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Neurofeedback has been used in several countries (UK, Canada, …) for many years

What is Neurofeedback

Neurofeedback (NFB) is a non invasive method to measure brain waves to improve brain with training.
The comprehension and translation of the brain waves emitted by the users and detected by a non-invasive head mounted ElectroEncephaloGraphic sensor (EEG) are part of a NFB process.

An explanation of Neurofeedback
[Picture elaborated from here]

Neurofeedback has been proved a clinically effective, non-invasive, non-pharmaceutical treatment method for a host of health challenges; using “brain games”, NFB improves cognition and better everyday activities. A typical NFB training session is represented in the below figure where EEG is performed by a helmet.

Where neurofeedback is used

Some application of Neurofeedback techniques are:

Supporting people to Take fast and effective decisions under pressure of  continuous or sudden requests

Improving company executives’ peak performances

Taking in charge of situations and  control all their details

Better pro athletes performances

What is Neurofeedback

Neurofeedback (NFB) is a non invasive method to measure brain waves to improve brain with training.
The comprehension and translation of the brain waves emitted by the users and detected by a non-invasive head mounted ElectroEncephaloGraphic sensor (EEG) are part of a NFB process.

An explanation of Neurofeedback
[Picture elaborated from here]

Neurofeedback has been proved a clinically effective, non-invasive, non-pharmaceutical treatment method for a host of health challenges; using “brain games”, NFB improves cognition and better everyday activities. A typical NFB training session is represented in the below figure where EEG is performed by a helmet.

Where neurofeedback is used

Some application of Neurofeedback techniques are:

Supporting people to Take fast and effective decisions under pressure of  continuous or sudden requests

Improving company executives’ peak performances

Taking in charge of situations and  control all their details

Better pro athletes performances

Some useful academic resources

Set My Brain is based on 50 years of scientific research in the field of neuroscience. For further information on the most recent research, please find below an essential bibliography on publications concerning Neurofeedback and its use for the evaluation of cognitive processes and the improvement of Peak Performance
  • Chung-Yen Liao, Rung-Ching Chen, Shao-Kuo Tai (2018). Emotion stress detection using EEG signal and deep learning technologies. 2018 IEEE International Conference on Applied System Invention (ICASI).
  • Davidson, R.J. (1992). Anterior cerebral asymmetry and the nature of emotion. Brain and Cognition factors. Psychophysiology, 35, 389-404.
  • Doppelmayr M., Weber E. (2011). Effects of SMR and Theta/Beta Neurofeedback on Reaction Times, Spatial Abilities, and Creativity, Journal of Neurotherapy, 15 (2), 115-129.
  • Gruzelier, J.H (2014). EEG-neurofeedback for optimising performance. A review of cognitive and affective outcome in healthy participants, Neuroscience & Biobehavioral Reviews, 44 124-141
  • Hammond, D.C. (2007). What is neurofeedback? Journal of Neurotherapy, 10 (4), 25-36.
  • Hao, Y., et al. (2014). A visual feedback design based on a Brain-Computer Interface to assist users regulate their emotional state. CHI ’14 Extended Abstracts on Human Factors in Computing Systems, 2491-2496, doi:10.1145/2559206.2581132.
  • Katie, C., Aidan, S., Ian, P. & Dave, M. (2010). Evaluating a brain-computer interface to categorise human emotional response. Proc. 10th IEEE International Conference on Advanced Learning Technologies, 276-278.
  • Katona, J., Farkas, I., Ujbanyi, T., Dukan, P., Kovari, A. (2014). Evaluation Of The Neurosky MindFlex EEG Headset Brain Waves Data. Proc. 12th IEEE International Symposium on Applied Machine Intelligence and Informatics, 91-94.
  • LeDoux J. (1996). The emotional brain. Phoenix, New York.
  • Lim, C.A. & Chia, W.C. (2015). Analysis of single-electrode EEG rhythms using MATLAB to elicit correlation with cognitive stress. International Journal of Computer Theory and Engineering, 7, 149-155, doi:10.7763/IJCTE.2015.V7.947.
  • Liu Y., Sourina O., Nguyen M.K. (2011). Real-Time EEG-Based Emotion Recognition and Its Applications. In: Gavrilova M.L., Tan C.J.K., Sourin A., Sourina O. (eds) Transactions on Computational Science XII. Lecture Notes in Computer Science, vol 6670. Springer, Berlin, Heidelberg, doi:10.1007/978-3-642-22336-5_13
  • Riera A., Soria-Frisch A., Albajes-Eizagirre A., Cipresso P., Grau C., Dunne S., Ruffini G. (2012). Electro-Physiological Data Fusion for Stress Detection. Studies in Health Technology and Informatics, Volume 181, Annual Review of Cybertherapy and Telemedicine, 228-232 , doi:10.3233/978-1-61499-121-2-228
  • Thut, G., Schyns, P.G. & Gross, J. (2011). Entrainment of perceptually relevant brain oscillations by non-invasive rhythmic stimulation of the human brain. Frontiers of Psychology, 170 (2). doi: 10.3389/ fpsyg.2011.00170.
  • Wilson V., Moss D., Peper E. (2006).  “The Mind Room” in Italian soccer training: The use of biofeedback and neurofeedback for optimum performance, Biofeedback, 34 (3), 79-81.
  • Zoefel, B., et al., (2011). Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance, NeuroImage, 54 (2), 1427-1431

