Academic literature on the topic 'Brainwave-control'
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Journal articles on the topic "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.
Full textMd 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.
Full textM, 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.
Full textBraboszcz, 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.
Full textCheng, 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.
Full textAbdul 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.
Full textGonzález-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.
Full textFlavia, 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.
Full textFlavia, 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/10.54105/ijainn.c1032.061321.
Full textHou, 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.
Full textDissertations / Theses on the topic "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.
Full textSamarnggoon, Keattikorn. "Modelling of human control and performance evaluation using artificial neural network and brainwave." Thesis, Staffordshire University, 2016. http://eprints.staffs.ac.uk/2389/.
Full textSheng-Kai, Huang, and 黃聖凱. "The Applications of Brainwave Control in Medicine." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/21365709140841347371.
Full text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.
Hung, Chia-Chun, and 洪家俊. "The Direction-Control Application Based on Brainwave Recognition." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/87969283042309706120.
Full text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.
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.
Full text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.
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.
Full text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.
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.
Full text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.
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.
Full text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.
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.
Full text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%.
Book chapters on the topic "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.
Full textEaton, 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.
Full textHuzmezan, 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.
Full textBill 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.
Full textA. 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.
Full textConference papers on the topic "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.
Full textZhao, 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.
Full textPakoktom, 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.
Full textLiu, 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.
Full textMathew, 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.
Full textMustafa, 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.
Full textYung-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.
Full textNoor, 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.
Full textJahidin, 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.
Full textJiang, 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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