Готові списки джерел за темами / Brainwave-control

Добірка наукової літератури з теми "Brainwave-control"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Brainwave-control"

Chiu, Wayne, Chunhua Su, Chuan-Yen Fan, Chien-Ming Chen, and Kuo-Hui Yeh. "Authentication with What You See and Remember in the Internet of Things." Symmetry 10, no. 11 (October 23, 2018): 537. http://dx.doi.org/10.3390/sym10110537.

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The Internet-of-Things (IoT) is an emerging paradigm seamlessly integrating a great number of smart objects ubiquitously connected to the Internet. With the rise in interest in the IoT, industry and academia have introduced a variety of authentication technologies to deal with security challenges. Authentication in IoT involves not only shifting intelligent access control down to the end smart objects, but also user identification and verification. In this paper, we build an authentication system based on brainwave reactions to a chain of events. Brainwaves, as external signals of a functioning brain, provide a glimpse into how we think and react. However, seen another way, we could reasonably expect that a given action or event could be linked back to its corresponding brainwave reaction. Recently, commercial products in the form of wearable brainwave headsets have appeared on the market, opening up the possibility of exploiting brainwaves for various purposes and making this more feasible. In the proposed system, we use a commercially available brainwave headset to collect brainwave data from participants for use in the proposed authentication system. After the brainwave data collection process, we apply a machine learning-based approach to extract features from brainwaves to serve as authentication tokens in the system and support the authentication system itself.

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Md Ahnaf Shariar, Syeda Maliha Monowara, Md. Shafayat Ul Islam, Muhammed Junaid Noor Jawad, and Saifur Rahman Sabuj. "Brainwave assistive system for paralyzed individuals." ITU Journal on Future and Evolving Technologies 2, no. 3 (July 15, 2021): 79–89. http://dx.doi.org/10.52953/ibjp6517.

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The Brain-Computer Interface (BCI) is a system based on brainwaves that can be used to translate and comprehend the innumerable activities of the brain. Brainwave refers to the bioelectric impulses invariably produced in the human brain during neurotransmission, often measured as the action potential. Moreover, BCI essentially uses the widely studied Electroencephalography (EEG) technique to capture brainwave data. Paralysis generally occurs when there is a disturbance in the central nervous system prompted by a neurodegenerative or unforeseen event. To overcome the obstacles associated with paralysis, this paper on the brainwave-assistive system is based on the BCI incorporated with Internet-of-things. BCI can be implemented to achieve control over external devices and applications. For instance, the process of cursor control, motor control, neuroprosthetics and wheelchair control, etc. In this paper, the OpenBCI Cyton-biosensing board has been used for the collection of the EEG data. The accumulated EEG data is executed subsequently to obtain control over the respective systems in real-time. Hence, it can be concluded that the experiments of the paper support the idea of controlling an interfaced system through the real-time application of EEG data.

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M, Yusof, M. M., Salleh, S. M, Ainul, H. M. Y, Siswanto, W. A, and Mahmud, W. M. A. .W. "Analysis of Golfer‘S Brainwave Signal During Par Tee Ireland and Driving Range Game." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 469. http://dx.doi.org/10.14419/ijet.v7i4.30.22370.

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Анотація:

Ways to improve sport performance become exceptional contemporary interest. Nowadays, many studies use human brain as an input signal include eyes blinking, attention and meditation to control the exchange process. Brain–Computer Interface (BCI) requires generating control signals for external device by analysing the internal brain signal. The objective is to identify the signal of brainwave which gives effect to performance of golfer. The analysis involved the meditation (α) and attention (β) state of different golf players. In this project, the brainwave of golfer’s will be analyzed based on the movement before club strike the ball. EEG signal used to find out the features by using Fast Fourier Transform (FFT). The analysis included three categories of player include beginner, intermediate and professional. Two types of game have been considered which are Par Tee Ireland and Driving Range. The project interfaces MATLAB software with an EEG headset. The data has interpreted in time and frequency domain graph that show different level in an attention (β) state for both games. Brainwave signals indicated players’ performance and lead to better performance. This data benefits increasing the performance of golfer to become the professional golfer by using electroencephalography (EEG) headset in future study.

