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This report examines the work of 66 community groups organizing to improve public education in low-performing schools and districts. These largely local, community-based organizations focus on engaging public school parents, low-income families, and students in school improvement efforts. Their goals are to build political power and to challenge school systems that serve children poorly. This report describes the diversity of methods and approaches groups are using and reports groups' organizing achievements and challenges. Results find that school reform organizing plays a significant role in creating the political context in which change can happen. Organizing groups focus schools on critical issues, identify and build support for key interventions, and establish a stronger sense of accountability between schools and communities. They are increasing the ability of young people, parents, and community residents to participate in local reform efforts, and they are helping members to raise essential school performance questions forcefully and persistently. The report discusses implications for educators and offers recommendations for funders (e.g., the need to develop greater capacity in the organizing groups, create and strengthen intermediary or "support" organizations that provide technical help such as data analysis, and develop better ways to measure the impact of organizing). An appendix describes the methodology used and includes contact information for the organizations profiled in the study. (SM)

Ipomoea hederifolia Linn. is a medicinal herb belonging to the family Convolvulaceae. Ipomoea is derived from the Greek word ips and homois meaning worm like, referring to the vining habit. The Latin word hederifolia means having leaves like Ivy. It is commonly known as scarlet morning glory, scarlet creeper. Ipomoea hederifolia has been reported to possess oxytoxic, anti-cancer, anti-psychotic, antiinflammatory, anti-oxidant and anti-microbial properties according to indigenous systems of medicine in India. This paper reviews the important biological activity of this plant especially its antimicrobial activity.

Electroencephalogram (EEG) based brain–computer interface (BCI) is an augmented communication modality between the brain and computer that exclusively depends on noninvasively recorded neuro-electrical activity. To establish multiple control commands in BCI systems, it is essential to efficiently identify unique EEG activation patterns underlying multiple motor tasks. This article investigates the brain activation modulations elicited by imagined movement directions and the EEG features that characterize them. An experiment was conducted in which ten subjects imagined two-dimensional right-hand movement in the left, right, up, and down directions in the vertical plane. The frequency and phase characteristics of EEG rhythms are explored to identify the features that discriminate movement imagination toward multiple directions. The proposed decoding step consists of Fisher's analysis of instantaneous phase values to identify EEG channels with optimal discriminative information followed by extraction of phase-locking values and temporal-spatial features, which are then used for classification. The proposed approach offers significantly higher (p < 0.05) performance than the state-of-the-art method while employing subject-specific time segments and channels. Hence, an average accuracy of 69.25% is obtained for binary classification of all pairs of movement directions with a performance increment of 8.33% compared to the conventional approaches using fixed channel-time paradigm. The results illustrate the potential use of noninvasive EEG signals in decoding imagined movement kinematics, and further research is needed to investigate and optimize the separability in multiple directions. IEEE

ObjectivesVeterans Health Administration (VHA) launched a national initiative to train providers in a specific, protocolized auricular acupuncture treatment (also called Battlefield Acupuncture or BFA) as a nonpharmacological approach to pain management. This evaluation assessed the real-world effectiveness of BFA on immediate pain relief and identified subgroups of patients for whom BFA is most effective.Research designIn a cross-sectional cohort study, electronic medical record data for 11,406 Veterans treated with BFA at 57 VHA medical centers between October 2016 and September 2018 was analyzed. The multivariate analysis incorporated data on pain history, change in pain level on an 11-point scale, complications, and demographic information.MethodsA total of 11,406 Veterans were treated with BFA at 57 VHA medical centers between October 2016 and September 2018 and had effectiveness data recorded in their electronic medical record.ResultsMore than 3 quarters experienced immediate decreases in pain following administration of BFA, with nearly 60% reported experiencing a minimal clinically important difference in pain intensity. The average decrease in pain intensity was -2.5 points (SD=2.2) at the initial BFA treatment, and -2.2 points (SD=2.0) at subsequent treatments. BFA was effective across a wide range of Veterans with many having preexisting chronic pain, or physical, or psychological comorbid conditions. Veterans with opioid use in the year before BFA experienced less improvement, with pain intensity scores improving more among Veterans who had not recently used opioids.ConclusionVHA's rapid expansion of training providers to offer BFA as a nonpharmacological approach to pain management has benefited many Veterans.

