Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance

Zoefel, Benedikt; Huster, René J.; Herrmann, Christoph S.

doi:10.1016/j.neuroimage.2010.08.078

Cited by 419 publications

(427 citation statements)

References 32 publications

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“…Here, a computational model is presented that mimics the results of Zoefel et al (2011). The model consists of an EEG-generator and a feedback-based learning mechanism that based on the feedback it receives adjusts the neural configuration through which the EEG-generator is more likely to produce the target frequency.…”

Section: Simulation Study 1: a Spiking Neuron Model Of Upper Alpha Upmentioning

confidence: 99%

“…Recent work by Legenstein et al (2010) shows how the credit assignment problem can be solved in a physiological plausible way by making fronto-striatal synaptic modification contingent on the covariance between the change in postsynaptic activity and the change in reward signal. The methods used for the EEG analysis are the same as used by Zoefel et al (2011). In short, the sampling frequency is 1 kHz and a moving window consisting of 1024 samples is updated every 100ms.…”

Section: Striatal Learningmentioning

confidence: 99%

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Mechanisms of Neurofeedback: A Computation-theoretic Approach

Davelaar

2018

Neuroscience

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Neurofeedback training is a form of brain training in which information about a neural measure is fed back to the trainee who is instructed to increase or decrease the value of that particular measure. This paper focuses on electroencephalography (EEG) neurofeedback in which the neural measures of interest are the brain oscillations. To date, the neural mechanisms that underlie successful neurofeedback training are still unexplained. Such an understanding would benefit researchers, funding agencies, clinicians, regulatory bodies, and insurance firms. Based on recent empirical work, an emerging theory couched firmly within computational neuroscience is proposed that advocates a critical role of the striatum in modulating EEG frequencies. The theory is implemented as a computer simulation of peak alpha upregulation, but in principle any frequency band at one or more electrode sites could be addressed. The simulation successfully learns to increase its peak alpha frequency and demonstrates the influence of threshold setting -the threshold that determines whether positive or negative feedback is provided. Analyses of the model suggest that neurofeedback can be likened to a search process that uses importance sampling to estimate the posterior probability distribution over striatal representational space, with each representation being associated with a distribution of values of the target EEG band. The model provides an important proof of concept to address pertinent methodological questions about how to understand and improve EEG neurofeedback success.

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Section: Simulation Study 1: a Spiking Neuron Model Of Upper Alpha Upmentioning

confidence: 99%

Section: Striatal Learningmentioning

confidence: 99%

Mechanisms of Neurofeedback: A Computation-theoretic Approach

Davelaar

2018

Neuroscience

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“…Two of the most widely used methods to stimulate certain frequencies are evoking emotions [47] or presenting emotional faces [21]. Pineda et al [33] presented MI-BCI as an alternative, which is relatively simple to perform by participants.…”

Section: Discussionmentioning

confidence: 99%

“…Brain plasticity can be restored into a more normal brain function by means of inducing brain activity [13]. In this regard, neurofeedback training (NFT) has been shown to be an appropriate way to control one's own brain activity [47]. In fact, NFT can lead to neuroplastic changes occurring after NFT [34] and microstructural changes in white and grey matter [18].…”

Section: Introductionmentioning

confidence: 99%

“…Traditional NFT methodology provides feedback by means of LED lights or audio tones of a specific biofeedback apparatus [28,29], changes in a display as the saturation of a colored square [47] or changing the height of a colored bar [46]. We hypothesized that the proposed method (MI-BCI) could be a possible alternative to the traditional NFT approach, avoiding the use of conventional apparatus to provide feedback.…”

Section: Introductionmentioning

confidence: 99%

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