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Braboszcz, Claire, B. Rael Cahn, Jonathan Levy, Manuel Fernandez, and Arnaud Delorme. "Increased Gamma Brainwave Amplitude Compared to Control in Three Different Meditation Traditions." PLOS ONE 12, no. 1 (January 24, 2017): e0170647. http://dx.doi.org/10.1371/journal.pone.0170647.

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Cheng, Chi Hang, Shuai Li, and Seifedine Kadry. "Mind-Wave Controlled Robot: An Arduino Robot Simulating the Wheelchair for Paralyzed Patients." International Journal of Robotics and Control 1, no. 1 (June 27, 2018): 6. http://dx.doi.org/10.5430/ijrc.v1n1p6.

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This project attempts to implement an Arduino robot to simulate a brainwave-controlled wheelchair for paralyzed patients with an improved controlling method. The robot should be able to move freely in anywhere under the control of the user and it is not required to predefine any map or path. An accurate and natural controlling method is provided, and the user can stop the robot any time immediately to avoid risks or danger. This project is using a low-cost and a brainwave-reading headset which has only a single lead electrode (Neurosky mind wave headset) to collect the EEG signal. BCI will be developed by sending the EEG signal to the Arduino Mega and control the movement of the robot. This project used the eye blinking as the robot controlling method as the eye blinking will cause a significant pulse in the EEG signal. By using the neural network to classify the blinking signal and the noise, the user can send the command to control the robot by blinking twice in a short period of time. The robot will be evaluated by driving in different places to test whether it can follow the expected path, avoid the obstacles, and stop in a specific position.

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Abdul Rahman, Ahmad Danial, and Hanim Hussin. "Detection of attention and meditation state-based brainwave system to control prosthetic arm." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 2 (February 1, 2019): 794. http://dx.doi.org/10.11591/ijeecs.v13.i2.pp794-800.

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<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>

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González-González, Gabriela, Víctor M. Velasco-Herrera, and Alicia Ortega-Aguilar. "Use of Covariance Analysis in Electroencephalogram Reveals Abnormalities in Parkinson’s Disease." Applied Sciences 11, no. 20 (October 15, 2021): 9633. http://dx.doi.org/10.3390/app11209633.

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Covariance analysis from wavelet data in electroencephalographic records (EEG) was, for the first time, applied in this study to unravel information contained in the standard EEG, which was previously not taken into consideration due to the mathematical models used. The methodology discussed here could be applied to any neurological condition, including the important early stages of neurodegenerative diseases. In this study, we analyzed EEG from control (CL) participants and participants with diagnosed Parkinson’s disease (PD), who were age-matched women in an eyes-closed resting state, to test the model. PD is predicted to rise over the next decades as the population ages. Furthermore, women are more likely to undergo PD-related complications and worse disability than men. Two groups based on age were considered: under and over 60 years (PD patients <60 and >60; CL <60 and >60). Continuous Wavelet Transform and Cross Wavelet Transform were applied to determine patterns of global wavelet curves, main frequencies, and power analyses. Our results indicate that both CL age groups and PD patients <60 share a main α brainwave and PD patients >60 showed a main δ brainwave. Interestingly, power anomalies analyses show a decreasing anteroposterior gradient in CL, whereas it is increasing in PD patients, which was not previously observed. The brainwave power in PD patients <60 was higher in θ, α and β waves and in >60 group, the δ, θ and β brainwaves were predominant. This methodology offers a tool to reveal abnormal electrical brain activity unseen by a regular EEG analysis. The advent of new models that process EEG, such as the model proposed in this study, promotes renewed interest in electrophysiology of the brain to study the early stages of PD and improve understanding of the origin and progress of the disease.

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Flavia, Ms Judy, Aviraj Patel, Diwakar Kumar Jha, and Navnit Kumar Jha. "A Wearable Brain-Computer Interface Instrument with Aug- Mented Reality-Based Interface for General Applications." Indian Journal of Artificial Intelligence and Neural Networking 1, no. 3 (June 10, 2021): 23–28. http://dx.doi.org/10.35940/ijainn.c1032.061321.