Brain-computer interface (BCI) is relatively a new approach to communication between man and machine, which translates brain activity into commands for communication and control. As BCI is capable of detecting human intentions, it is a promising communication tool for paralyzed patients for communicating with external world. Many of the current BCI systems employ electroencephalogram (EEG) which is the most widely used noninvasive brain activity recording technique. EEG signal carries potential features to identify and decode human intentions and mental tasks. Recently, many researchers have started exploiting the possibilities of BCI in entertainment and cognitive skill enhancement. BCI-based games have been identified as a unique entertainment mechanism nowadays, “controlling a 2-D, 3-D or virtual computer game solely by player's brain waves.” BCI games work based on a neurofeedback paradigm which allows an individual to self-regulate his brain signal in response to the real-time visual or auditory feedback of his brain waves/features. This neurofeedback in a gaming environment motivates and trains the players to control his brain features toward the desired stage (self-regulation). This chapter explores the state-of-the-art BCI technology in neurofeedback games, employing EEG signal. It also provides a survey of the existing EEG-based neurofeedback games and evaluates their success rates, challenging factors and influence on players. In neurofeedback games, a number of features extracted from EEG accompanied with sustained attention, selective attention, visuospatial attention, motor imagery, eye movements, etc. have been employed as distinct control signals. We will briefly review and compare various signal processing methodologies and machine-learning techniques employed in those studies to extract and decode the brain features. Besides the structure and algorithms used in neurofeedback games, the therapeutic effects of neurofeedback training and its capabiliti

Electroencephalogram (EEG)-based BrainComputer Interface (BCI) technology needs efficient algorithms to find distinct EEG patterns/features to realize applications with distinct high-dimensional control signals. This paper proposes a novel feature extraction methodology for separating EEG patterns associated right hand motor imagery performed towards left and right directions. The most discriminative subject-specific feature set is chosen based on Fisher's ratio of absolute phase values of EEG in 6 low frequency sub bands. Using this, the proposed BCI system is capable of providing better classification results than state-ofthe-art methodology with fixed channels, fusing absolute phase and spatial features from selected subject-specific discriminative channels. Experimental analysis shows that though parietal lobe is vital in providing distinguishable features, the channel set that provide maximum accuracy, is highly subject-specific. Hence, subject-specific BCI that can decode finer parameters of imagined movement are feasible and further research to understand the activations elicited in parietal lobe can contribute towards robust BCI systems. © 2019 IEEE.

Chilli is an important spice which is grown throughout India. Chillies are integral and the most important ingredient in many different cuisines around the world as it adds pungency, taste, flavour and color to the dishes. Chilli is grown in Kolathur block of Salem district in an area of nearly 879 ha. The farmers are mainly growing the local varieties and private hybrids in kolathur block. During the farmers and scientist conference conducted at KVK, Sandhiyur (2013), the growers opted for new varieties (high yield, lengthy fruit, good pungency, and colour retention during storage). An onfarm trial was conducted in pannavadi village of kolathur block. In this study three varieties (Lalima, LCA 625 and Kovilpatti 2) were assessed for yield, pest disease tolerance and quality parameters. LCA 625 gave an average yield of 6.2-6.8 t / ha, fruit length of 9-11 cm, good pungency and good colour retention during storage compared to other two varieties. The colour of dry chilli during storage was orange compared to Lalima with bright attractive red colour. Hence in the market Lalima fetched more price than the other two varieties. Hence, it is suggested for the Researchers that LCA 625 may be refined for marketable colour.

