David Mayor

The effects of transcutaneous electroacupuncture stimulation (TEAS) on large-scale brain function remain insufficiently characterized. This study employed a graph-theoretical approach to analyze electroencephalogram (EEG) data from 48 healthy participants in the Pilot-6 TEAS study. Participants received sham (0 pps), 2.5 pps, 10 pps, and 80 pps stimulation during baseline, stimulation, and recovery phases. Functional connectivity was assessed using coherence and the weighted phase-lag index, followed by calculation of global and nodal graph measures from thresholded weighted undirected sensor-level networks. Descriptive analysis indicated potential frequency-related differences in EEG network organization. The 2.5 pps condition exhibited the highest average degree, whereas the 80 pps condition demonstrated the highest average clustering coefficient. At 10 pps, sensor-level maps revealed a distinct frontal–central betweenness-centrality pattern. Although 48 participants provided usable EEG data for descriptive analysis, only 3 participants had complete matched graph-metric data for all four stimulation conditions, limiting repeated-measures statistical validation. After correction for multiple comparisons, no statistically significant frequency-related effects were observed, and nodal hub differences were not independently confirmed. Consequently, these patterns should be interpreted as descriptive and exploratory rather than established group-level effects. These findings indicate that graph-theoretical EEG analysis may facilitate the identification of candidate network features for future investigations of TEAS-related brain network organization.

This study investigates the effects of transcutaneous electroacupuncture stimulation (TEAS) on eyeblink rate, EEG, and heart rate variability (HRV), emphasising whether eyeblink data—often dismissed as artefacts—can serve as useful physiological markers. Sixty-six participants underwent four TEAS sessions with different stimulation frequencies (2.5, 10, 80, and 160 pps, with 160 pps as a low-amplitude sham). EEG, ECG, PPG, and respiration data were recorded before, during, and after stimulation. Using non-parametric statistical analyses, including Friedman’s test, Wilcoxon, Conover–Iman, and bootstrapping, the study found significant changes across eyeblink, EEG, and HRV measures. Eyeblink laterality, particularly at 2.5 and 10 pps, showed strong frequency-specific effects. EEG power asymmetry and spectral centroids were associated with HRV indices, and 2.5 pps stimulation produced the strongest parasympathetic HRV response. Blink rate correlated with increased sympathetic and decreased parasympathetic activity. Baseline HRV measures, such as lower heart rate, predicted participant dropout. Eyeblinks were analysed using BLINKER software (v. 1.1.0), and additional complexity and entropy (‘CEPS-BLINKER’) metrics were derived. These measures were more predictive of adverse reactions than EEG-derived indices. Overall, TEAS modulates multiple physiological markers in a frequency-specific manner. Eyeblink characteristics, especially laterality, may offer valuable insights into autonomic function and TEAS efficacy in neuromodulation research.

Background/Objectives: Transcutaneous electrical acupoint stimulation (TEAS), also known as transcutaneous electroacupuncture stimulation, delivers electrical pulses to the skin over acupuncture points (“acupoints”) via surface electrodes. Electroencephalography (EEG) is an important tool for assessing the changes in the central nervous system (CNS) that may result from applying different TEAS frequencies peripherally—i.e., acting via the peripheral nervous system (PNS)—and determining how these influence cerebral activity and neural plasticity. Methods: A total of 48 healthy volunteers were allocated in a semi-randomized crossover study to receive four different TEAS frequencies: 2.5 pulses per second (pps); 10 pps; 80 pps; and sham (160 pps at a low, clinically ineffective amplitude). TEAS was applied for 20 min to each hand at the acupuncture point Hegu (LI4). The EEG was recorded during an initial 5 min baseline recording, then during TEAS application, and after stimulation for a further 15 min, separated into three periods of 5 min (initial, intermediate, and final) in order to assess post-stimulation changes. Source localization analysis was conducted for the traditional five EEG frequency bands: delta (0.1–3.9 Hz), theta (4–7.9 Hz), alpha (8–13 Hz), beta (14–30 Hz), and gamma (30.1–45 Hz). Results: Within-group source localization analyses of EEG data showed that during the initial 5 min post-stimulation, theta oscillations in the 2.5 pps TEAS group increased over the parahippocampal gyrus (t = 4.42, p < 0.01). The 10 pps TEAS group exhibited decreased alpha rhythms over the inferior parietal gyrus (t = −4.20, p < 0.05), whereas the sham (160 pps) TEAS group showed decreased delta rhythms over the postcentral gyrus (t = −3.97, p < 0.05). During the intermediate 5 min post-stimulation, the increased theta activity over the left parahippocampal gyrus (BA27) remained in the 2.5 pps TEAS group (t = 3.97, p < 0.05). However, diminished alpha rhythms were observed in the 10 pps TEAS group over the postcentral gyrus (t = −4.20, p < 0.01), as well as in the delta rhythms in the sham (160 pps) TEAS group in the same area (t = −4.35, p < 0.01). In the final 5 min post-stimulation, reduced alpha rhythms were exhibited over the insula in the 10 pps TEAS group (t = −4.07, p < 0.05). Interaction effects of condition by group demonstrate decreased alpha rhythms in the 10 pps TEAS group over the supramarginal gyrus during the initial 5 min post-stimulation (t = −4.31, p < 0.05), and decreased delta rhythms over the insula in the sham TEAS group during the final 5 min post-stimulation (t = −4.42, p < 0.01). Conclusions: This study revealed that low TEAS frequencies of 2.5 pps and 10 pps modulate theta and alpha oscillations over the brain areas related to emotional and attentional processes driven by external stimuli, as well as neural synchronization of delta rhythms in the sham group in brain areas related to stimulus expectation at baseline. It is hoped that these findings will stimulate further research in order to evaluate such TEAS modulation effects in clinical patients.

