with a stick lodged in the root canal of maxillary right lateral incisor along with the displaced fractured tooth segment at the apex and the associated management. How to cite this article: Moda A, Singla R, Agrawal PM. Foreign Body causing Displacement of Immature Fractured Apical Root Fragment: An Unusual Case Report. Int J Clin Pediatr Dent 2018;11(3):247-249.

Influence of stimuli colour in SSVEP-based BCI wheelchair control using support vector machines.

This study aims to develop a Steady State Visual Evoked Potential (SSVEP)-based Brain Computer Interface (BCI) system to control a wheelchair, with improving accuracy as the major goal. The developed wheelchair can move in forward, backward, left, right and stop positions. Four different flickering frequencies in the low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using LabVIEW. Four colours (green, red, blue and violet) were included in the study to investigate the colour influence in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital region were first segmented into 1 s windows and features were extracted by using Fast Fourier Transform (FFT). Three different classifiers, two based on Artificial Neural Network (ANN) and one based on Support Vector Machine (SVM), were compared to yield better accuracy. Twenty subjects participated in the experiment and the accuracy was calculated by considering the number of correct detections produced while performing a pre-defined movement sequence. SSVEP with violet colour showed higher performance than green and red. The One-Against-All (OAA) based multi-class SVM classifier showed better accuracy than the ANN classifiers. The classification accuracy over 20 subjects varies between 75-100%, while information transfer rates (ITR) varies from 12.13-27 bpm for BCI wheelchair control with SSVEPs elicited by violet colour stimuli and classified using OAA-SVM.

Single Visit versus Multiple Visit Root Canal Therapy.

AIM: The purpose of this study was to determine clinical success rate of single visit verses multiple visit root canal treatment in cariously exposed vital primary molars. Material& methods: 40 children in age group of 4 to 7 years were divided equally into two treatment groups and recall visits were carried out after one week, one month and three months and six months. RESULTS: Statistically no significant difference was found. CONCLUSION: Multiple visit and single visit root canal treatment demonstrated almost equal success but most important aspect for success in pulpectomy cases is the indication of each case and then its subsequent treatment, be it multiple or single visit root canal treatment.