Electroencephalogram (EEG) is one the most commonly used brain activity measurement tools in Brain-Computer Interface (BCI) framework to establish 'thought' based human-machine interaction. On account of the fact that mental rehearsal of motor movement termed motor imagery, is a robust candidate for developing BCI systems, it is of great importance to find the distinction between the neural correlates of executed motor movement and its imagination, to enhance command signal generation and utilization in BCI-based communication and control. This paper investigates the EEG-based parameters associated with execution and imagination of left and right hand movement from a set of 10 healthy subjects. The study explores the distinguishable power and phase features in theta (4-8 Hz), alpha/mu (8-12 Hz), lower beta (12-20 Hz), higher beta (20-30 Hz) and gamma (30-40 Hz) bands of EEG. It is observed that the most informative channels that effectively discriminates motor execution and imagination varies across bands and tasks. Frontal and parietal channels are found to be more discriminative compared to those in central region in both tasks. Combining the task-specific discriminative features along with Common Spatial Pattern (CSP) based spatial features, an average classification accuracy of 81% is achieved in 5 \times 5-fold cross validation, over 10 subjects. Further study is essential to investigate the temporal structure of the distinguishable features, to generalize the discriminative spectral and spatial characteristics and to improve the classification accuracy. © 2018 IEEE.

Neural engineering research is actively engaged in optimizing the robustness of sensorimotor rhythms (SMR) - brain-computer interface (BCI) to boost its potential real-world use. Objective. This paper investigates two vital factors in efficient and robust SMR-BCI design - algorithms that address subject-variability of optimal features and neurophysiological factors that correlate with BCI performance. Existing SMR-BCI research using electroencephalogram (EEG) to classify bilateral motor imagery (MI) focus on identifying subject-specific frequency bands with most discriminative motor patterns localized to sensorimotor region. Approach. A novel strategy to further optimize BCI performance by taking into account the variability of discriminative spectral regions across various EEG channels is proposed in this paper. Main results. The proposed technique results in a significant () increase in average () classification accuracy by accompanied by a considerable reduction in number of channels and bands. The session-to-session transfer variation in spectro-spatial patterns using proposed algorithm is investigated offline and classification performance of the optimized BCI model is successfully evaluated in an online SMR-BCI. Further, the effective prediction of SMR-BCI performance with physiological indicators derived from multi-channel resting-state EEG is demonstrated. The results indicate that the resting state activation patterns such as entropy and gamma power from pre-motor (fronto-central) and posterior (parietal and centro-parietal) areas, and beta power from posterior (centro-parietal) areas estimate BCI performance with minimum error. These patterns, strongly related to BCI performance, may represent certain cognitive states during rest. Significance. Findings reported in this paper imply the need for subject-specific modelling of BCI and the prediction of BCI performance using multi-channel rest-state parameters, to ensure enhanced BCI performance. © 2018 IOP Pu

The research to identify the relationship between neural activity underlying motor tasks such as motor imagery, execution, planning and observation has been of significant interest, since it is an important aspect in improving our understanding of neuromotor networks. Neuroimaging literature suggests that the central mechanism in all these tasks is the activation of the primary motor areas of the brain. To study correlates of these tasks, the researchers often employ neural data recorded from the subjects as they perform motor tasks using their dominant hand. In this study, we investigate the distinction between EEG spectral features during unilateral right and unilateral left, imagined and executed hand motor tasks. The analysis is conducted for the data from each hand separately, with the objective to identify whether the EEG activity that distinguished unilateral motor tasks is distinct for each hand. Further, a novel approach of selection of multichannel EEG spectral features is proposed and supervised classification based on support vector machines is employed to classify unilateral motor imagination and motor execution tasks using the selected features. Grand averages of spectral features, over 10 subjects, are studied and it is observed that EEG activity during unilateral motor tasks are both spatially localized and lateralized. Using the proposed pattern recognition model, accuracies of 82.4 % and 83.1 % with a reduction in 15-20 % features for right and left unilateral motor tasks respectively. © 2018 IEEE.

