On this page you will find several Neurofeedback Resources from different studies we have conducted in the past. These are freely available, however these are unsupported and not for clinical use. Look here for a review on neurofeedback.
Real-time Filtering in BioExplorer
This document explains various filter types, their strengths and weaknesses and other differences. Filters explained are the FIR (Finite Impulse Response) and IIR (Infinite Impulse Response) filters such as the Butterworth, Chebyshev and Elliptic IIR filters. These filters are embedded in the real-time feedback software BioExplorer, but these filters are also found in other EEG analysis software tools.
Slow Cortical Potentials (SCP) and Galvanic Skin Response (GSR) Biofeedback
In 2008 we carried out a study funded by the Bial Foundation investigating Slow Cortical Potential (SCP) neurofeedback, Galvanic Skin Response (GSR) Biofeedback and discrete SMR Neurofeedback. In this study we aimed to compare these 3 feedback modalities with an identical design in healthy volunteers, and when providing feedback on 1 parameter, also measure the other 2 parameters at the same time, to investigate if for example SCP neurofeedback also results in changes in GSR and SMR at the same time. Furthermore, it was also investigated how well these feedback modalities could be used for Brain Computer Interfaces (BCI). The rationale for this study was that all 3 modalities have been reported to have anticonvulsant effects which suggested an overlap in underlying neurophysiological circuitry. These results have been published by Spronk et al (2010) and Kleinnijenhuis et al (2008) and can be downloaded here
You can download the BioExplorer designs and manual from this study (can be used with the PET EEG). Note that for the SCP Neurofeedback a modified electrode configuration was used with a linked-mastoid montage (A1+A2/2) in order to reduce the effects of lateral eye movements.
Spronk, D., Kleinnijenhuis, M., Luijtelaar, G., & Arns, M. (2010). Discrete-Trial SCP and GSR training and the interrelationship between central and peripheral arousal. Journal of Neurotherapy, 14(3), 217-228.
Kleinnijenhuis, M., Arns, M. W., Spronk, D. B., Breteler, M. H. M., & Duysens, J. E. J. (2008). Comparison of discrete-trial based SMR and SCP training and the interrelationship between SCP and SMR networks: Implications for brain-computer interfaces and neurofeedback. Journal of Neurotherapy, 11(4), 19-35.
Neurofeedback for ADHD Meta-analysis
In 2009 we published a meta-analysis on Neurofeedback treatment for ADHD. In this study we incorporated all literature investigating neurofeedback in the treatment of ADHD up to 2009 and performed a meta-analysis. You can download the excel sheet with all data for individual studies, means, SDs, sample sizes and the within subject effect sizes (ES).
Personalized Medicine in ADHD and Depression
Download the full PDF of the PhD thesis by Martijn Arns on ‘Personalized Medicine in ADHD and Depression: A quest for EEG treatment predictors’ from our bookstore, it’s free of charge!. This thesis covers neuromodulation techniques such as neurofeedback and rTMS, as well as medication. Furthermore, there is an extensive introduction and discussion on the value of EEG and quantitative EEG (QEEG) in predicting treatment outcome to these different treatments. Current insights on personalized medicine can be found here.
PET Sport – Real-life Neurofeedback in Golf
In 2008 we conducted a study investigating the EEG during golf-putting on a golf course. We overlayed the EEG signals in a video of the putting in slow motion, where a piezo-sensor was used to measure the impact of the golf-ball on the golf club. These videos can be found on Youtube.
When montaging the video we noticed that we could almost predict which ball was successfully put and which one was not, by the appearance of an alpha-burst 0.5-1.0 sec. before the put, also see the figure below with representative examples of 2 successful puts (right) and 2 unsuccessful puts (left). This led us to the idea to repeat this exercise in a larger group, and to also test if golf-pro’s could learn this.
Therefore, we sought to replicate this in a larger sample and also investigate if golf-pros could learn this. See our publication (2008) here
In this study we obtained personalized performance profiles by inspecting the averaged event related EEG before and after successful vs. unsuccessful puts. Interestingly, the EEG power related to successful puts was different for all participants. Below see the figure with the results obtained in a controlled ABAB design where participants were putting with no-feedback (A) and with-feedback (B). Interestingly in session 2 their putting performance improved with 25% (considering the chance level of 50%) as a result of the provided feedback. Note that this refers to ‘real-life neurofeedback’ since the feedback was providedduring the putting performance. We hypothesized this process could be more related to classical rather than operant conditioning.