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Literatura académica sobre el tema "Alpha brainwaves"

Autor: Grafiati

Publicado: 7 de junio de 2025

Última modificación: 2 de agosto de 2025

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Artículos de revistas sobre el tema "Alpha brainwaves"

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Shin, Yun-A., Sang-Min Hong, Seung-Ho Kim, and Byung-Kun Lee. "The Effect of Kendo’s Chudan-no-kamae and Three Striking Movements on Changes in Brainwaves Related to Concentration Index." Asian Journal of Kinesiology 23, no. 2 (2021): 34–44. http://dx.doi.org/10.15758/ajk.2021.23.2.34.

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OBJECTIVES This study was conducted to figure out the effects of kendo training on the activity of brainwaves by analyzing changes in brainwaves during chudan-no-kamae and three-movement-striking, which are basic movements of kendo, in elementary school students who undergo kendo training and comparing left and right side brainwave activities to find out differences.METHODS This study was conducted with 29 male elementary school students living in K City who were experts in kendo with a career as an athlete or a kendo training period not shorter than six months. The brainwave activity was meas

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Nasaruddin, Nurul Hanim, Farah Dania Hipni, and Noraisah Arbadul. "Exploring Background Noise During Learning: A Neurofeedback Study." Journal of Cognitive Sciences and Human Development 9, no. 2 (2023): 229–42. http://dx.doi.org/10.33736/jcshd.5867.2023.

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Distinguishing effective background noise during learning is crucial for students. This study was conducted to determine the effective noise background during learning. Two healthy female subjects were recruited for this experiment. They were put in separate brainwave recording sessions for learning tasks without noise and with background noise. Their theta and alpha brainwave readings showed higher activity while learning with background noise. The finding indicated that both brainwaves represented insight and alertness were more dominant during active learning tasks than without noise. Howev

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Mohd Radi, Nur Afiqah, and Nurul Hanim Nasaruddin. "Brainwaves Activities during Resting State: A Neurofeedback Case Study." Journal of Cognitive Sciences and Human Development 8, no. 1 (2022): 100–111. http://dx.doi.org/10.33736/jcshd.4004.2022.

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Neurofeedback training (NFT) on a healthy participant was used to analyse brainwave activity during resting state. Electrodes were placed on the prefrontal cortex and at reference sites to conduct the experiment. The patient was asked to remain still and rest by sitting in a chair. The technology was then used to record the electrical waves emitted by brain activities. After ten cycles with a break interval, theta, alpha, and high beta brainwaves were discovered. There were statistically significant brainwaves in the prefrontal cortex. Remarkably, the ascending high beta oscillation range was

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Chaipakornwong, Tinnaphol, and Phakkharawat Sittiprapaporn. "Brain exercise in elderly: NeuroSky smarter Kit investigation." Asian Journal of Medical Sciences 11, no. 1 (2020): 69–74. http://dx.doi.org/10.3126/ajms.v11i1.26528.

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Background: The measurement analysis of the brainwaves can show whether or not human’s mental or cognitive status is in a sober state. The speed of the computer signal processing technology increased until it could sufficiently process the complex brainwave signals measured in real-time. Therefore, the information about the mental status thus gradually revealed a research in information science called affective computing. Aims and Objective: The purpose of this study was to study the use of NeuroSky Smarter Kit in brain exercise training program by Brainwave Cloud Classroom (BCC) system,

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Sirikongtham, Puwadol, Worapat Paireekreng, and Suwannit Chareen Chit. "Brainwave Detection Model for Panic Attacks Based on Event-related Potential." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 27, no. 1 (2019): 333–44. http://dx.doi.org/10.29196/jubpas.v27i1.2168.

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Panic attacks could adversely affect a patient’s daily life and can pose risks to others. The symptoms of panic attacks can be timely observed by detecting the brainwave. This research presents a model that can evaluate the level of panic attack symptoms using the brainwaves detection during (or before) the symptom occurs. It helps monitor the patient’s brainwave based on Event-related potential (ERP). The model is derived from the simulation with horror pictures and frightening sound on the experimental group of 30 people. The survey related to symptoms has been used regarding to the criteria

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Ketenci, Seniha, and Temel Kayikcioglu. "Investigation of Theta Rhythm Effect in Detection of Finger Movement." Journal of Experimental Neuroscience 13 (January 2019): 117906951982873. http://dx.doi.org/10.1177/1179069519828737.