A dynamic power channel executed with a four-leg voltage-source inverter utilizing a prescient control conspire is exhibited. The utilization of a four-leg voltage-source inverter permits the remuneration of current consonant parts, and in addition lopsided current created by single-stage nonlinear burdens. A point by point yet basic numerical model of the dynamic power channel, including the impact of the proportional power framework impedance, is determined and used to outline the prescient control calculation. A fluffy controller is intended to relieve the aggregate symphonious contortion. The pay execution of the proposed dynamic power channel and the related control plot under relentless state and transient working conditions is shown through recreations utilizing MATLAB/SIMULINK condition.

Objective: Battlefield Acupuncture (BFA) is an auricular needling protocol for pain. More than 1300 Veterans Health Administration (VHA) clinicians have been trained in BFA delivery. However, little is known about how well BFA has been implemented at the VHA. The aim of this research was to identify the challenges providers experience in implementing BFA and to look for any successful strategies used to overcome these challenges. Materials and Methods: Semistructured telephone interviews were conducted from June 2017 to January 2018, using an interview guide informed by the integrated Promoting Action on Research Implementation in Health Services framework to address several implementation domains: knowledge and attitudes about BFA; professional roles and training in BFA; organization of BFA delivery and resources to provide BFA; and implementation challenges and strategies to address challenges. The interviews were analyzed, using a grounded theory-informed approach. This research was conducted at 20 VHA facilities and involved 23 VHA BFA providers nationwide. Results: Nine main implementation themes were identified: (1) providers organizing BFA delivery in various ways; (2) insufficient time to provide BFA to meet patient demand; (3) beliefs and knowledge about BFA; (4) lack of BFA indication guidelines or effectiveness data; (5) self-efficacy; (6) time delay between training and practice; (7) limited access to resources; (8) key role of leadership and administrative buy-in, and (9) written consent an unwarranted documentation burden. Providers offered some possible strategies to address these issues. Conclusions: System- and provider-level challenges can impede BFA implementation. However, several providers discovered strategies to address some challenges that can be used within and outside the VHA, which, in turn, might improve access to this potentially promising pain-management intervention.

Recognition of individuals based on their unique physiological or behavioral characteristics, called biometric identification or authentication, has received much attention by researchers during the last decade. Among the existing biometric traits, brain signaling has emerged as a powerful candidate due to its highly unique nature, which makes it impossible to steal or mimic. Electroencephalography (EEG), a popular, noninvasive method used for recording brain activity, has recently been identified as an efficient technique to develop biometric systems due to its simplicity, portability, and relatively low cost in comparison to other noninvasive brain signal acquisition models. EEG signals are captured during a relaxed rest state from an individual whose eyes can be closed or open and who can also experience active response states. These signals have been reported to be robust carriers of unique personality traits.

Biometric recognition of persons based on unique features extracted from brain signals is an emerging area of research nowadays, on account of the subject-specificity of human neural activity. This paper proposes an online Electroencephalogram (EEG) based biometric authentication system using band power features extracted from alpha, beta and gamma bands, when the subject is in relaxed rest state with eyes open or closed. The most distinct band features are chosen specifically for each subject which are then used to generate subject-specific template during enrollment. During online authentication, recorded test EEG pattern is matched with the respective template stored in the database and degree of matching in terms of its correlation coefficient predicts the genuineness of the claimant. A number of client and imposter authentication tests have been conducted in online framework among 6 subjects using the proposed system, and achieves an average recognition rate of 88.33% using 14 EEG channels. Experimental analysis shows the subject-specificity of distinct bands and features, and highlights the utility of subjectspecific band power features in EEG-based biometric systems. © 2017 ACM.

