scholarly journals Detection of attention and meditation state-based brainwave system to control prosthetic arm

2019 ◽  
Vol 13 (2) ◽  
pp. 794
Author(s):  
Ahmad Danial Abdul Rahman ◽  
Hanim Hussin
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Brain Computer Interfaces ◽  
Brain Machine Interfaces ◽  
Motor Disabilities ◽  
Computer Interfaces ◽  
Acquisition Device ◽  
3D Printed ◽  
Data Acquisition Device ◽  
The Brain

<span>Neurotechnology has led to the development of Brain-Computer Interfaces (BCIs) or Brain-Machine Interfaces (BMIs) which are devices that use brain transmission signal to operate. Electroencephalography (EEG) is one of the recent methods that could retrieve transmission signal of the brain from scalp safely. This paper will discuss the development of Neuroprosthetics limb by using patients’ attention and meditation level to produce movement. The main objective of this project is to restore mobility of patients that have suffered from motor disabilities. This project is carried out by interfacing the data acquisition device which is NeuroSky Mindwaves Headset with the microcontroller to move the prosthetic arm as the output. Arduino Nano microcontroller acts as data processing and a controller to the arm as the output. The prosthetic arm is designed by using SOLIDWORKS software and fabricated by 3D printed. From this project, the user will be able to control the prosthetic arm ranging from rotating the hand to bending the fingers creating a grasp and release gesture.</span>

Automated System for Epileptic Seizures Prediction based on Multi-Channel Recordings of Electrical Brain Activity

2018 ◽  
pp. 115-122 ◽  
Author(s):  
V. A. Maksimenko ◽  
A. A. Harchenko ◽  
A. Lüttjohann
Keyword(s):  
Epileptic Seizure ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Automated System ◽  
Brain Computer Interfaces ◽  
Electrical Brain Activity ◽  
Computer Interfaces ◽  
Prediction And Prevention ◽  
The Brain

Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation  into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans. 

MONITORING AND PREDICTIVE ANALYTICS OF TECHNOLOGICAL EQUIPMENT ON THE BASED OF INDUSTRIAL INTERNET OF THINGS

2020 ◽  
pp. 7-12
Author(s):  
С.Л. Добрынин ◽  
В.Л. Бурковский
Keyword(s):  
Internet Of Things ◽  
Data Acquisition ◽  
Predictive Analytics ◽  
Actual State ◽  
Monitoring And Control ◽  
Industrial Internet Of Things ◽  
Machining Time ◽  
Industrial Internet ◽  
Acquisition Device ◽  
Data Acquisition Device

Произведен обзор технологий в рамках концепции четвертой промышленной революции, рассмотрены примеры реализации новых моделей управления технологическими процессами на базе промышленного интернета вещей. Описано техническое устройство основных подсистем системы мониторинга и контроля, служащей для повышения осведомленности о фактическом состоянии производственных ресурсов в особенности станков и аддитивного оборудования в режиме реального времени. Архитектура предлагаемой системы состоит из устройства сбора данных (УСД), реализующего быстрый и эффективный сбор данных от станков и шлюза, передающего ликвидную часть информации в облачное хранилище для дальнейшей обработки и анализа. Передача данных выполняется на двух уровнях: локально в цехе, с использованием беспроводной сенсорной сети (WSN) на базе стека протоколов ZigBee от устройства сбора данных к шлюзам и от шлюзов в облако с использованием интернет-протоколов. Разработан алгоритм инициализации протоколов связи между устройством сбора данных и шлюзом, а также алгоритм выявления неисправностей в сети. Расчет фактического времени обработки станочных подсистем позволяет более эффективно планировать профилактическое обслуживание вместо того, чтобы выполнять задачи обслуживания в фиксированные интервалы без учета времени использования оборудования We carried out a review of technologies within the framework of the concept of the fourth industrial revolution; we considered examples of the implementation of new models of process control based on the industrial Internet of things. We described the technical structure of the main subsystems of the monitoring and control system to increase awareness of the actual state of production resources in particular machine tools and additive equipment in real time. The architecture of the proposed system consists of a data acquisition device (DAD) that implements fast and efficient data collection from machines and a gateway that transfers the liquid part of information to the cloud storage for further processing and analysis. We carried out the data transmission at two levels, locally in the workshop, using a wireless sensor network (WSN) based on ZigBee protocol stack from the data acquisition device to the gateways and from the gateways to the cloud using Internet protocols. An algorithm was developed for initializing communication protocols between a data acquisition device and a gateway, as well as an algorithm for detecting network malfunctions. Calculating the actual machining time of machine subsystems allows us to more efficiently scheduling preventive maintenance rather than performing maintenance tasks at fixed intervals without considering equipment usage

scholarly journals fNIRS Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task