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Movements cause changes in cortical rhythms emanating from the sensorimotor area. It is known that alpha and beta brainwaves take an important role of motor activity and motor imagery. Besides, theta rhythm is considered to carry substantial information about movement initiation and execution. In this study, effect of theta brainwave on movement detection was investigated in four-right handed participants who performed extensions with fingers of right hand using electroencephalography (EEG). Movement and rest epochs from continuous EEG record were extracted using muscle signals. Channels locat

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Zhao, Wenbo. "The Impact of Alpha Wave Music On Employee Productivity In Real Work Environments." Advances in Economics, Management and Political Sciences 21, no. 1 (2023): 324–33. http://dx.doi.org/10.54254/2754-1169/21/20230269.

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In recent years, it has been discovered in some brainwave studies that alpha waves are one of the four basic brainwaves that effectively activate the left hindbrain region. Therefore, a type of music has emerged that can trigger these brainwaves, collectively known as Alpha wave music. This article aims to demonstrate whether Alpha Wave Music can make employees more productive so companies can increase their effectiveness affordably and efficiently. This study fills a gap in the application of Alpha wave music in practical application scenarios and builds a data model on the relationship betwe

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Kim, Joohyun, Miji Kim, Miran Jang, and Junyeop Lee. "The Effect of Juingong Meditation on the Theta to Alpha Ratio in the Temporoparietal and Anterior Frontal EEG Recordings." International Journal of Environmental Research and Public Health 19, no. 3 (2022): 1721. http://dx.doi.org/10.3390/ijerph19031721.

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(1) Background: The effect of Juingong meditation on brainwave patterns has not been explored yet. This study aimed to study the changes in brainwave patterns produced by Juingong meditation, through electroencephalography (EEG) measurements. (2) Methods: The study included 23 participants from the Hanmaum Seon Center in Korea. EEG measurements were performed using InteraXon’s four-channel EEG measurement equipment, Muse. It measures EEG patterns in the temporoparietal and anterior frontal lobes. Brainwaves were measured in two different states: when Juingong meditation was practiced and when

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Choi, Nam Sik, Min-Kyung Kim, and Hee Wook Ryu. "Brainwaves and Emotional Responses to Changes in Red Eye Shadow Brightness and Saturation." Asian Journal of Beauty and Cosmetology 22, no. 1 (2024): 147–61. http://dx.doi.org/10.20402/ajbc.2024.0003.

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Purpose: Investigating how Munsell’s primary color (i.e., red eye shadow) influences human brain and emotional responses to establish foundational data for image formation. Methods: We analyzed the relative power differences in background brainwaves due to variations in five levels of brightness and saturation for each eye both in male and female individuals in their twenties. We used the coefficient of variation for brainwaves for our analysis. In addition, we assessed the preferences for altered red eye shadow and the emotional responses to determine the average preference index. We performe

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Nalluri, Veera Raghava Swamy, V. J. K. Kishor Sonti, and G. Sundari. "Analysis of frequency dependent Vedic chanting and its influence on neural activity of humans." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 2 (2023): 230. http://dx.doi.org/10.11591/ijres.v12.i2.pp230-239.

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<p>In this paper a novel methodology is proposed to identify and to compare the frequency range of different Vedic chantings from Rig Veda, Yajur Veda, Atharva Veda and Sama Veda. Nowadays in spite of busy schedule and hectic work, the human beings are mostly stressed. To get rid from this stressed state, one of the best solutions is listening Vedic chantings. The alpha brainwaves are in the frequency range of 8-12 Hz under giving relaxation to stressed human being. Three selected samples from each Veda have been processed through the simulation compiler Praat and the parameters like spe

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Libros sobre el tema "Alpha brainwaves"

1

Wise, Anna. The high-performance mind: Mastering brainwaves for insight, healing, and creativity. Putnam, 1997.

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2

Halpern, Steven. Deep alpha: 8 hz brainwave synchronization for meditation and healing. Inner Peace Music, 2012.