This study investigates the neurophysiological effects of transcutaneous electroacupuncture stimulation (TEAS) on brain activity, using advanced machine learning techniques. This work analyzed the electroencephalograms (EEG) of 48 study participants, in order to analyze the brain’s response to different TEAS frequencies (2.5, 10, 80, and sham at 160 pulses per second (pps)) across 48 participants through pre-stimulation, during-stimulation, and post-stimulation phases. Our approach introduced several novel aspects. EEGNet, a convolutional neural network specifically designed for EEG signal processing, was utilized in this work, achieving over 95% classification accuracy in detecting brain responses to various TEAS frequencies. Additionally, the classification accuracies across the pre-stimulation, during-stimulation, and post-stimulation phases remained consistently high (above 92%), indicating that EEGNet effectively captured the different time-based brain responses across different stimulation phases. Saliency maps were applied to identify the most critical EEG electrodes, potentially reducing the number needed without sacrificing accuracy. A phase-based analysis was conducted to capture time-based brain responses throughout different stimulation phases. The robustness of EEGNet was assessed across demographic and clinical factors, including sex, age, and psychological states. Additionally, the responsiveness of different EEG frequency bands to TEAS was investigated. The results demonstrated that EEGNet excels in classifying EEG signals with high accuracy, underscoring its effectiveness in reliably classifying EEG responses to TEAS and enhancing its applicability in clinical and therapeutic settings. Notably, gamma band activity showed the highest sensitivity to TEAS, suggesting significant effects on higher cognitive functions. Saliency mapping revealed that a subset of electrodes (Fp1, Fp2, Fz, F7, F8, T3, T4) could achieve accurate classification, indicating potential for more efficient EEG setups.

The field of signal processing using machine and deep learning algorithms has undergone significant growth in the last few years, with a wide scope of practical applications for electroencephalography (EEG). Transcutaneous electroacupuncture stimulation (TEAS) is a well-established variant of the traditional method of acupuncture that is also receiving increasing research attention. This paper presents the results of using deep learning algorithms on EEG data to investigate the effects on the brain of different frequencies of TEAS when applied to the hands in 66 participants, before, during and immediately after 20 min of stimulation. Wavelet packet decomposition (WPD) and a hybrid Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) model were used to examine the central effects of this peripheral stimulation. The classification results were analysed using confusion matrices, with kappa as a metric. Contrary to expectation, the greatest differences in EEG from baseline occurred during TEAS at 80 pulses per second (pps) or in the ‘sham’ (160 pps, zero amplitude), while the smallest differences occurred during 2.5 or 10 pps stimulation (mean kappa 0.414). The mean and CV for kappa were considerably higher for the CNN-LSTM than for the Multilayer Perceptron Neural Network (MLP-NN) model. As far as we are aware, from the published literature, no prior artificial intelligence (AI) research appears to have been conducted into the effects on EEG of different frequencies of electroacupuncture-type stimulation (whether EA or TEAS). This ground-breaking study thus offers a significant contribution to the literature. However, as with all (unsupervised) DL methods, a particular challenge is that the results are not easy to interpret, due to the complexity of the algorithms and the lack of a clear understanding of the underlying mechanisms. There is therefore scope for further research that explores the effects of the frequency of TEAS on EEG using AI methods, with the most obvious place to start being a hybrid CNN-LSTM model. This would allow for better extraction of information to understand the central effects of peripheral stimulation.