2021 ◽  
Vol 11 (11) ◽  
pp. 4922
Author(s):  
Tengfei Ma ◽  
Wentian Chen ◽  
Xin Li ◽  
Yuting Xia ◽  
Xinhua Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Total Duration ◽  
Signal Classification ◽  
Imagery Task ◽  
Neural Activities ◽  
Computer Interfaces ◽  
Acquisition Device ◽  
The Time Domain ◽  
The Brain

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

scholarly journals A Flexible Microcontroller-Based Data Acquisition Device

Sensors ◽  
2014 ◽  
Vol 14 (6) ◽  
pp. 9755-9775 ◽  
Author(s):  
Darko Hercog ◽  
Bojan Gergič
Keyword(s):  
Data Acquisition ◽  
Acquisition Device ◽  
Data Acquisition Device

scholarly journals The current state of electrocorticography-based brain–computer interfaces

2020 ◽  
Vol 49 (1) ◽  
pp. E2 ◽  
Author(s):  
Kai J. Miller ◽  
Dora Hermes ◽  
Nathan P. Staff
Keyword(s):  
Functional Limitations ◽  
Brain Computer Interfaces ◽  
Brain Surface ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Current State ◽  
Temporal And Spatial ◽  
Direct Feedback ◽  
The Brain ◽  
Lateral Sclerosis

Brain–computer interfaces (BCIs) provide a way for the brain to interface directly with a computer. Many different brain signals can be used to control a device, varying in ease of recording, reliability, stability, temporal and spatial resolution, and noise. Electrocorticography (ECoG) electrodes provide a highly reliable signal from the human brain surface, and these signals have been used to decode movements, vision, and speech. ECoG-based BCIs are being developed to provide increased options for treatment and assistive devices for patients who have functional limitations. Decoding ECoG signals in real time provides direct feedback to the patient and can be used to control a cursor on a computer or an exoskeleton. In this review, the authors describe the current state of ECoG-based BCIs that are approaching clinical viability for restoring lost communication and motor function in patients with amyotrophic lateral sclerosis or tetraplegia. These studies provide a proof of principle and the possibility that ECoG-based BCI technology may also be useful in the future for assisting in the cortical rehabilitation of patients who have suffered a stroke.

Neuroethics and Implanted Brain Machine Interfaces

2013 ◽  
pp. 351-365
Author(s):  
Ellen M. McGee
Keyword(s):  
Legal Issues ◽  
The Internet ◽  
Brain Machine Interfaces ◽  
Computer Interfaces ◽  
Scientific Organizations ◽  
Human Ability ◽  
Ethical And Legal Issues ◽  
The Individual ◽  
The Brain ◽  
Radical Enhancement

Transformations of humans through advances in bioelectronics, nanotechnologies, and computer science are leading to hybrids of humans and machines. Future brain-machine interfaces will enable humans not only to be constantly linked to the Internet, and to cyber think, but will also enable technology to take information directly from the brain. Brain-computer interfaces, where a chip is implanted in the brain, will facilitate a tremendous augmentation of human capacities, including the radical enhancement of the human ability to remember and to reason, and to achieve immortality through cloning and brain downloading, or existence in virtual reality. The ethical and legal issues raised by these possibilities represent global challenges. The most pressing concerns are those raised by privacy and autonomy. The potential exists for control of persons, through global tracking, by actually “seeing” and “hearing” what the individual is experiencing, and by controlling and directing an individual’s thoughts, emotions, moods, and motivations. Public dialogue must be initiated. New principles, agencies, and regulations need to be formulated and scientific organizations, states, countries, and the United Nations must all be involved.

scholarly journals Design of Smart-Meter data acquisition device based on Cloud Platform

2018 ◽  
Vol 153 ◽  
pp. 042028
Author(s):  
Xiangqun Chen ◽  
Rui Huang ◽  
Liman Shen ◽  
Hao chen ◽  
Dezhi Xiong ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Smart Meter ◽  
Cloud Platform ◽  
Acquisition Device ◽  
Data Acquisition Device
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