An interactive national digital surveillance system to fight against COVID-19 in Bangladesh.

Background: COVID-19 has affected many people globally, including in Bangladesh. Due to a lack of preparedness and resources, Bangladesh has experienced a catastrophic health crisis, and the devastation caused by this deadly virus has not yet been halted. Hence, precise and rapid diagnostics and infection tracing are essential for managing the condition and limiting its spread. The conventional screening procedure, such as reverse transcription polymerase chain reaction (RT-PCR), is not available in most rural areas and is time-consuming. Therefore, a data-driven intelligent surveillance system can be advantageous for rapid COVID-19 screening and risk estimation. Objectives: This study describes the design, development, implementation, and characteristics of a nationwide web-based surveillance system for educating, screening, and tracking COVID-19 at the community level in Bangladesh. Methods: The system consists of a mobile phone application and a cloud server. The data is collected by community health professionals via home visits or telephone calls and analyzed using rule-based artificial intelligence (AI). Depending on the results of the screening procedure, a further decision is made regarding the patient. This digital surveillance system in Bangladesh provides a platform to support government and non-government organizations, including health workers and healthcare facilities, in identifying patients at risk of COVID-19. It refers people to the nearest government healthcare facility, collecting and testing samples, tracking and tracing positive cases, following up with patients, and documenting patient outcomes. Results: This study began in April 2020, and the results are provided in this paper till December 2022. The system has successfully completed 1,980,323 screenings. Our rule-based AI model categorized them into five separate risk groups based on the acquired patient information. According to the data, around 51% of the overall screened populations are safe, 35% are low risk, 9% are high risk, 4% are mid risk, and the remaining 1% is very high risk. The dashboard integrates all collected data from around the nation onto a single platform. Conclusion: This screening can help the symptomatic patient take immediate action, such as isolation or hospitalization, depending on the severity. This surveillance system can also be utilized for risk mapping, planning, and allocating health resources to more vulnerable areas to reduce the virus's severity.

Recent Trends in Non-invasive Neural Recording Based Brain-to-Brain Synchrony Analysis on Multidisciplinary Human Interactions for Understanding Brain Dynamics: A Systematic Review.

The study of brain-to-brain synchrony has a burgeoning application in the brain-computer interface (BCI) research, offering valuable insights into the neural underpinnings of interacting human brains using numerous neural recording technologies. The area allows exploring the commonality of brain dynamics by evaluating the neural synchronization among a group of people performing a specified task. The growing number of publications on brain-to-brain synchrony inspired the authors to conduct a systematic review using the PRISMA protocol so that future researchers can get a comprehensive understanding of the paradigms, methodologies, translational algorithms, and challenges in the area of brain-to-brain synchrony research. This review has gone through a systematic search with a specified search string and selected some articles based on pre-specified eligibility criteria. The findings from the review revealed that most of the articles have followed the social psychology paradigm, while 36% of the selected studies have an application in cognitive neuroscience. The most applied approach to determine neural connectivity is a coherence measure utilizing phase-locking value (PLV) in the EEG studies, followed by wavelet transform coherence (WTC) in all of the fNIRS studies. While most of the experiments have control experiments as a part of their setup, a small number implemented algorithmic control, and only one study had interventional or a stimulus-induced control experiment to limit spurious synchronization. Hence, to the best of the authors' knowledge, this systematic review solely contributes to critically evaluating the scopes and technological advances of brain-to-brain synchrony to allow this discipline to produce more effective research outcomes in the remote future.

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework.

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.

An intelligent neuromarketing system for predicting consumers' future choice from electroencephalography signals.

Neuromarketing utilizes Brain-Computer Interface (BCI) technologies to provide insight into consumers responses on marketing stimuli. In order to achieve insight information, marketers spend about $400 billion annually on marketing, promotion, and advertisement using traditional marketing research tools. In addition, these tools like personal depth interviews, surveys, focus group discussions, etc. are expensive and frequently criticized for failing to extract actual consumer preferences. Neuromarketing, on the other hand, promises to overcome such constraints. In this work, an EEG-based neuromarketing framework is employed for predicting consumer future choice (affective attitude) while they view E-commerce products. After preprocessing, three types of features, namely, time, frequency, and time-frequency domain features are extracted. Then, wrapper-based Support Vector Machine-Recursive Feature Elimination (SVM-RFE) along with correlation bias reduction is used for feature selection. Lastly, we use SVM for categorizing positive affective attitude and negative affective attitude. Experiments show that the frontal cortex achieves the best accuracy of 98.67±2.98, 98±3.22, and 98.67±3.52 for 5-fold, 10-fold, and leave-one-subject-out (LOSO) respectively. In addition, among all the channels, Fz achieves best accuracy 90±7.81, 90.67±9.53, and 92.67±7.03 for 5-fold, 10-fold, and LOSO respectively. Subsequently, this work opens the door for implementing such a neuromarketing framework using consumer-grade devices in a real-life setting for marketers. As a result, it is evident that EEG-based neuromarketing technologies can assist brands and enterprises in forecasting future consumer preferences accurately. Hence, it will pave the way for the creation of an intelligent marketing assistive system for neuromarketing applications in future.

MarketBrain: An EEG based intelligent consumer preference prediction system.

