The goal of EEG neurofeedback (EEG-NFB) training is to induce changes in the power of targeted EEG bands to produce beneficial changes in cognitive or motor function. The effectiveness of different EEG-NFB protocols can be measured using two dependent variables: (1) changes in EEG activity and (2) behavioral changes of a targeted function (for therapeutic applications the desired changes should be long-lasting). To firmly establish a causal link between these variables and the selected protocol, similar changes should not be observed when appropriate control paradigms are used. The main objective of this review is to evaluate the evidence, reported in the scientific literature, which supports the validity of various EEG-NFB protocols. Our primary concern is to highlight the role that uncontrolled nonspecific factors can play in the results generated from EEG-NFB studies. Nonspecific factors are often ignored in EEG-NFB designs or the data are not presented, which means conclusions should be interpreted cautiously. As an outcome of this review we present a do's and don'ts list, which can be used to develop future EEG-NFB methodologies, based on the small set of experiments in which the proper control groups have excluded non-EEG-NFB related effects. We found two features which positively correlated with the expected changes in power of the trained EEG band(s): (1) protocols which focused on training a smaller number of frequency bands and (2) a bigger number of electrodes used for neurofeedback training. However, we did not find evidence in support of the positive relationship between power changes of a trained frequency band(s) and specific behavioral effects.

Mounting evidence indicates that resting-state EEG activity is related to various cognitive functions. To trace physiological underpinnings of this relationship, we investigated EEG and behavioral performance of 36 healthy adults recorded at rest and during visual attention tasks: visual search and gun shooting. All measures were repeated two months later to determine stability of the results. Correlation analyses revealed that within the range of 2-45 Hz, at rest, beta-2 band power correlated with the strength of frontoparietal connectivity and behavioral performance in both sessions. Participants with lower global beta-2 resting-state power (gB2rest) showed weaker frontoparietal connectivity and greater capacity for its modifications, as indicated by changes in phase correlations of the EEG signals. At the same time shorter reaction times and improved shooting accuracy were found, in both test and retest, in participants with low gB2rest compared to higher gB2rest values. We posit that weak frontoparietal connectivity permits flexible network reconfigurations required for improved performance in everyday tasks.One of the main goals of neuroscience is to understand how neuronal activity organizes behavior. To elucidate this relationship, many studies have focused on the putative links between task-related neuronal electrical activity and behavioral performance 1-5 . In parallel, BOLD (blood-oxygen-level-dependent) imaging investigations have revealed that (i) resting-state (spontaneous) connectivity correlates with functionally activated networks 6-14 , and (ii) specifically with the performance of various cognitive functions, such as attention 15 , working memory 16,17 and fluid intelligence 18 . It has been further suggested that such correlations could be explained by the individual characteristics of brain networks 19,20 . However, the uncertain physiological origins of hemodynamic signals 21 provide limited insight into the mechanisms governing the relationship between resting-state and task-related activations and therefore behavioral outcomes.Recording electrophysiological activity is one way that the relationship between resting-state and behavioral performance can be directly assessed. Indeed, several investigations have already revealed correlations between specific EEG bands and different aspects of cognitive performance. For example, in the attentional domain, alpha oscillations have been proposed to clear sensory information from distractors 22 , the beta to gamma band ratio can assure critical-state dynamics for optimal information processing 23 and alpha and beta band activity can reduce attentional investment during rest 24 . Yet, none of the proposed mechanisms have characterized the intrinsic properties of resting-state networks such as power of EEG bands and connectivity, and how they could relate to functional connectivity underlying behavioral performance.We hypothesized that specific electrophysiological signatures of spontaneous, individual EEG activity in large-scale networks would predic...

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