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3

Alpha Meditation: A nonreligious mindfulness exercise that produces synchronous Alpha frequency brainwaves. Innovative Eggz LLC, 2019.

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Alpha Meditation: A Nonreligious Mindfulness Exercise That Produces Synchronous Alpha Frequency Brainwaves. Innovative Eggz LLC, 2019.

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5

Peebles, Karen. Death Is the Daybreak: The Discovery of Alpha Brainwaves. PublishAmerica, 2006.

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6

Wise, Anna. High Performance Mind: Mastering Brainwaves for Relaxation, Insight, Healing and Creativity. Relaxation Company, 1998.

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7

Thompson, Dr Jeffrey. Brainwave Symphony: Alpha. The Relaxation Company, 1999.

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8

Thompson, Jeffrey. Alpha (Brainwave Suite). Relaxation Company, 2000.

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9

Thompson, Jeffrey. Alpha-Theta (Brainwave Suite). Relaxation Company, 2000.

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10

Wise, Anna. High-Performance Mind: Brainwave Development Exercises. Relaxation Company, 1998.

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Capítulos de libros sobre el tema "Alpha brainwaves"

1

Empson, Jacob. "The Alpha Rhythm, Other Brainwaves and States of Mind." In Human Brainwaves. Macmillan Education UK, 1986. http://dx.doi.org/10.1007/978-1-349-18312-8_2.

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2

Komini, Asimina, Ioulia Kokka, Dimitrios Vlachakis, George P. Chrousos, Christina Kanaka-Gantenbein, and Flora Bacopoulou. "A Systematic Review on the Adult Alpha Brainwave Activity After Essential Oil Inhalation." In Advances in Experimental Medicine and Biology. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31986-0_53.

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Kanna, R. Kishore, R. Gomalavalli, Yamuna Devi, and A. Ambikapathy. "Computational Cognitive Analysis for Intelligent Engineering Using EEG Applications." In Advances in Civil and Industrial Engineering. IGI Global, 2023. http://dx.doi.org/10.4018/979-8-3693-0044-2.ch017.

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NFT increases EEG's higher alpha band to improve working memory. Five sessions of visual cue feedback instructed patients. Single-channel EEGs collect EEG signals. Each participant's unique alpha frequency band calculated the Higher Alpha band. LabVIEW programme extracted the higher alpha band (10–13hz) signal. The patient was then encouraged to relax by watching the device's nature. Thus, higher alpha waves predominate Relaxation creates alpha waves. Thus, the NFT retrains the brain to make alpha waves on its own and boosts activity. Participants learned and increased alpha band amplitude. Ne

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4

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 deox

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5

J., Shanthalakshmi Revathy, Uma Maheswari N., and Sasikala S. "Enhanced BiLSTM Model for EEG Emotional Data Analysis." In Principles and Applications of Socio-Cognitive and Affective Computing. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3843-5.ch005.

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Emotion recognition based on biological signals from the brain necessitates sophisticated signal processing and feature extraction techniques. The major purpose of this research is to use the enhanced BiLSTM (E-BiLSTM) approach to improve the effectiveness of emotion identification utilizing brain signals. The approach detects brain activity that has distinct characteristics that vary from person to person. This experiment uses an emotional EEG dataset that is publicly available on Kaggle. The data was collected using an EEG headband with four sensors (AF7, AF8, TP9, TP10), and three possible

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Zafar Sherin. "Parallel Computing Contrive Optimized NFB Through QEEG & LENS Approach." In Advances in Parallel Computing. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-814-3-453.

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Neuro-feedback (NFB) has been considered as the cognitive science to retrain brainwave patterns. The occurrence of various brain waves take place at different frequencies, some fast, some quite slow called as delta (0.5&ndash;3.5 Hz very slow high amplitude experienced in deep restorative sleep), theta (4&ndash;8 Hz represents daydream correlated with mental inefficiency a twilight zone between waking and sleep), alpha (8&ndash;12 Hz, slower and larger correlated with a state of relaxation), beta (13&ndash;30 Hz, fast brainwave correlated with intellectual activity) and gamma (

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Actas de conferencias sobre el tema "Alpha brainwaves"

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Makada, Tasnim, Danyal Ozair, Mudassir Mohammed, and Cheryl Abellanoza. "Enhancing Memory Retention by Increasing Alpha and Decreasing Beta Brainwaves using Music." In PETRA '16: 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments. ACM, 2016. http://dx.doi.org/10.1145/2910674.2935851.

