Search

The human brain is the central organ of the human system. Many people in the world cannot move on their own and can't control things on their own. A person whose brain is active can control things using the neuro-controlled robot car. It is interesting to all types of people to measure their concentration and piece level of mind with the neuro sky mind wave device. One can easily control the robot's movements by simply blinking eyes; the robot's speed will be according to the subject's attention levels. The neuro sky mind wave device digitizes brain wave signals to power the user-interface of the computers, game, and health application. The neuro sky mind wave device will measure brain waves from the forehead. The paper aims to control a robot using the brain-computer interface concept without any muscular activity controlling healthcare applications directions. The brain activity is recorded with the neuro sky mind wave device's help, and the attention values are sent to the Arduino with the help of the HC-05 Bluetooth module. Arduino is programmed so that if the attention values between 0-29 and the person are relaxed, the green light will glow for the feedback.

The catastrophic outbreak of the Novel Corona virus (Covid-19) has brought to light, the significance of reliable predictive mathematical models. The results from such models greatly affect the use of non-pharmaceutical intervention measures, management of medical resources and understanding risk factors. This paper compares popular mathematical models based on their predictive capabilities, practical validity, presumptions and drawbacks. The paper focuses on popular techniques in use for the predictive modeling of the Covid-19 epidemic. The paper covers the Gaussian Model, SIRD, SEIRD and the latest θ-SEIHRD techniques used for predictive modeling of epidemics.

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

Specialists are dealing with the improvement of EEG-based human–computer interface for upgrading the personal satisfaction in restorative and additionally non-medicinal applications using the blink of eyes. Such innovation can be consolidated to brain science, anesthesiology, gaming, security framework, and for continuous patients checking. It is easy to use the Neurosky Mindwave headset gadgets, which are for the most part used to identify and measure electrical action of the client’s temple and transmit the gathered information remotely, to a computer. Subsequent to preparing EEG signal, it is classify into different recurrence groups for highlight extraction. This paper for the most part deals with extricating the component of EEG sign in OpenViBE. Here, the characteristics and specification of EEG-based HCIs for real-time applications are presented. Furthermore, the discussion about the mental or behavioral state of the person (eyeblink, meditation, attention levels) through the NeuroSky Mindwave (MW001) device using OpenViBE has been done.

Electroencephalography (EEG) based human computer interface for upgrading the personal satisfaction is one of the burning research field in restorative and additionally non-medicinal applications. Such innovation can be consolidated to brain science, anesthesiology, gaming, security framework and for continuous patients monitoring. The Neurosky Mind wave headset gadgets are for the most part use to identify and measure electrical action of the client's temple and transmit the gathered information remotely, to a Computer for further preparing. Subsequent to preparing EEG signal, it is classifications into different recurrence groups for highlight extraction. This paper deals with Eye state prediction of dataset using different classifiers viz. Ada Boost, Naive Bayes and Multilayer perception (MLP) through WEKA. In this paper we are acquire different subjects from the age gap between 18–40 years perform Eye state levels have been examined. We have used different machine learning schemes for Eye blinking and Eye Open but MLP classifier provides highest classification rate i.e. 85% and ROC [Receiver Operating Characteristics] area is 0.855.

This research paper presents path following two wheeled compact portable robot with arduino nano as cental driving functional unit with novel features of wireless control using wifi and bluetooth module with collision detection, avoidance and control features which provides the unique ability of danger avoidance, falling from a hieght with improved stablity and precision control. The extremely sophistcated design provides very good controlled movement on horizantal ground terrain surfaces with data collecting and processing capabilties. The design is integrated with infrared sensors, bluetooth module, wifi module control with dc gear motors which controls the speed of the vehicle of the robotic vehicle and avoid collision with any obstacle detected in the path of the robot. It has the unique abilty of running in maze with path following abilties controlled from any remote location using WiFi for long range control and bluetooth for short range control. In this research article a entire system is designed and implemented in which movement is stably controlled based on feedback from infrared transreciever module. A low cost robust portable design using GUI control has been implemented with advanced features which makes it very unique and attractive for commercial production.

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG) based human computer interface in order to enhance the quality of life for medically as well as non-medical applications. Industry and community of research have been attracted by wireless EEG reading devices and they are easily available in the market. Such technology can be incorporated into psychology, anesthesiology, and for real-time patients monitoring. A brain computer interface (BCI) is a direct communication channel between the human brain and the digital computer. In this paper, we present a review on characteristics and specification of EEG-based human computer interfaces for real-time applications using wearable or wireless EEG devices.