Introduction. Kava (Piper methysticum) is a culturally significant Pacific keystone species traditionally consumed as a water-based beverage. Global demand has led to commodification and misrepresentation, with kava often diverging from traditional forms. Evidence on the physiological and therapeutic effects of traditionally prepared beverage kava remains limited, while conflation with nonkava products risks obscuring cultural meaning and safety. This study addresses these gaps. Methods. A pilot electroencephalography (EEG) study investigated neurophysiological effects of traditionally prepared kava in culturally authentic settings. Two experienced adult male users were observed over a 6-hr session. Resting-state EEG was recorded pre- and postconsumption with the EMOTIV Insight 5-channel EEG headset. Results. EEG findings showed divergent responses. One participant displayed increased alpha and theta activity consistent with relaxation, while the other showed elevated gamma power linked to cognitive focus. These differences may reflect individual habituation or cultural use. Results highlight the need for larger studies connecting EEG data with behavioral measures to explore the ethnopsychopharmacology of traditional kava. Conclusion. This pilot study provides preliminary evidence that traditionally prepared kava produces measurable neurophysiological effects aligned with its cultural role as a calming, relational substance. The study underscores the value of culturally grounded, rigorous research on kava.
Proper upper limb (UL) function is very crucial for manual exploration and manipulation of the environment. Dexterity is affected after stroke and takes times to recover. Effective and economical treatment approaches are desired to overcome the numerous challenges associated with UL rehabilitation. This review aims at establishing the effectiveness of various therapeutic currents on hand function in individuals with stroke. A search was conducted in three databases for randomized controlled trials published from inception to November 2024. The methodological quality of the included studies was measured by the PEDro scale. Cochrane risk-of-bias tool for randomized trials (RoB 2) was used to assess the risk of bias. Meta-analysis was performed on the studies providing sufficient and complete data. The search identified 334 records from which 12 records met the eligibility criteria. The average PEDro score was 6.92. Majority of the trials presented with low risk of bias. Meta-analysis of functional electrical stimulation (FES) showed no significant effects of FES on dexterity (SMD = 2.03, Z = 1.81, p = .07). Meta-analysis of FES trials identified a small effect size which, while not significant, warrants further investigation. Large and more robust trials are needed with larger sample size to draw more definite conclusions.
Previous studies have shown that the brain processes familiar and unfamiliar music differently, yet there is a lack of EEG analysis focusing on active rhythm tasks during music listening. Our study aims to address this gap by investigating EEG responses to familiar and unfamiliar music while participants engage in a rhythm game within a virtual reality environment. We utilized a commercially available four-electrode headband to collect EEG data from 10 healthy subjects during experiments. Participants played the rhythm game Beat Saber, using virtual sabers to match the beat of the music. This experiment employed a matched pair design, with each subject serving as their own control in EEG comparisons. EEG data were categorized into delta, theta, alpha, beta, and gamma frequency bands, and power within each band was analyzed to discern patterns across trials. Our findings revealed significant differences in how the brain processed familiar versus unfamiliar music across both audio-only and virtual reality settings. These changes occurred predominantly on the right side of the brain, suggesting hemispheric specialization in music processing. Overall, our study contributes new insights into neural dynamics underlying music perception during active engagement, highlighting distinct EEG responses to familiar and unfamiliar music across sensory contexts.
The raga system of Indian classical music has long been associated with emotional and cognitive regulations, but its effect on large-scale brain networks is still not adequately explored. This study sought to examine the impact of Raga Kirwani on resting-state brain dynamics using EEG microstate analysis. A within-subject approach was utilized with 10 healthy adult volunteers (M = 20.5 years, SD = 3.32). EEG data were acquired prior to and after a 5-min listening session of Raga Kirwani. Microstate characteristics, such as mean duration, occurrence, coverage, global explained variance (GEV), and transition probabilities, were obtained using the MICROSTATE toolbox in EEGLAB. Results indicated a decline in coverage of microstate B, reduced transition from C to B, and increased transition from C to D—a reduction in visual-spatial processing and an increase in executive activities. Although early, these findings offer basic evidence that Indian classical music may in fact function as a culturally ingrained instrument of mental alignment and control. Subsequent research with larger sample size and control is needed to expand upon these findings.