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Gupta, Anushka, Esther Ramdinmawii, and V. K. Mittal. "Significance of alpha brainwaves in meditation examined from the study of binaural beats." In 2016 International Conference on Signal Processing and Communication (ICSC). IEEE, 2016. http://dx.doi.org/10.1109/icspcom.2016.7980629.

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3

Staboulis, Miltiadis, and Irene Lazaridou. "APPLICATION OF ALPHA AND BETA BRAINWAVES ON E-LEARNING PROJECTS IN TERMS OF EXPANDING CRITICAL AND COGNITIVE SKILLS: AN EXPERIMENTAL APPROACH." In International Conference on e-Learning 2019. IADIS Press, 2019. http://dx.doi.org/10.33965/el2019_201909c043.

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Li, Yan. "Does alpha brainwave music really work?" In 2012 5th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2012. http://dx.doi.org/10.1109/bmei.2012.6513012.

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Cimpanu, Corina, Tiberius Dumitriu, and Florina Ungureanu. "INSTRUCTIONAL DESIGN BASED ON THE ASSESSMENT OF COGNITIVE LOAD AND WORKING MEMORY LOAD." In eLSE 2018. Carol I National Defence University Publishing House, 2018. http://dx.doi.org/10.12753/2066-026x-18-078.

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Instruction is usually designed based on the cognitive load theory. Human cognition can be portrayed as a native information processing system while handling biologically secondary information, which works similarly to the information processing during evolution by natural selection. There is a critical aspect to be taken into account assuming that human cognition does not explicitly evolve to learn topics that require domain-specific knowledge. This characteristic of human cognition strongly related to the capacity of working memory is essential for the development of a germane architecture t

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Puzi, N. S. M., R. Jailani, H. Norhazman, and N. M. Zaini. "Alpha and Beta brainwave characteristics to binaural beat treatment." In 2013 IEEE 9th International Colloquium on Signal Processing & its Applications (CSPA). IEEE, 2013. http://dx.doi.org/10.1109/cspa.2013.6530069.

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Andrian, Yoga Fingki, Prihartini Widiyanti, and Franky Chandra Satria Arisgraha. "Alpha brainwave stimulator using light and binaural beats stimulation." In THE 2ND INTERNATIONAL CONFERENCE ON PHYSICAL INSTRUMENTATION AND ADVANCED MATERIALS 2019. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0034988.

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Eroğlu, Günet. "Early EEG Biomarkers of Dyslexia: An AI-Driven Discovery from First-Session Recordings." In The Second International Conference of AI new Technology and open Discussion. Algorithm Lab, 2025. https://doi.org/10.63211/j.p.25.645311.

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Using artificial intelligence (AI) and quantitative EEG (qEEG) data, this paper analyses early electrophysiological signs of dyslexia. Drawing from first-session EEG recordings of 208 youngsters labelled as either dyslexic or neurotypical, we found statistically significant variations in particular brainwave characteristics. Analysis aided by machine learning found strong distinction (p < 0.001) for characteristics including beta1 power at O1 (B1_O1), alpha power at O1 (A_O1), and gamma power at P7 (G_P7). These results imply that early screening of dyslexia may be diagnosed even with a sin

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Zainuddin, Balkis Solehah, Zakaria Hussain, and Iza Sazanita Isa. "Alpha and beta EEG brainwave signal classification technique: A conceptual study." In 2014 IEEE 10th International Colloquium on Signal Processing & its Applications (CSPA). IEEE, 2014. http://dx.doi.org/10.1109/cspa.2014.6805755.

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Kadir, Ros Shilawani S. Abdul, Noor Atisha Aliyasak, Zunairah Hj Murat, and Rozita Jailani. "Preliminary Study: The Comparison between Hands-Free and Handheld 3G Mobile Phone on Alpha Brainwave." In 2012 3rd International Conference on Intelligent Systems, Modelling and Simulation (ISMS). IEEE, 2012. http://dx.doi.org/10.1109/isms.2012.72.

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