Some useful academic resources

Set My Brain is based on 50 years of scientific research in the field of neuroscience. For further information on the most recent research, please find below an essential bibliography on publications concerning Neurofeedback and its use for the evaluation of cognitive processes and the improvement of Peak Performance
  • Chung-Yen Liao, Rung-Ching Chen, Shao-Kuo Tai (2018). Emotion stress detection using EEG signal and deep learning technologies. 2018 IEEE International Conference on Applied System Invention (ICASI).
  • Davidson, R.J. (1992). Anterior cerebral asymmetry and the nature of emotion. Brain and Cognition factors. Psychophysiology, 35, 389-404.
  • Doppelmayr M., Weber E. (2011). Effects of SMR and Theta/Beta Neurofeedback on Reaction Times, Spatial Abilities, and Creativity, Journal of Neurotherapy, 15 (2), 115-129.
  • Gruzelier, J.H (2014). EEG-neurofeedback for optimising performance. A review of cognitive and affective outcome in healthy participants, Neuroscience & Biobehavioral Reviews, 44 124-141
  • Hammond, D.C. (2007). What is neurofeedback? Journal of Neurotherapy, 10 (4), 25-36.
  • Hao, Y., et al. (2014). A visual feedback design based on a Brain-Computer Interface to assist users regulate their emotional state. CHI ’14 Extended Abstracts on Human Factors in Computing Systems, 2491-2496, doi:10.1145/2559206.2581132.
  • Katie, C., Aidan, S., Ian, P. & Dave, M. (2010). Evaluating a brain-computer interface to categorise human emotional response. Proc. 10th IEEE International Conference on Advanced Learning Technologies, 276-278.
  • Katona, J., Farkas, I., Ujbanyi, T., Dukan, P., Kovari, A. (2014). Evaluation Of The Neurosky MindFlex EEG Headset Brain Waves Data. Proc. 12th IEEE International Symposium on Applied Machine Intelligence and Informatics, 91-94.
  • LeDoux J. (1996). The emotional brain. Phoenix, New York.
  • Lim, C.A. & Chia, W.C. (2015). Analysis of single-electrode EEG rhythms using MATLAB to elicit correlation with cognitive stress. International Journal of Computer Theory and Engineering, 7, 149-155, doi:10.7763/IJCTE.2015.V7.947.
  • Liu Y., Sourina O., Nguyen M.K. (2011). Real-Time EEG-Based Emotion Recognition and Its Applications. In: Gavrilova M.L., Tan C.J.K., Sourin A., Sourina O. (eds) Transactions on Computational Science XII. Lecture Notes in Computer Science, vol 6670. Springer, Berlin, Heidelberg, doi:10.1007/978-3-642-22336-5_13
  • Riera A., Soria-Frisch A., Albajes-Eizagirre A., Cipresso P., Grau C., Dunne S., Ruffini G. (2012). Electro-Physiological Data Fusion for Stress Detection. Studies in Health Technology and Informatics, Volume 181, Annual Review of Cybertherapy and Telemedicine, 228-232 , doi:10.3233/978-1-61499-121-2-228
  • Thut, G., Schyns, P.G. & Gross, J. (2011). Entrainment of perceptually relevant brain oscillations by non-invasive rhythmic stimulation of the human brain. Frontiers of Psychology, 170 (2). doi: 10.3389/ fpsyg.2011.00170.
  • Wilson V., Moss D., Peper E. (2006).  “The Mind Room” in Italian soccer training: The use of biofeedback and neurofeedback for optimum performance, Biofeedback, 34 (3), 79-81.
  • Zoefel, B., et al., (2011). Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance, NeuroImage, 54 (2), 1427-1431
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