Biometric recognition of persons are widely explored nowadays to develop robust and trustworthy security systems. On account of the unique neural signature of each person, the brain activity recorded by Electroencephalogram (EEG) has recently been identified as a potential biometric trait. In this paper, we propose an online EEG-based biometric system which utilizes the activations of brain towards a set of subjectspecific self-referential visual stimuli. The stimuli set consist of a number of self-face images and that of subject's relatives, which are expected to elicit brain activity in a subject-specific manner. The subject-specific biometric marker used in the proposed authentication system is the relative spectral activity of left and right hemisphere EEG in various frequency bands. Experimental analysis reveals utility of alpha and beta bands for the proposed approach, offering an average biometric recognition accuracy of 87.50% over 4 subjects. Results of the proposed methodology give insights for further research to examine the permanence and generalization of the proposed system in a larger group of subjects. © 2017 IEEE.

Brain-Computer Interface (BCI) technology provides a new mode of direct communication between man and machine, replacing human's normal output control pathways of nerves and muscles. It embeds huge potential to be explored in medicine, rehabilitation and entertainment that can be used for the healthy as well as the disabled. In order to thoroughly exploit BCI's capabilities, efficient man-machine interaction has to be established by accurately identifying human intentions from brain waves. Electroencephalogram (EEG) is a strong tool in BCI research as it is a cheap and easy non-invasive recording methodology of brain activity encoded with human thoughts. This paper investigates an EEG dataset recorded from 85 healthy subjects performing imagination of motor movements, and highlights the necessity of selecting the subject-specific spatial and spectral features to optimize motor imagery recognition performance. Subject-specific spectral and spatial features associated with right and left hand motor imagery are identified on account of the discriminative weights of EEG signals recorded from motor cortex region. Discriminative capability has been estimated using the Fisher ratio values of each frequency component for each channel. Using the proposed hybrid subject-specific selection of channels and bands, the proposed BCI system is capable of offering comparable classification accuracy with the state of the art methodology which employs more number of channels and frequency bands. © 2017 IEEE.

Brain activities are inherently determined by a person's unique pattern of neural pathways and are closely associated with his/her genetic personality traits. Brain activity recorded by electroencephalogram (EEG), has recently been regarded as potential candidate in future generation biometric systems. In this paper, a biometric identification system is proposed, combining subject-specific alpha peak frequency, peak power and delta band power values to form discriminative feature vectors and templates. A public dataset of EEG signals recorded from 109 healthy subjects during eyes open/closed (EO/EC) relaxed rest states, has been analyzed here. Using simple similarity measurements based on correlation and distance measures of the test EEG sequences from the template vectors, an average recognition rate of up to 90 % is achieved, by combining spectral features from delta and alpha bands extracted from selected 19 EEG channels. Experimental results explicitly show the usefulness of combining subject-specific alpha and delta bands in future biometric recognition systems. Further investigation is essential to precisely analyze the system and to improve recognition accuracy. © 2016 IEEE.

Biometric recognition of persons using brain waves has been identified as an attractive topic of research today. Existing popular biometric modalities of face, finger prints and voice signals are vulnerable to various kinds of attacks and spoofing techniques, whereas the emerging biometric trait extracted from brain wave is expected to act as an ideal biometric feature offering high degree of uniqueness, stability and universality. This paper analyses the efficacy of the complexity of Electroencephalogram (EEG) signals recorded during rest state for recognizing individuals from a publicly available EEG dataset consisting of 109 subjects. Sample entropy features extracted from delta, theta, alpha, beta and gamma bands of 64 channel EEG have been evaluated for subject-identification in the proposed system. It is found that beta band entropy has the highest inter-subject variability. Based on a Mahalanobis distance based classifier, beta entropy gives an average correct recognition rate of 98.31%. It has also been observed that concatenation of entropy features with power spectral density (PSD) values improves the system performance. Further analysis is essential to investigate the stability of results over time and to optimize the recognition performance at a reduced number of channels. © 2016 IEEE.

