Advances in Electrical Neuroimaging, Brain Networks and Neurofeedback Protocols

Human EEG biofeedback (neurofeedback) started in the 1940s using 1 EEG recording channel, then to 4 channels in the 1990s. New advancements in electrical neuroimaging expanded EEG biofeedback to 19 channels using Low Resolution Electromagnetic Tomography (LORETA) three-dimensional current sources of the EEG. In 2004–2006 the concept of a “real-time” comparison of the EEG to a healthy reference database was developed and tested using surface EEG z-score neurofeedback based on a statistical bell curve called “real-time” z-scores. The “real-time” or “live” normative reference database comparison was developed to help reduce the uncertainty of what threshold to select to activate a feedback signal and to unify all EEG measures to a single value, i.e., the distance from the mean of an age matched reference sample. In 2009 LORETA z-score neurofeedback further increased the specificity by targeting brain network hubs referred to as Brodmann areas. A symptom check list program to help link symptoms to dysregulation of brain networks based on fMRI and PET and neurology was created in 2009. The symptom checklist and NIH based networks linking symptoms to brain networks grew out of the human brain mapping program starting in 1990 which is continuing today. A goal is to increase specificity of EEG biofeedback by targeting brain network hubs and connections between hubs likely linked to the patient’s symptoms. New advancements in electrical neuroimaging introduced in 2017 provide increased resolution of three-dimensional source localization with 12,700 voxels using swLORETA with the capacity to conduct cerebellar neurofeedback and neurofeedback of subcortical brain hubs such as the thalamus, amygdala and habenula. Future applications of swLORETA z-score neurofeedback represents another example of the transfer of knowledge gained by the human brain mapping initiatives to further aid in helping people with cognition problems as well as balance problems and parkinsonism. A brief review of the past, present and future predictions of z-score neurofeedback are discussed with special emphasis on new developments that point toward a bright and enlightened future in the field of EEG biofeedback.

Neurofeedback for Chronic Pain

Neurofeedback is a novel neuromodulatory therapy where individuals are given real-time feedback regarding their brain neurophysiological signals in order to increase volitional control over their brain activity. Such biofeedback platform can be used to increase an individual’s resilience to pain as chronic pain has been associated with abnormal central processing of ascending pain signals. Neurofeedback can be provided based on electroencephalogram (EEG) or functional magnetic resonance imaging (fMRI) recordings of an individual. Target brain rhythms commonly used in EEG neurofeedback for chronic pain include theta, alpha, beta and sensorimotor rhythms. Such training has not only been shown to improve pain in a variety of pain conditions such as central neuropathic pain, fibromyalgia, traumatic brain injury and chemotherapy induced peripheral neuropathy, but has also been shown to improve pain associated symptoms such as sleep, fatigue, depression and anxiety. Adverse events associated with neurofeedback training are often self-limited and resolve with decreased frequency of training. Provision of such training has also been explored in the home setting whereby individuals have been encouraged to practice this as and when required with promising results. Therefore, neurofeedback has the potential to provide low-cost yet holistic approach to the management of chronic pain.

Neuromeditation: The Science and Practice of Combining Neurofeedback and Meditation for Improved Mental Health

Beginning meditators often complain that they do not know if they are “doing it right” or give up before realizing significant benefits. Advanced meditators often reach a plateau and struggle to reach “the next level” of their practice. Modern researchers and practitioners are finding a possible new solution to these challenges by using EEG biofeedback to increase awareness of subtle states of consciousness and speed the learning process. By tracking brainwave activity in specific regions of the brain, we can tell if someone is focused or relaxed. We can tell if the mind is wandering, if they are engaged in body-based emotions, or if they have entered a space of internal quiet. By monitoring this activity and connecting it directly to the intent of the meditation, it is possible to help meditators learn to quickly enter a desired state of consciousness and maintain this state for increasing periods of time. This chapter will describe the early research conducted in this area along with an original case study conducted by the author. In addition, the author will describe the way this technology is being used as a treatment intervention for ADHD, anxiety, depression, and PTSD.

Biofeedback in Clinical Psychology: Modalities and Perspectives

Biofeedback is a technique of self-regulation applied by health professionals in order to reshape a series of physiological information based in health parameters diminishing psychopathological symptoms and improving cognitive performance. The biofeedback technique is widely recognized in many countries, leaving no doubt about its effectiveness and applicability. In clinical psychology, biofeedback has been applied effectively to psychophysiological conditions such as anxiety, depression and ADHD. This chapter has the aim to elucidate the techniques applied to clinical settings, where psychophysiological conditions are more prone to be treated with biofeedback. Moreover, this chapter also evaluates the advances of the technique and possible future directions.

Blockchain-Based Medical Record Management with Biofeedback Information

Blockchain is a new emerging technology of distributed databases, which guarantees the integrity, security and incorruptibility of data by means of the cryptography. Such features are suitable for secure and reliable data storage. This chapter investigates the blockchain-based architecture with applications to medical health record or biofeedback information management. This framework employs the smart contract to establish a medical record management system to ensure the privacy of patients. Moreover, the blockchain technique accelerates the medical record or information exchange such that the cost of human resource is significant reduced. All patients can manage their individual medical records and information easily in the different hospitals and clinics. They also have the privilege to deal with and authorize personal medical records in the proposed management framework.