Neurofeedback is a method that uses real-time brain activity to provide individuals with feedback on their own brain function. Brain activity is often measured with electroencephalography (EEG). The goal of neurofeedback is to help individuals gain more control over their brain function. In this process, the user is provided with positive feedback for desirable brain activity and negative feedback for undesirable brain activity. This feedback mechanism is hypothesized to enable users to train and regulate their brain activity over time1–5,7.

What is the history of neurofeedback?

Neurofeedback has been a subject of research since around 1960. During this time, however, neurofeedback did not prove to be effective, and interest in the technology waned. Towards the beginning of the 2000s, there was a resurgence of interest in neurofeedback, largely due to the advancements in EEG technology4. Following the global health crisis caused by the coronavirus pandemic in 2019, there has been a marked increase in interest in neurofeedback. This is because the coronavirus pandemic has brought renewed attention to effective mental health care, and there is a possibility that neurofeedback might be able to help with this5.

What are the main steps in neurofeedback?

There are five main steps in neurofeedback1,2, which can be seen in Figure 1.

Neurofeedback TMSi

Figure 1: The five main steps in neurofeedback1,2.

The first step in neurofeedback is to collect data on brain activity. This is often done with EEG, as it is accessible for most researchers and can be used anywhere. Other techniques, such as magnetic resonance imaging (MRI), magnetoencephalography (MEG) or functional near-infrared spectroscopy (fNIRS), can also be used, although these techniques are either not widely accessible or have a lower temporal resolution1.

The second step in neurofeedback is the preprocessing of the data, which includes the filtering of the data.It may be helpful to filter out external artifacts,  such as power interference (50/60 Hz), or physiological artifacts , such as eye blinks1.

The third step is feature extraction, during which the signal of interest is extracted from the pre-processed EEG signal. The signal of interest is based on neuroscientific insights into the specific target and its associated brain region. One of the most commonly used forms of neurofeedback is frequency band training. Here, the frequencies are divided into bands, which are most often categorized as delta, theta, alpha, beta and gamma1,2. For more information on these bands, we would like to refer you to this blog. The aim of the frequency band training is to alter the power of these bands, which is most often calculated with the Fast Fourier Transform (FFT)1,2,6 .

The fourth element of neurofeedback is the feedback itself and can be given visually, auditory, and/or tactile. It is necessary to set a threshold of the parameter of interest to give positive and/or negative feedback on1,2,4. It is possible to set the threshold individually, for example by measuring a resting state. Alternatively, it can also be based on groups, for example by comparing it to the mean of a control group of healthy individuals1,2.

The fifth and last step in the process of neurofeedback is the learner. Neurofeedback involves the individual taking an active role in modifying their own brain function in line with the feedback provided1,2.

Who may benefit from neurofeedback?

Neurofeedback has the potential to be employed in several different areas, including the treatment of certain neurological disorders such as epilepsy and attention deficit hyperactivity disorder (ADHD) 1–5. In addition, cognitive decline and mental health issues such as anxiety and depression are also topics of interest4,5. For example, frequency band training can be used for the treatment of ADHD, where the protocol is to increase the beta band or decrease the theta/beta ratio. This is because people with ADHD can have a reduced beta activity and increased theta activity7. However, the overall conclusion about the effectiveness of neurofeedback in the treatment of these diseases is not yet clear, largely due to the diversity in research designs and research limitations1,2,4,5.

What equipment do you need for neurofeedback?

As researchers continue to explore this field, a number of clinics have already implemented neurofeedback. Special equipment can be used to incorporate the five main steps of neurofeedback described above. It is important for systems that the latency between the measured signal and the feedback signal does not exceed 400 milliseconds. Otherwise, the resulting feedback may occur too late to be effective1. In addition, a low input referred noise (below 1 µV, as this is the lowest EEG amplitude8) and a high common mode rejection ratio (CMRR) (above 80 dB at 50/60 Hz9) are requirements for a neurofeedback amplifier to enhance the quality of the real EEG signal. 

Conclusion

To conclude, neurofeedback is a technique that employs real-time EEG signals to provide individuals with feedback. The aim of neurofeedback is to help individuals gain greater control over their brain function. It is a method that has been the subject of study since the 1960s, but it has only recently gained more interest, particularly in the wake of the coronavirus pandemic. There are five main steps in neurofeedback and it is thought that neurofeedback might be beneficial for neurological disorders, such as ADHD, as well as mental disorders.

TMSi’s APEX is a compact, high-quality EEG amplifier that can measure 24- or 32-channel EEG for Neurofeedback purposes. Learn more about APEX on this page. Alternatively, TMSi collaborates with MindMedia which has specialized systems for neurofeedback.

References

  1. Huster, R. J., Mokom, Z. N., Enriquez-Geppert, S., & Herrmann, C. S. (2014). Brain–computer interfaces for EEG neurofeedback: Peculiarities and solutions. International Journal Of Psychophysiology, 91(1), 36–45. https://doi.org/10.1016/j.ijpsycho.2013.08.011
  2. Enriquez-Geppert, S., Huster, R. J., & Herrmann, C. S. (2017). EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial. Frontiers in Human Neuroscience, 11. https://doi.org/10.3389/fnhum.2017.00051
  3. Viviani, G., & Vallesi, A. (2021). EEG‐neurofeedback and executive function enhancement in healthy adults: A systematic review. Psychophysiology, 58(9). https://doi.org/10.1111/psyp.13874
  4. Omejc, N., Rojc, B., Battaglini, P. P., & Marusic, U. (2018). Review of the therapeutic neurofeedback method using electroencephalography: EEG Neurofeedback. Bosnian Journal Of Basic Medical Sciences. https://doi.org/10.17305/bjbms.2018.3785
  5. Patil, A. U., Lin, C., Lee, S., Huang, H., Wu, S., Madathil, D., & Huang, C. (2023). Review of EEG-based neurofeedback as a therapeutic intervention to treat depression. Psychiatry Research Neuroimaging, 329, 111591. https://doi.org/10.1016/j.pscychresns.2023.111591
  6. Hossain, F., & Yaacob, H. (2022). Review on Signal Generation for Neurofeedback. 2022 10th International Conference On Cyber And IT Service Management (CITSM). https://doi.org/10.1109/citsm56380.2022.9935866
  7. Marzbani, H., Marateb, H., & Mansourian, M. (2016). Methodological Note: Neurofeedback: A Comprehensive Review on System Design, Methodology and Clinical Applications. Basic And Clinical Neuroscience Journal, 7(2). https://doi.org/10.15412/j.bcn.03070208
  8. Harrison, Reid. (2007). A Versatile Integrated Circuit for the Acquisition of Biopotentials. Proceedings of the Custom Integrated Circuits Conference. 115 - 122. 10.1109/CICC.2007.4405694
  9. MettingVanRijn, A. C., Peper, A., & Grimbergen, C. A. (1994). Amplifiers for bioelectric events: A design with a minimal number of parts. Medical & Biological Engineering & Computing, 32(3), 305–310. https://doi.org/10.1007/bf02512527