Introduction. This study investigated whether personalized transcranial direct current stimulation (tDCS) protocols informed by quantitative EEG (qEEG) patterns could enhance treatment outcomes in adults who stutter (AWS), addressing individual variability in neural activity. Methods. Twenty male AWS participated in a double-blind, randomized controlled trial. EEG signals were recorded during speech tasks to differentiate neural substrates of fluent and stuttered speech. Over 10 days, participants received 10 sessions of speech therapy combined with tDCS. The experimental group received personalized tDCS (2 mA for 25 min per session) targeting regions identified through qEEG, while the control group received standard stimulation over FC5. Behavioral outcomes (SSI-4 scores) and EEG metrics were compared pretreatment, posttreatment, and at 3-month follow-up using repeated-measures ANOVA. Results. Both groups exhibited significant reductions in stuttering severity posttreatment. However, the study group maintained greater fluency at follow-up (p < .001). EEG analysis revealed that the study group demonstrated enhanced delta power in the FC5, reduced phase coherence in motor-auditory-somatosensory networks, and greater suppression of high-frequency bands in the personalized group. Conclusion. Personalized, qEEG-informed tDCS protocols yielded more sustainable fluency improvements than conventional tDCS, highlighting the potential of individualized neuromodulation strategies for treating stuttering.
Background. The combination of meditation and EEG neurofeedback has gained attention as a nonpharmacological approach for emotion regulation, stress reduction, and cognitive enhancement. Methods. This systematic review, registered in PROSPERO (CRD42024554716) and conducted in accordance with PRISMA guidelines, aimed to map methodologies, protocols, and evidence on these combined interventions. Experimental or quasi-experimental empirical studies involving human participants across ages and contexts, published between 2015 and 2025, were included. Searches across six databases yielded 356 records; 45 met eligibility criteria. Results. Studies showed substantial methodological heterogeneity, with a predominance of randomized clinical trials (44%) and within-subject designs (33%). Focused attention and mindfulness meditations and auditory feedback prevailed; wearable devices were used in 35 studies. Intervention dose varied widely, from 1 to 50+ sessions, ranging from 1.5 to 80 min long. Primary outcomes consistently showed reductions in stress, anxiety, depression, and fatigue, alongside gains in well-being, attention, and resilience. Neurophysiological findings included increases in alpha and theta power. Conclusions. Combining meditation with EEG neurofeedback is a promising strategy, but the lack of protocol standardization, small sample sizes, and limited blinding reduce evidence robustness. Future research with rigorous methodology is needed to establish clinical efficacy and guide interventions.
This is a retraction of the article EEG Signatures of Resilience Across Individuals With High and Low Anxiety originally published in Volume 12, Number 1, on March 24, 2025, page 12.
Background. In neurological diagnostics, where complexity, data volume, and diagnostic urgency present major obstacles, artificial intelligence (AI) systems have the potential to revolutionize the field. Despite widespread use, there is a lack of comparable performance assessments of publicly available AI tools for integrated clinical reasoning in neurology. Methods. This cross-sectional study evaluated five AI platforms (ChatGPT 3.5, Google Gemini, Bing AI, Perplexity AI, DeepSeek) utilizing 15 standardized neurological cases from Case Files: Neurology, Third Edition. Each platform was given identical prompts imitating clinical consultations. Responses were evaluated (maximum 6 points per case; total 90) in three domains: diagnosis, subsequent diagnostic step, and therapeutic/molecular foundation. Nonparametric statistical methods (Kruskal-Wallis, Chi-square) assessed performance disparities. Results. ChatGPT achieved the highest overall score (88/90, 97.8%), followed by DeepSeek (86/90, 95.6%), Perplexity (84/90, 93.3%), Google Gemini (78/90, 86.7%), and Microsoft Copilot (73/90, 81.1%). Therapeutic accuracy was 100% for ChatGPT, DeepSeek, and Gemini, whereas it was 80% for Copilot. Although there were disparities in performance, inferential statistics revealed no significant differences between platforms (Kruskal-Wallis p = .423; Chi-square p = .374). Verbosity showed significant variation: DeepSeek averaged 488 words per response, whereas Copilot and Perplexity averaged 239 to 240 words. Conclusion. Popular AI platforms (ChatGPT, DeepSeek) exhibit significant proficiency in neurological diagnosis and treatment planning, but there is a huge difference in the depth and structure of responses across all of the tools. AI should be used as complementary healthcare assistance, with future integration requiring better explainability and real-world validation.