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In the project we are demonstrating the combined usage Augmented Reality(AR) and brain faced com- puter interface(BI) which can be used to control the robotic acuratorby.Thismethod is more simple and more user friendly. Here brainwave senor will work in its normal setting detecting alpha, beta, and gam- ma signals. These signals are decoded to detect eye movements. These are very limited on its own since the number of combinations possible to make higher andmorecomplextaskpossible.Asasolutiontothis AR is integrated with the BCI application to make control interface more user friendly. Thisapplication can be used in many cases including many robotic anddevicecontrollingcases.HereweuseBCI-ARto detect eye paralysis that can be archive by detecting eyelidmovementofpersonbywearingheadbend.

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Анотація:

In the project we are demonstrating the combined usage Augmented Reality(AR) and brain faced com- puter interface(BI) which can be used to control the robotic acurator by. This method is more simple and more user friendly. Here brainwave senor will work in its normal setting detecting alpha, beta, and gam- ma signals. These signals are decoded to detect eye movements. These are very limited on its own since the number of combinations possible to make higher and more complex task possible. Asa solution to this AR is integrated with the BCI application to make control interface more user friendly. This application can be used in many cases including many robotic and device controlling cases. Here we use BCI-AR to detect eye paralysis that can be archive by detecting eye lid movement of person by wearing head bend.

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Hou, Yong Mei, Pei Cheng Hu, Mei Wang, and Zhan Yu Mo. "Combined Brainwave Synchronizer with Progressive Muscle Relaxation on the Maintained Hemodialysis Patients: A Randomly Controlled Study." Applied Mechanics and Materials 140 (November 2011): 115–24. http://dx.doi.org/10.4028/www.scientific.net/amm.140.115.

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Mental problems have profound influence on the development and transfer of illness of the maintained hemodialysis (MHD) patients in the past study. The goal of this pilot study was to explore the effects of psychological intervention which combined brainwave synchronizer with progressive muscle relaxation (PMR) on the mental health, physiological function and quality-of-life (QOL) of MHD patients. Seventy-seven MHD patients were randomly assigned to the intervention group (ITG, 39 patients) or the control group (CTG, 38 patients). The CTG received the conventional therapy which included hemodialysis (HD) 3 times/week and recombinant human erythropoietin (rHuEPO) at dosages of 100 units/(kg·week-1) to 150 unites/(kg·week-1) by i.v or subcutaneous injection. The ITG received the conventional therapy combined with psychological intervention, which included brainwave synchronizer and PMR. Psychological and QOL outcomes in both groups were assessed with The Symptom Checklist 90 (SCL-90) and the Short Form 36 Health survey Questionnaire (SF-36) three days before and after the intervention, respectively. And blood routing and kidney functions indexes were tested with hematic parameter at the same time. Two-tailed independent samplettest andχ2test were performed. Thirty-six patients of the ITG and 34 patients of the CTG completed the study. The patients in the ITG showed better mental state, higher global QOL and higher values of heamoglobin (HB), accounting of red blood cells (RBC), hematocrit (HCT) and serum calcium, lower values of serum phosphor, serum kalium, blood urea nitrogen (BUN) before HD and the ratio of interdialytic weight gain to dry weight (IWGR) than those in the CTG. There is no significant difference in serum creatinine (Scr) before HD between both groups. Psychological intervention seems effective in improving MHD patients’ mental state, increasing their compliance to treatment, and mending their physiological function and QOL.

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Більше джерел

Дисертації з теми "Brainwave-control"

Abdrabou, Mohamed H., Khalid Elsaka, and O. Kravchuk. "EEG-based brain control interfaces with mobile robot." Thesis, ХНУРЕ, 2021. https://openarchive.nure.ua/handle/document/15690.

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This paper describes the development of a brainwave-controlled wheelchair. Robots have been not only widely used in industry but also gradually entering into human life. It can provide a support for disabled people in daily and professional life.