The traditional marketing research tools (Personal Depth Interview, Surveys, FGD, etc.) are cost-prohibitive and often criticized for not extracting true consumer preferences. Neuromarketing tools promise to overcome such limitations. In this study, we proposed a framework, MarketBrain, to predict consumer preferences. In our experiment, we administered marketing stimuli (five products with endorsements), collected EEG signals by EMOTIV EPOC+, and used signal processing and classification algorithms to develop the prediction system. Wavelet Packet Transform was used to extract frequency bands (δ, θ, α, ß1, ß2, γ) and then statistical features were extracted for classification. Among the classifiers, Support Vector Machine (SVM) achieved the best accuracy (96.01±0.71) using 5-fold cross-validation. Results also suggested that specific target consumers and endorser appearance affect the prediction of the preference. So, it is evident that EEG-based neuromarketing tools can help brands and businesses effectively predict future consumer preferences. Hence, it will lead to the development of an intelligent market driving system for neuromarketing applications.

Progress in Brain Computer Interface: Challenges and Opportunities.

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks.

Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs) for robust movement decoding of Parkinson's disease (PD) and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value) at about 0.729 ± 0.16 for decoding movement from the resting state and about 0.671 ± 0.14 for decoding left and right visually cued movements.

Swallowing accelerometry signal feature variations with sensor displacement.

Dual-axis accelerometry has recently shown promise as a non-invasive method for detecting swallowing impairment using signal processing and pattern classification algorithms. However, it is unknown whether variations in sensor placement alter signal characteristics, threatening the accuracy of signal processing classifiers for aspiration detection. To address this question, water swallows were recorded in 14 healthy adults using a dual-axis accelerometer in 13 different positions (baseline, and 2, 4, 6 and 8 mm above, below and to the right of baseline). The baseline position was midline, immediately below the thyroid cartilage during quiet breathing. After segmentation and pre-processing, signal features were extracted in multiple domains (time, frequency, time-frequency). The effect of sensor position on signal feature distributions was examined with non-parametric statistical analysis. The analysis showed that the sensor could be displaced by as much as 4 mm inferior and lateral to the baseline position and by up to 6 mm above the baseline location without significantly altering time-frequency features. In other words, when considering the baseline position as the origin, the admissible region for sensor placement spans 10 mm in the superior-inferior axis and 8 mm in the medial-lateral direction. Results of this study suggest that time-frequency representations of accelerometry signals are most robust to sensor placement variations around the baseline position. The implication of this finding is that a swallowing accelerometry classifier based on time-frequency features can likely tolerate small variations in sensor location without degradation in classification performance.

Online transcranial Doppler ultrasonographic control of an onscreen keyboard.

Brain-computer interface (BCI) systems exploit brain activity for generating a control command and may be used by individuals with severe motor disabilities as an alternative means of communication. An emerging brain monitoring modality for BCI development is transcranial Doppler ultrasonography (TCD), which facilitates the tracking of cerebral blood flow velocities associated with mental tasks. However, TCD-BCI studies to date have exclusively been offline. The feasibility of a TCD-based BCI system hinges on its online performance. In this paper, an online TCD-BCI system was implemented, bilaterally tracking blood flow velocities in the middle cerebral arteries for system-paced control of a scanning keyboard. Target letters or words were selected by repetitively rehearsing the spelling while imagining the writing of the intended word, a left-lateralized task. Undesired letters or words were bypassed by performing visual tracking, a non-lateralized task. The keyboard scanning period was 15 s. With 10 able-bodied right-handed young adults, the two mental tasks were differentiated online using a Naïve Bayes classification algorithm and a set of time-domain, user-dependent features. The system achieved an average specificity and sensitivity of 81.44 ± 8.35 and 82.30 ± 7.39%, respectively. The level of agreement between the intended and machine-predicted selections was moderate (κ = 0.60). The average information transfer rate was 0.87 bits/min with an average throughput of 0.31 ± 0.12 character/min. These findings suggest that an online TCD-BCI can achieve reasonable accuracies with an intuitive language task, but with modest throughput. Future interface and signal classification enhancements are required to improve communication rate.

Long term consistency of handwriting grip kinetics in adults.

While there is growing interest in clinical applications of handwriting grip kinetics, the consistency of these forces over time is not well-understood at present. In this study, we investigated the short- and long-term intra-participant consistency and inter-participant differences in grip kinetics associated with adult signature writing. Grip data were collected from 20 adult participants using a digitizing tablet and an instrumented pen. The first phase of data collection occurred over 10 separate days within a three week period. To ascertain long-term consistency, a second phase of data collection followed, one day per month over several months. In both phases, data were collected three times a day. After pre-processing and feature extraction, nonparametric statistical tests were used to compare the within-participant grip force variation between the two phases. Participant classification based on grip force features was used to determine the relative magnitude of inter-participant versus intra-participant differences. The misclassification rate for the longitudinal data were used as an indication of long term kinetic consistency. Intra-participant analysis revealed significant changes in grip kinetic features between the two phases for many participants. However, the misclassification rate, on average, remained stable, despite different demarcations of training, and testing data. This finding suggests that while signature writing grip forces may evolve over time, inter-participant kinetic differences consistently exceeds within-participant force changes in the long-term. These results bear implications on the collection, modeling and interpretation of grip kinetics in clinical applications.