Multi-disciplinary study of human computer interaction has provided significant impact in the fields of neural engineering, cognitive neuroscience, rehabilitation and brain-computer interaction. This paper evaluates the impact of neurofeedback in the context of a simple computer game controlled by attention based brain signals. The designed game protocol requires the player to memorize a set of numbers displayed in a matrix format, and to correctly fill the matrix using his attention based brain patterns. Attention level of the player, quantified using sample entropy values of Electroencephalogram (EEG) signals, is the core control parameter of the game. A comparative study using a single neurofeedback group and 2 control groups (each group consists of 8 subjects) has been carried out to examine the impact of neurofeedback on enhancing attention score and cognitive skill in the context of the attention-driven game. Experimental results explicitly demonstrate the significance and usefulness of neurofeedback in EEG based games. © 2016 IEEE.

Biometric recognition of individuals has been widely employed in establishing secure and trustworthy systems nowadays. However, due to its increased demand and usability, the associated security risks have been increased, which necessitates finding of more robust biometric traits than existing modalities. Recently, brain signal recorded by Electroencephalogram (EEG) technique has been reported as a potential biometric candidate on account of its high degree of uniqueness, stability and universality. This paper presents an EEG-based biometric authentication system employing brain patterns in response to a number of visual or auditory stimuli by seeing/hearing self, familiar and unfamiliar faces/voices. The system employs power spectral density (PSD) features extracted from alpha, beta and gamma bands of EEG for biometric authentication. The PSD values of multi-band EEG signals from 14 channels form template feature vector for each subject, and are stored in the database during enrollment phase. During online authentication, test feature vector is correlated with the respective template vector and the obtained correlation value is compared with a pre-defined threshold value. Based on the authentication experiments performed on 5 healthy subjects, the proposed system offers an overall accuracy of 79.73% with a false acceptance rate (FAR) of 13.91% and false rejection rate (FRR) of 26.6%. © 2016 IEEE.

Biometric recognition of individuals has been widely employed in establishing secure and trustworthy systems nowadays. However, due to its increased demand and usability, the associated security risks have been increased, which necessitates finding of more robust biometric traits than existing modalities. Recently, brain signal recorded by Electroencephalogram (EEG) technique has been reported as a potential biometric candidate on account of its high degree of uniqueness, stability and universality. This paper presents an EEG-based biometric authentication system employing brain patterns in response to a number of visual or auditory stimuli by seeing/hearing self, familiar and unfamiliar faces/voices. The system employs power spectral density (PSD) features extracted from alpha, beta and gamma bands of EEG for biometric authentication. The PSD values of multi-band EEG signals from 14 channels form template feature vector for each subject, and are stored in the database during enrollment phase. During online authentication, test feature vector is correlated with the respective template vector and the obtained correlation value is compared with a pre-defined threshold value. Based on the authentication experiments performed on 5 healthy subjects, the proposed system offers an overall accuracy of 79.73% with a false acceptance rate (FAR) of 13.91% and false rejection rate (FRR) of 26.6%.

Background: Neonatal sepsis remains one of the leading cause of morbidity and mortality both among term and preterm infants. Sepsis and meningitis are responsible for most of these deaths. According to WHO estimates, there are about 5 million neonatal deaths a year. Jaundice and hepatic dysfunction frequently accompany a variety of bacterial infections. This study was aimed to evaluate bilirubin fractions and liver function tests in septic and non septic neonates with hyperbilirubinemia. Materials and Methods: A total of 41 neonates, their age ranged from (1-28 days), mean age sepsis cases 4.295.34 and mean age in non sepsis 2.274.44. The patients admitted to neonatology unit for the management of hyperbilirubinemia were included in this study. Out of 41, 20 babies having sepsis (17 were males and 3 were females) and 21 (15 were males and 6 were females) were non sepsis. All study subjects were studied for the serum bilirubin fractions and other liver function tests by using vitros dry chemistry analyzer. Results: In the present study, delta bilirubin (0.955 0.546) and conjugated bilirubin (1.172.10) levels are significantly increased in sepsis cases when compared to non sepsis controls. Conclusion: In conclusion, conjugated bilirubin and delta bilirubin were significantly increased in neonates suffering from sepsis with hyperbilirubinemia. By studying individual fractions of bilirubin, especially unconjugated bilirubin, conjugated bilirubin and delta bilirubin (not as direct and indirect bilirubin) will help in early diagnosis of sepsis and thus may help in better management of the sepsis neonates.