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Samarnggoon, Keattikorn. "Modelling of human control and performance evaluation using artificial neural network and brainwave." Thesis, Staffordshire University, 2016. http://eprints.staffs.ac.uk/2389/.

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Conventionally, a human has to learn to operate a machine by himself/herself. Human Adaptive Mechatronics (HAM) aims to investigate a machine that has the capability to learn its operator skills in order to provide assistance and guidance appropriately. Therefore, the understanding of human behaviour during the human-machine interaction (HMI) from the machine’s side is essential. The focus of this research is to propose a model of human-machine control strategy and performance evaluation from the machine’s point of view. Various HAM simulation scenarios are developed for the investigations of the HMI. The first case study that utilises the classic pendulum-driven capsule system reveals that a human can learn to control the unfamiliar system and summarise the control strategy as a set of rules. Further investigation of the case study is conducted with nine participants to explore the performance differences and control characteristics among them. High performers tend to control the pendulum at high frequency in the right portion of the angle range while the low performers perform inconsistent control behaviour. This control information is used to develop a human-machine control model by adopting an Artificial Neural Network (ANN) and 10-time- 10-fold cross-validation. Two models of capsule direction and position predictions are obtained with 88.3% and 79.1% accuracies, respectively. An Electroencephalogram (EEG) headset is integrated into the platform for monitoring brain activity during HMI. A number of preliminary studies reveal that the brain has a specific response pattern to particular stimuli compared to normal brainwaves. A novel human-machine performance evaluation based on the EEG brainwaves is developed by utilising a classical target hitting task as a case study of HMI. Six models are obtained for the evaluation of the corresponding performance aspects including the Fitts index of performance. The averaged evaluation accuracy of the models is 72.35%. However, the accuracy drops to 65.81% when the models are applied to unseen data. In general, it can be claimed that the accuracy is satisfactory since it is very challenging to evaluate the HMI performance based only on the EEG brainwave activity.

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Sheng-Kai, Huang, and 黃聖凱. "The Applications of Brainwave Control in Medicine." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/21365709140841347371.

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Анотація:

碩士
正修科技大學
資訊工程研究所
103
Some patients with the Amyotrophic Lateral Sclerosis disease, limb paralysis or other symptoms may be difficult to move by themselves. Thus, they need someone’s daily helps in their lives. In this master thesis, this gives us a research motivation to design the control system of the electrical appliances by using the brain waves of the patients. To make sure that the patients can use their own brains to control the electrical appliances on their demands, in this thesis, we have established a complete design flow to setup their dedicate brain control systems. First of all, we have designed a MATLAB based software program to measure several times the attention values of the patient’s brain wave, the average attention value can be calculated automatically. Then the average attention value can be built into the open source -- Arduino based control program which is used as the kernel of the brain wave control system to manipulate the electrical appliances. Therefore, this may be helpful to improve the patients’ own lives.

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Hung, Chia-Chun, and 洪家俊. "The Direction-Control Application Based on Brainwave Recognition." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/87969283042309706120.

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Анотація:

碩士
大葉大學
資訊工程學系碩士班
101
This research discusses about direction recognition by characteristic frequency bands of brainwave. This experiences proceeded by catching brain wave signals of human vision while sensing the test interfaces of arrows representing direction by brain wave sensor. To compile the related samples of energies from brainwave frequency band, then establish the directions which stand for forward and backward of brainwave characteristics frequency band. The experiments catch and analyze brain wave signals from by brain-wave sensor, and then calculate the attention value of direction recognition to control the movement of direction. This research not only analyzed and discussed every kind of brain wave frequency band characteristics while subjects recognize the directions, but also proposed the formula to calculate the value of attention controlling the movement of direction in the view of Cognitive Neuroscience.

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Cheng, Chiao-Chih, and 鄭喬植. "Based on brainwave sensing multi-control system of home appliances." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/gs976y.