Background. Many patients presenting with headache complaints are often concerned about the possibility of serious underlying conditions and request brain imaging to rule out ominous pathology. However, brain imaging is costly and carries potential risks for both patients and the healthcare system. Objective. This study aims to evaluate the diagnostic value of brain imaging for patients with headaches and intact neurological examination and to identify additional risk factors associated with abnormal imaging results. Methods. A retrospective cohort analysis was conducted on 185 patients with primary complaints of headache and normal neurological examinations, assessed at a general neurology clinic over a 4-year period. Results. Pathological findings on imaging studies were observed in 9.7% of cases, while 16.2% showed findings of uncertain significance. Patients with pathological or uncertain significance (US) findings are significantly older compared to those with normal results (p = .002 and p < .001, respectively). Male sex is associated with a higher likelihood of US findings (p = .03). Nonthrobbing headaches and the presence of red flags in patient history are linked to pathological findings (p = .001 and p = .002, respectively). The presumption of a secondary headache syndrome before ordering imaging is strongly associated with abnormal imaging results (p < .001 for pathological findings and p < .024 for US findings). Conclusion. The decision to perform brain imaging in patients with normal neurological examinations should be individualized based on patient demographics and the presence of nonthrobbing headaches or red flags in the clinical history. A lower threshold for imaging is recommended when secondary headache is suspected.
Substance use disorders (SUD) remain among the most treatment-resistant conditions, particularly in incarcerated populations. Despite decades of neuropsychological models, few approaches capture the dynamic, network-level self-regulation deficits observed in SUD. In this study, 63 incarcerated individuals completed 20 sessions of LORETA neurofeedback targeting alpha (8–13 Hz) at the left precuneus. Pre–post EEG current source density (CSD) and Personality Assessment Inventory (PAI) scores were analyzed. A Random Forest classifier was trained on spectral CSD features and behavioral deltas, with Shapley Additive Explanation (SHAP) values used to interpret model contributions. Results showed significant alpha increases at the trained ROI, accompanied by posterior-to-frontal energy redistribution and functional asymmetry. Notably, both alpha synchronization and desynchronization predicted behavioral improvement, with an overall PAI effect size of d = 0.85. Regression and PCA confirmed directional reorganization and subgroup differentiation. Cohen’s d analysis revealed frequency-specific effects in alpha and low beta bands. Machine learning (ML) revealed that changes in posterior and frontal regions of interest (ROI) were differentially predictive of treatment response, and Reliable Change Index (RCI) identified responders with physiological and behavioral concordance. These findings challenge static trait-based models of addiction and suggest that rhythmic neurofeedback and ML interpretation can uncover emergent regulatory subtypes. This work proposes a shift from amplitude-based training to network-informed modulation as a foundation for scalable, individualized SUD interventions.
Background. Poststroke cognitive impairment (PSCI) involves cognitive deficits emerging within 3 months after stroke. Quantitative EEG (qEEG) in PSCI typically shows changes in relative power, delta-alpha ratio, and peak alpha frequency. Neurofeedback training (NFT) is a promising intervention to improve cognitive function and qEEG patterns, though findings remain inconsistent. Nonetheless, even brief NFT interventions may yield meaningful benefits. Methods. This study assessed the effectiveness of five individualized qEEG-guided NFT sessions (30 min each) in 24 PSCI patients, focusing on changes in MoCA-INA scores and qEEG parameters. Results. NFT significantly improved MoCA-INA scores (Z = −4.106, p < .001, effect size = 0.839), particularly in visuospatial/executive and delayed recall domains, with sustained effects 1 month later. QEEG analysis revealed increased temporal alpha (t = −1.875, p = .037, effect size = 0.23) and parietal beta relative power (t = −1.827, p = .040, effect size = 0.11). Greater cognitive gains were observed in patients aged ≤60 years. Conclusion. These findings support the clinical utility of short-term, qEEG-guided NFT in improving cognitive outcomes and modulating neural activity in PSCI patients. The sustained benefits observed suggest potential for long-term therapeutic impact.
