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Therapeutic Applications of BCI Technologies
Published in Chang S. Nam, Anton Nijholt, Fabien Lotte, Brain–Computer Interfaces Handbook, 2018
Neurofeedback (also referred to as neurotherapy or biofeedback) has been used for many years to treat a large assortment of conditions including attention-deficit/hyperactivity disorder (ADHD; Lubar & Shouse 1976), depression (Hammond 2005), substance use disorders (Trudeau 2005), insomnia (Hammer et al. 2011), autism (Friedrich et al. 2015), and stroke (Bearden et al. 2003; Reichert et al. 2016). Neurofeedback involves providing feedback in the form of some visual or auditory stimulus based on some predetermined EEG feature (Micouland-Franchi et al. 2015). As noted above, the patient’s task is only to regulate the specific brain signal and no additional behavior is required. Thus, there is no specific context in which neurofeedback occurs, other than a therapist’s office. As a result, neurofeedback protocols implicitly assume that the effects of training persist as a permanent change in brain state that is sustained beyond the therapeutic context.
Stress Management: Concept
Published in Arvind K. Birdie, Employees and Employers in Service Organizations, 2017
Neurofeedback is feedback about the brain activity which is measured through the sensors being placed on the scalp. This can be given through the video or sound and the positive activity and negative activity can be differentiated by different colors or sounds. The aim of this kind of feedback is to self-regulate the brain activity.
Brain–Computer Interface Games Based on Consumer-Grade EEG Devices: A Systematic Literature Review
Published in International Journal of Human–Computer Interaction, 2020
Gabriel Alves Mendes Vasiljevic, Leonardo Cunha de Miranda
The theta, alpha and beta waves, for example, are related to certain brain states. Theta and alpha rhythms are stronger and beta waves are weaker when the user is in a relaxation or meditation state, while beta rhythms are stronger and alpha and theta rhythms are weaker when the user is in a state of concentration or attention. These changes in the brain waves can be used to measure the user’s brain state and are frequently employed as a control signal to BCI systems, as the user can learn to self-regulate his/her brain state through a process known as neurofeedback (Coben & Evans, 2011; Moriyama et al., 2012). Various metrics can be used for such measure, such as the beta/alpha and theta/beta ratios (Liu, Hou, & Sourina, 2015; Vernon et al., 2009), the wave’s entropy (e.g., sample entropy and fractal dimension) (Ming et al., 2009; Thomas, Vinod, & Guan, 2013a) or the wave’s raw power spectrum (Wang & Larsen, 2012).
Differentiation among bio- and augmented- feedback in technologically assisted rehabilitation
Published in Expert Review of Medical Devices, 2021
Giovanni Morone, Sheida Ghanbari Ghooshchy, Angela Palomba, Alessio Baricich, Andrea Santamato, Chiara Ciritella, Irene Ciancarelli, Franco Molteni, Francesca Gimigliano, Giovanni Iolascon, Pierluigi Zoccolotti, Stefano Paolucci, Marco Iosa
Neurofeedback is a kind of biofeedback, which teaches self-control of neural functions to subjects by measuring neural activity and providing a feedback signal. Given the difficulties in measuring nerve activity, most of neurofeedback systems are based on the measures of brain waves to provide an audio or video feedback, giving positive or negative feedback for desirable or undesirable brain activities, respectively [46,47].