Neurofeedback training (NFT) of the alpha rhythm has been used for several decades but is still controversial in regards to its trainability and effects on working memory. Alpha rhythm of the frontoparietal region are associated with either the intelligence or memory of healthy subjects and are also related to pathological states. In this study, alpha NFT effects on memory performances were explored. Fifty healthy participants were recruited and randomly assigned into a group receiving a 8-12-Hz amplitude (Alpha) or a group receiving a random 4-Hz amplitude from the range of 7 to 20 Hz (Ctrl). Three NFT sessions per week were conducted for 4 weeks. Working memory was assessed by both a backward digit span task and an operation span task, and episodic memory was assessed using a word pair task. Four questionnaires were used to assess anxiety, depression, insomnia, and cognitive function. The Ctrl group had no change in alpha amplitude and duration. In contrast, the Alpha group showed a progressive significant increase in the alpha amplitude and total alpha duration of the frontoparietal region. Accuracies of both working and episodic memories were significantly improved in a large proportion of participants of the Alpha group, particularly for those with remarkable alpha-amplitude increases. Scores of four questionnaires fell in a normal range before and after NFT. The current study provided supporting evidence for alpha trainability within a small session number compared with that of therapy. The findings suggested the enhancement of working and episodic memory through alpha NFT. Hum Brain Mapp 37:2662-2675, 2016. © 2016 Wiley Periodicals, Inc.

Background: Neurofeedback training (NFT) has recently been proposed as a valuable technique for cognitive enhancement and psychiatric amelioration. However, effect of NFT of alpha activity on memory is controversial. The current study analyzed previous works in terms of randomized and blinded analyses, training paradigms, and participant characteristics to validate the efficacy of alpha NFT on memory in a healthy population.Objectives: A systematic meta-analysis of studies with randomized controlled trials was performed to explore the effect of alpha NFT on working memory (WM) and episodic memory (EM) in a healthy population.Methods: We searched PubMed, Embase, and Cochrane Library from January 1, 1999, to November 30, 2019. Previous studies were evaluated with the Cochrane risk of bias (RoB). A meta-analysis calculating absolute weighted standardized mean difference (SMD) using random-effects models was employed. Heterogeneity was estimated using I2 statistics. Funnel plots and Egger's test were performed to evaluate the quality of evidence.Results: Sixteen studies with 217 healthy participants in the control group and 210 participants in the alpha group met the eligibility criteria. Alpha NFT studies with WM measures presented little publication bias (P = 0.116), and 5 of 7 domains in the Cochrane RoB exhibited a low risk of bias. The overall effect size from 14 WM studies was 0.56 (95% CI 0.31–0.81, P < 0.0001; I2 = 28%). Six EM studies exhibited an effect size of 0.77 (95% CI 0.06–1.49, P = 0.03; I2 = 77%).Conclusion: Meta-analysis results suggest that alpha NFT seems to have a positive effect on the WM and EM of healthy participants. Future efforts should focus on the neurophysiological mechanisms of alpha NFT in memory.

Neurofeedback training (NFT) enables users to learn self-control of EEG activity of interest and then to create many benefits on cognitive function. A considerable number of nonresponders who fail to achieve successful NFT have often been reported in the within-session prediction. This study aimed to investigate successful EEG NFT of upregulation alpha activity in terms of trainability, independence, and between-session predictability validation. Forty-six participants completed 12 training sessions. Spectrotemporal analysis revealed the upregulation success on brain activity of 8–12 Hz exclusively to demonstrate trainability and independence of alpha NFT. Three learning indices of between-session changes exhibited significant correlations with eyes-closed resting state (ECRS) alpha amplitude before the training exclusively. Through a stepwise linear discriminant analysis, the prediction model of ECRS’s alpha frequency band amplitude exhibited the best accuracy (89.1%) validation regarding the learning index of increased alpha amplitude on average. This study performed a systematic analysis on NFT success, the performance of the 3 between-session learning indices, and the validation of ECRS alpha activity for responder prediction. The findings would assist researchers in obtaining insight into the training efficacy of individuals and then attempting to adapt an efficient strategy in NFT success.

Discrimination is an important function in pain processing of the somatic cortex. The involvement of the somatic cortex has been studied using equivalent dipole analysis and neuroimaging, but the results are inconsistent. Scalp electroencephalography (EEG) can reflect functional changes of particular brain regions underneath a lead. However, the responses of EEG leads close to the somatic cortex in response to pain have not been systematically evaluated. The present study applied CO2 laser stimulation to the dorsum of the left hand. Laser-evoked potentials (LEPs) of C4, T3, and T4 leads and pain ratings in response to four stimulus intensities were analyzed. LEPs started earlier at the C4 and T4 leads. The onset latency and peak latency of LEPs for C4 and T4 leads were the same. Only 10 of 22 subjects (45 %) presented equivalent current dipoles within the primary somatosensory or motor cortices. LEP amplitudes of these leads increased as stimulation intensity increased. The stimulus–response pattern of the C4 lead was highly correlated with pain rating. In contrast, an S-shaped stimulus–response curve was obtained for the T3 and T4 leads. The present study provides supporting evidence that particular scalp channels are able to reflect the functional characteristics of their underlying cortical areas. Our data strengthen the clinical application of somatic-cortex-related leads for pain discrimination.