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Анотація:

碩士
樹德科技大學
電腦與通訊系碩士班
106
Aging of population is the trend in the 21st century. Even though we live in an ever-changing technology country, the lowest birth rates still cause the problem of aging society with fewer children. Government must to face the aging problem in the future. At this time some elders move with difficult need to rely on these tools to improve their quality of life and control home appliances through sensing technology. Technology has also rapidly advanced in the 21st century. By using brainwave sensing technology, users can start and control electrical home appliances through the "think" way only. Because the technology does not need to talk, it can improve people who is physical disabilities greatly benefit the quality of life who is the physical abilities and the aging population in particular and the life of an aging society has also been assisted.

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Yeh, Geng-Wei, and 葉耿維. "Solve Maze Problem Using Error-Related Brainwave Potentials and Shared-Control Strategies." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/62476317920251381739.

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Анотація:

碩士
國立清華大學
資訊系統與應用研究所
102
In this paper we present a method of shared control strategy which combine electroencephalography (EEG) signals and heuristic algorithm, to guide a virtual dot on the two dimensional maze to reach the goal. One of the most main problems of EEG-based brain computer interfaces (BCIs) is the low classify accuracy. It is hard to build a generalize classifier to identify all the people’s EEG signals, in order to using people’s intention as the control factor with the low information rate, recent works have explored shared-control strategies which the system does not only execute the decoded commands from signals’ owner, but also involved in executing the task have been set up beforehand. That is, the system’s execution result can more close to people’s intension. Our shared-control system use error-related potentials (ErrP) as feedback which only be detected when the subjects feel wrong or confused. ErrP can be evoked steady in 0.3~0.6 milliseconds after the stimulation happened, with the subjects’ assessment of the target moving in the maze, transfer them as feedback into our heuristic function, we can guide the target to reach the goal without knowing the goal’s location efficiently. Shared-control strategies in BCI systems such as we presented here may prove to be the foundation for complex BCIs capable of doing more than we ever imagined.

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CHENG, MING-YANG, and 程明陽. "Multiple DOF Exoskeleton Bionic Robot Arm For Brainwave Remote Control A Mobile Robot." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/q6yhxx.

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Анотація:

碩士
國立高雄科技大學
電機工程系
107
Up to Now it has been possible to monitor the state of human brain activity through brainwaves with the advancement of technology. In this study, a single-pole brainwave headset developed by NeuroSky Technology was employed to develop a Brain Computer Interface that uses brain waves to communicate with computers and external devices. Then, the Zenbo home-based robot developed by Asus is combined with a bionic robot arm and real-time controlled by the human body posture to complete a grasping robot. Using the brain wave signal generated by eye movement as the input source, first the K-Nearest Neighbor (KNN) is used to initially filter the noise waveform and then the Convolutional Neural Network (CNN) is used to classify five kinds of eye movement signals into control commands which are forward, backward, left turn, right turn, and stop, respectively. And the Zenbo robot can be controlled to perform corresponding actions which the robot arm synchronization motion is controlled by the posture of the right hand of the user. At the same time, the image obtained by the lens on the Zenbo robot is real-time streamed to the brain machine interface for the user to remotely control. Finally, the experimental results roughly prove this robot system is work.

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Lai, Ping-Chiao, and 賴秉喬. "Use error-related brainwave potentials and a share-control strategy for drawing competition in two-dimensional grids." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/17271053102639335363.

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Анотація:

碩士
國立清華大學
資訊系統與應用研究所
102
Many scientists are interested in the analysis of human’s brainwave using electroencephalography (EEG) and conducting several applications such as game-controlling and physical therapy based on the EEG analysis. A brain-computer interface (BCI) could capture subjects’ EEG signals and to some extent infer the subject’s intention by analyzing the signals in a back-end system. In my thesis, we propose to use a novel kind of EEG analysis that is called error-related potential (ErrP) to enhance the performance and applicability of BCIs. ErrP is based on the fact that human’s cognitive state can be aware of error, and the unique kind of brainwave will be produced to reflect this cognitive state called ErrP which belongs to a kind of event-related potential (ERP). For conquering the low information rate of brain-computer interface, we use the shared-control strategy to enhance the robustness of our proposed model. Therefore, we combine the ErrP from human subject and shared-control strategy to propose a new kind model: Drawing model, and use the moving method : Convince and Reduce search (RAC search) we proposed to make the object move on the drawing model according to the rule of RAC search as system receive the response EEG signal of human subject. Because the accuracy of each subject’s personal trained classifier is not 100%, therefore, the object might deviate from the normal track while the classifier make a misclassification. So we add a probability mechanism for backtracking the out-of-track object to the normal track as the system has enough confidence. Our objective is to make the object complete the drawing mission quickly by using our drawing model. In the last, we will conduct a series of analysis for our experimental results, and the results show that our proposed drawing model enhance about 16% performance by comparing to the try and error model which is without the influence of subjects’ brainwave.

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CHENG, MAO-SHENG, and 鄭茂生. "Based on Brainwave with Adaptive Learning Rate Convolutional Neural Network Algorithm for Multi-User Remote Control A Mobile Robot." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/c8fs6y.

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Анотація:

碩士
國立高雄第一科技大學
電機工程研究所碩士班
106
In this study, a Brain-Computer Interface system is developed that can communicate with external devices through brainwave. The original waveform is acquired by a single-pole brainwave sensing headset, and features are extracted according to different signals generated by eye movements. First, K-Nearest Neighbor (KNN) is used to filter out unwanted signals. Then the method of Multi-user eye movements signal rapid modeling is employed to train eye movements signal classifiers through Convolutional Neural Network (CNN), and the five types of eye movements can be caught: up, down, left, right, and blinking. Establish a connection channel between brain-computer interface and Zenbo robot based on previous technics. Eventually, the images obtained using the lens on the Zenbo robot are instantly streamed to the brain-computer interface for remote control by the user. Finally, the average correct rate of multi-user eye movements on eye movements signal rapid modeling is over than 90%.

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Частини книг з теми "Brainwave-control"

Onunka, Chiemela, Glen Bright, and Riaan Stopforth. "Brainwave Variability Identification in Robotic Arm Control Strategy." In Robot Intelligence Technology and Applications 2, 173–89. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05582-4_16.

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Eaton, Joel, and Eduardo R. Miranda. "The Hybrid Brain Computer Music Interface - Integrating Brainwave Detection Methods for Extended Control in Musical Performance Systems." In Music, Mind, and Embodiment, 132–45. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46282-0_8.

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Huzmezan, Mihai, William A. Gough, and Guy A. Dumont. "Adaptive predictive regulatory control with brainwave." In Techniques for Adaptive Control, 99–143. Elsevier, 2003. http://dx.doi.org/10.1016/b978-075067495-9/50005-7.

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Bill Gough, W. A. "BrainWave®: Model Predictive Control for the Process Industries." In Advanced Model Predictive Control. InTech, 2011. http://dx.doi.org/10.5772/17002.

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A. Markovics, Jen. "Training the Conductor of the Brainwave Symphony: In Search of a Common Mechanism of Action for All Methods of Neurofeedback." In Artificial Intelligence. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98343.

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There are several different methods of neurofeedback, most of which presume an operant conditioning model whereby the subject learns to control their brain activity in particular regions of the brain and/or at particular brainwave frequencies based on reinforcement. One method, however, called infra-low frequency [ILF] neurofeedback cannot be explained through this paradigm, yet it has profound effects on brain function. Like a conductor of a symphony, recent evidence demonstrates that the primary ILF (typically between 0.01–0.1 Hz), which correlates with the fluctuation of oxygenated and deoxygenated blood in the brain, regulates all of the classic brainwave bands (i.e. alpha, theta, delta, beta, gamma). The success of ILF neurofeedback suggests that all forms of neurofeedback may work through a similar mechanism that does not fit the operant conditioning paradigm. This chapter focuses on the possible mechanisms of action for ILF neurofeedback, which may be generalized, based on current evidence.