Vitamins are essential nutrients in food crucial for maintaining good health. Tomato, being a widely consumed vegetable, provides a good quantity of vitamins. Metabolite profiling of vitamins at different stages of fruit maturity in a crop helps identify the right stage for better quality. Based on preliminary screening for quality parameters, tomato lines rich in TSS, antioxidants, lycopene and beta-carotene were selected for the present study. Eight genotypes and a wild species were profiled for 'B' vitamins at three different stages of fruit maturity, viz., green, breaker and ripe stage. A simple and sensitive liquid chromatography-tandem mass spectrometric (LC-MS/MS) method for simultaneous determination of six 'B' vitamins was developed and validated by us. Among the genotypes studied, IIHR-249-1 recorded higher niacin, pantothenic acid and biotin content. Pyridoxine content was higher in the hybrid, Arka Rakshak. The wild species, LA-1777(Solanum habrochaites) was found to be rich in pantothenic acid, riboflavin and thiamine. Content of most of the vitamins increased with ripening of the fruit. IIHR-249-1 and LA-1777 were found to be rich in 'B' vitamins, earlier reported to be also rich in antioxidants and lycopene. These genotypes can be used for improving the nutritive value of tomato under crop improvement programmes, through conventional breeding or biotechnological approaches.

Brain-Computer Interface (BCI) is an alternative communication and control channel between brain and computer which finds applications in neuroprosthetics, brain wave controlled computer games etc. This paper proposes an Electroencephalogram (EEG) based neurofeedback computer game that allows the player to control the game with the help of attention based brain signals. The proposed game protocol requires the player to memorize a set of numbers in a matrix, and to correctly fill the matrix using his attention. The attention level of the player is quantified using sample entropy features of EEG. The statistically significant performance improvement of five healthy subjects after playing a number of game sessions demonstrates the effectiveness of the proposed game in enhancing their concentration and memory skills. © 2013 IEEE.

Neurofeedback, the self-regulation of brain signals recorded using Electroencephalogram (EEG), allows Brain-Computer Interface (BCI) users to enhance cognitive as well as motor functions using specific training strategies. Therapeutic effects of neurofeedback (by the induction of neuroplasticity) on treatment of people with neurological disorders such as Attention-Deficit Hyperactive Disorder (ADHD), dementia and stroke have been reported in literature. In this paper, we investigate the impact of a neurofeedback based BCI game on the enhancement of attention and cognitive skills of healthy subjects. The BCI game is controlled by player's attention-related EEG signal. In the proposed training paradigm, subjects play the neurofeedback game regularly for a period of 5 days. The experimental analysis of player's attention level (measured by entropy values of EEG) and the comparison of cognitive test results demonstrate the benefits of practicing BCI based neurofeedback game in the enhancement of attention/cognitive skills. © 2013 IEEE.

Human-Computer Interface (HCI) enables people to control computer applications using bio-electric signals recorded from the body. HCI can be a potential tool for people with severe motor disabilities to communicate to external world through bio-electric signals. In an Electrooculogram (EOG) based HCI, signals during various eye (cornea) movements are employed to generate control signals. This paper presents the design of an EOG-based typing system which uses a virtual keyboard for typing letters on the monitor using 8 types of distinct EOG patterns. Identification of EOG pattern is based on the amplitude and timing of positive and negative components within the signal. Experimental results show that proposed EOG-based typing system achieves a higher typing speed of 15 letters/min and an improved accuracy of 95.2% compared to the state-of art method that has a typing speed of 12.1 letters/min and accuracy of 90.4%. © 2012 IEEE.