Background. Anxiety and depression are highly prevalent in the general population and primary care. While alpha rhythm (8–12 Hz) stimulation has been shown to reduce anxiety, its impact on broader emotional well-being, including depressive symptoms, is less studied. Objective. This exploratory study examined the effects of alpha neurofeedback training on anxiety and depression in adults. Methods. Fourteen female participants with anxiety and depressive symptoms were randomly assigned to an intervention group (n = 7) or a waitlist control group (n = 7). Psychological symptoms and alpha brainwave activity were assessed before and after the intervention. After the initial phase, the waitlist participants also received the training, forming a quasi-experimental design. Results. Ten sessions of alpha neurofeedback significantly reduced anxiety in both experimental and quasi-experimental phases. Depressive symptoms decreased notably only in the quasi-experimental phase, when all participants received the intervention. Alpha amplitude increased, and improvements in anxiety and depression were correlated, though not statistically significant. Conclusions. These preliminary findings suggest that alpha neurofeedback may be an effective nonpharmacological intervention to reduce anxiety and depression in adults. Results are exploratory, highlighting the need for larger, diverse samples and follow-up assessments to confirm the durability of effects.
This study presents the first implementation of a neurofeedback (NF) protocol based on cordance targeting mood and anxiety disorders. Cordance, a multivariate measure of brain activity, integrates both power within frequency bands and interfrequency relationships, providing a unique perspective on neural synchronization and connectivity. Using a single-case design, a 44-year-old male patient with anxiety, depression, and insomnia was selected based on left frontal discordance. Seven NF sessions were conducted, reinforcing increases in cordance in the left anterior quadrant. The results showed significant improvements in psychometric measures, including reductions in depression, anxiety, and insomnia scores, alongside a marked shift in cordance values toward normative levels. This study introduces cordance-based NF as a potential tool for mood and anxiety regulation, offering promising preliminary evidence for its efficacy. Future research should explore larger sample sizes and longer follow-ups to confirm these findings and expand the clinical applications of cordance-based interventions.
Introduction. Despite increasing diversity in patient populations and documented health disparities, the neuroregulation field has given minimal attention to cultural factors influencing treatment access, engagement, and outcomes. Methods. This article reviews existing literature examining cultural factors across neuroregulation modalities (neurofeedback, transcranial electrical stimulation, photobiomodulation, peripheral biofeedback) and synthesizes theoretical frameworks from cultural neuroscience, cultural humility, and health disparities research. Results. Literature review reveals striking paucity of cultural considerations research across all neuroregulation modalities. While fundamental physiological mechanisms (operant conditioning, neuroplasticity, autonomic regulation) appear universal across human populations, cultural factors profoundly influence treatment perceptions, explanatory models, technology acceptance, and therapeutic relationships. Evidence suggests comparable outcomes across diverse populations when access barriers are addressed. Key disparities stem from structural inequities rather than differential treatment response. Conclusion. The neuroregulation field requires systematic integration of cultural considerations while maintaining scientific understanding of universal physiological mechanisms. Distinguishing between invariant biological processes and culturally variable implementation factors provides foundation for developing inclusive, effective interventions across diverse populations.
Background. Tremor in Parkinson’s disease (PD) is usually a disabling symptom that doesn’t adequately respond to medications. Recently, the European clinical guidelines recommended repetitive transcranial magnetic stimulation (rTMS) as a Grade-B recommendation for improving motor function in PD. Objectives. To study the effect of different rTMS protocols on PD tremor. Methods. 60 PD patients were divided randomly into three groups equally according to rTMS protocols received; they were divided into Group I (5 Hz), Group II (1 Hz), and Group III (sham). Sessions were applied daily for 2 weeks. All patients were subjected to clinical assessment using different assessment tools; tremor Unified Parkinson’s Disease Rating Scale (UPDRS) as well as total UPDRS, tremor amplitude and frequency by EMG before sessions, after last session, and 1 month later. Results. Group I showed the most significant reduction in mean UPDRS (tremor and total) after the last session and 1 month later (p < .001). Group I had the highest reduction in mean tremor amplitude and frequency by EMG after the last session and 1 month later (p < .001; p < .05, respectively). Conclusion. 5 Hz rTMS protocol was the most effective in improving PD tremor.