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Тези доповідей конференцій з теми "Brainwave-control"

Li, Yingda, Fuyan Zhang, and Yiqing Yang. "Smart House Control System Controlled by Brainwave." In 2019 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). IEEE, 2019. http://dx.doi.org/10.1109/icitbs.2019.00134.

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Zhao, Jing, Qinghao Meng, Wei Li, Mengfan Li, Fuchun Sun, and Genshe Chen. "An OpenViBE-based brainwave control system for Cerebot." In 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2013. http://dx.doi.org/10.1109/robio.2013.6739622.

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Pakoktom, Nipawan, Suradej Tretriluxana, and Kitiphol Chitsakul. "Brainwave spectrum analysis during paced breathing control: A pilot study." In 2016 9th Biomedical Engineering International Conference (BMEiCON). IEEE, 2016. http://dx.doi.org/10.1109/bmeicon.2016.7859640.

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Liu, Chui-Yuan, Yu-Kai Huang, Han-Yen Yu, and I.-Chang Tsai. "Verifying User Concentration Based on Brainwave Control Applied to Different Game Training Methods." In 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE). IEEE, 2018. http://dx.doi.org/10.1109/tale.2018.8615311.

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Mathew, Marlene, Mert Cetinkaya, and Agnieszka Roginska. "BSoniq: A 3-D EEG Sound Installation." In The 23rd International Conference on Auditory Display. Arlington, Virginia: The International Community for Auditory Display, 2017. http://dx.doi.org/10.21785/icad2017.001.

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Brain Computer Interface (BCI) methods have received a lot of attention in the past several decades, owing to the exciting possibility of computer-aided communication with the outside world. Most BCIs allow users to control an external entity such as games, prosthetics, musical output etc. or are used for offline medical diagnosis processing. Most BCIs that provide neurofeedback, usually categorize the brainwaves into mental states for the user to interact with. Raw brainwave interaction by the user is not usually a feature that is readily available for a lot of popular BCIs. If there is, the user has to pay for or go through an additional process for raw brain wave data access and interaction. BSoniq is a multi-channel interactive neurofeedback installation which, allows for real-time sonification and visualization of electroencephalogram (EEG) data. This EEG data provides multivariate information about human brain activity. Here, a multivariate event-based sonification is proposed using 3D spatial location to provide cues about these particular events. With BSoniq, users can listen to the various sounds (raw brain waves) emitted from their brain or parts of their brain and perceive their own brainwave activities in a 3D spatialized surrounding giving them a sense that they are inside their own heads.

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Mustafa, Mahfuzah, Nur Syuhaida Hani Abdul Hamid, Nor Rul Hasma Abdullah, Rosdiyana Samad, and Dwi Pebrianti. "Brain dominance using brainwave signal." In 2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 2015. http://dx.doi.org/10.1109/iccsce.2015.7482201.

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Yung-Chin Hsiao, Hanayuki Kitagawa, and Junzo Watada. "Studies on eye tracking and brainwave measurement." In 2015 10th Asian Control Conference (ASCC). IEEE, 2015. http://dx.doi.org/10.1109/ascc.2015.7244781.

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Noor, Wan Mohd Fadzil Wan Mohd, N. Zaini, H. Norhazman, and Mohd Fuad Abdul Latip. "Dynamic encoding of binaural beats for brainwave entrainment." In 2013 IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 2013. http://dx.doi.org/10.1109/iccsce.2013.6720041.

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Jahidin, A. H., M. N. Taib, N. Md Tahir, M. S. A. Megat Ali, S. Lias, N. Fuad, and W. R. W. Omar. "Brainwave sub-band power ratio characteristics in intelligence assessment." In 2012 IEEE Control and System Graduate Research Colloquium (ICSGRC). IEEE, 2012. http://dx.doi.org/10.1109/icsgrc.2012.6287184.

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Jiang, Zhiling. "Study of brainwave frequency spectrum by AFT and FFT." In Optics and Optoelectronic Inspection and Control: Techniques, Applications, and Instruments, edited by Hong Liu and Qingming Luo. SPIE, 2000. http://dx.doi.org/10.1117/12.403988.

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