In an electroencephalogram (EEG)-based brain-computer interface (BCI), motor imagery has been successfully used as a communication strategy. Motor imagery causes detectable amplitude changes in certain frequency bands of EEGs, which are dubbed event-related desynchronization\synchronization. The frequency components that give effective discrimination between different types of motor imagery are subject specific and identification of these subject-specific discriminative frequency components (DFCs) is important for the accurate classification of motor imagery activities. In this paper, we propose a new method to estimate the DFC using the Fisher criterion and investigate the variability of these DFCs over multiple sessions of EEG recording. Observing the variability of DFC over sessions in the analysis, a new BCI approach called the Adaptively Weighted Spectral-Spatial Patterns (AWSSP) algorithm is proposed. AWSSP tracks the variation in DFC over time adaptively based on the deviation of discriminative weight values of frequency components. The classification performance of the proposed AWSSP is compared with a static BCI approach that employs fixed DFCs. In the offline and online experiments, AWSSP offers better classification performance than the static approach, emphasizing the significance of tracking the variability of DFCs in EEGs for developing robust motor imagery-based BCI systems. A study of the effect of feedback on the variation in DFCs is also performed in online experiments and it is found that the presence of visual feedback results in increased variation in DFCs. © 2011 IOP Publishing Ltd.

The performance of a Brain-Computer Interface (BCI) depends on reliable feature extraction and accurate classification. Motor imagery has been successfully used in BCI for communication and control. During motor imagery, for EEG based BCI, it was known that the discriminative frequency bands are subject-specific. Moreover, such discriminative frequency bands for each subject might vary from time to time. In this paper, we investigate the variability of discriminative spectral ranges and its impact on classification accuracy. It is found that for each subject, his discriminative frequency bands changes significantly from session to session, but keeps almost stable within a session. We then propose a method to adaptively update the discriminative frequency bands using Time-Frequency fisher ratio. From the experimental analysis, it is found that we can reduce the average error rate by 11.50% compared to the case where fixed discriminative frequency bands obtained from calibration session are used. ©2010 IEEE.

Brain Computer Interface (BCI) provides a direct communication pathway between a human and an external device. In this paper, we propose a new 'Discriminative FilterBank Common Spatial Pattern (DFBCSP)' algorithm to select the subject-specific filters automatically during training for a motor imagery based BCI. The subject-specific filters are selected using the fisher ratio values of filtered Electroencephalogram (EEG) signal. The channel 'C3' alone could give sufficient information to select the discriminative filterbank for the proposed system. We have also explored the possibility of boosting the system performance by including dynamic temporal features. Fusion of static and dynamic features in the proposed DFBCSP frame work gave an average test accuracy of 92.44%, which is significantly better than conventional filterbank based Common Spatial Pattern algorithms. ©2009 IEEE.

Brain Computer Interface (BCI) provides an alternative communication and control method for people with severe motor disabilities. Motor imagery patterns are widely used in Electroencephalogram (EEG) based BCIs. These motor imagery activities are associated with variation in alpha and beta band power of EEG signals called Event Related Desynchronization/synchronization (ERD/ERS). The dominant frequency bands are subject-specific and therefore performance of motor imagery based BCIs are sensitive to both temporal filtering and spatial filtering. As the optimum filter is strongly subjectdependent, we propose a method that selects the subject-specific discriminative frequency components using time-frequency plots of Fisher ratio of two-class motor imagery patterns. We also propose a low complexity adaptive Finite Impulse Response (FIR) filter bank system based on coefficient decimation technique which can realize the subject-specific bandpass filters adaptively depending on the information of Fisher ratio map. Features are extracted only from the selected frequency components. The proposed adaptive filter bank based system offers average classification accuracy of about 90%, which is slightly better than the existing fixed filter bank system. © 2008 IEEE.

The reaction of twenty-four new synthetic banana hybrids to Pratylenchus coffeae was studied under artificially inoculated pot conditions. Two banana hybrids, H-04-05 and H-04-06 were found to be resistant and ten hybrids, H-04-01, H-04-03, H-04-04, H-04-07, H-04-09, H-04-11, H-04-16, H-04-19, H-04-21 and H-04-24 were found to be tolerant to the lesion nematode, Pratylenchus coffeae and the remaining were rated as susceptible.