Background: As technology becomes more sophisticated, more accessible methods of interpretating Big Data become essential. We have continued to develop Complexity and Entropy in Physiological Signals (CEPS) as an open access MATLAB^® GUI (graphical user interface) providing multiple methods for the modification and analysis of physiological data. Methods: To demonstrate the functionality of the software, data were collected from 44 healthy adults for a study investigating the effects on vagal tone of breathing paced at five different rates, as well as self-paced and un-paced. Five-minute 15-s recordings were used. Results were also compared with those from shorter segments of the data. Electrocardiogram (ECG), electrodermal activity (EDA) and Respiration (RSP) data were recorded. Particular attention was paid to COVID risk mitigation, and to parameter tuning for the CEPS measures. For comparison, data were processed using Kubios HRV, RR-APET and DynamicalSystems.jl software. We also compared findings for ECG RR interval (RRi) data resampled at 4 Hz (4R) or 10 Hz (10R), and non-resampled (noR). In total, we used around 190–220 measures from CEPS at various scales, depending on the analysis undertaken, with our investigation focused on three families of measures: 22 fractal dimension (FD) measures, 40 heart rate asymmetries or measures derived from Poincaré plots (HRA), and 8 measures based on permutation entropy (PE). Results: FDs for the RRi data differentiated strongly between breathing rates, whether data were resampled or not, increasing between 5 and 7 breaths per minute (BrPM). Largest effect sizes for RRi (4R and noR) differentiation between breathing rates were found for the PE-based measures. Measures that both differentiated well between breathing rates and were consistent across different RRi data lengths (1–5 min) included five PE-based (noR) and three FDs (4R). Of the top 12 measures with short-data values consistently within ± 5% of their values for the 5-min data, five were FDs, one was PE-based, and none were HRAs. Effect sizes were usually greater for CEPS measures than for those implemented in DynamicalSystems.jl. Conclusion: The updated CEPS software enables visualisation and analysis of multichannel physiological data using a variety of established and recently introduced complexity entropy measures. Although equal resampling is theoretically important for FD estimation, it appears that FD measures may also be usefully applied to non-resampled data.

Background: We developed CEPS as an open access MATLAB^® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements. Methods: Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system. Results: The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ (‘tau’) where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded from Bitbucket. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing. Conclusions: We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing.

Background. Heart rate variability (HRV) is increasingly used in acupuncture research as a measure of parasympathetic/sympathetic (PNS/SNS) balance. Heart rate nonlinearity (HRNL) is a new ‘PNS-like’ measure that also reflects this balance. Our aim here was to investigate if frequency or amplitude of transcutaneous electroacupuncture stimulation (TEAS) has greater effects on HRV/HRNL. To our knowledge, this is the only study published to date that investigates our research question. Methods. In this single-blind, semi-randomised cross-over study, ethics approval was obtained and 66 participants attended for four sessions each. Following an initial 5-minute baseline recording, TEAS was applied for 20 minutes to each hand between acupuncture point LI4 (hegu ) and the ulnar border. In each session, stimulation was at a different frequency – 2.5, 10 or 80 pps (pulses per second) at a ‘strong but comfortable’ amplitude, or as ‘sham’ at 160 pps and zero amplitude. After stimulation, recording was continued for 15 minutes to assess post-stimulation changes (making eight 5-minute recordings for each session). ECG (electrocardiograph) data was collected using a Mitsar-EEG-202 amplifier and down-sampled to 500 Hz. After pre-processing in Matlab and Kubios HRV software, 1988 clean 5-minute recordings were available for analysis in Excel and SPSS using nonparametric methods. Fifty-six different HRV and five HRNL measures were considered as indices of PNS or SNS function and subgroups of measures then selected for further examination as described in the Background information provided online. Some results. On the basis of ECG-derived HRV measures and HRNL indices, TEAS at 10 pps appeared in this study to be experienced as less stressful than at 2.5 or 80 pps. Higher amplitude TEAS was in general experienced as more stressful, and the amplitude high-low differential had most effect at 10 pps. In general, stimulation at high and low amplitudes had opposite effects when comparing active stimulation at all frequencies with sham. Moreover, when 10 pps and 2.5 pps were compared with sham stimulation, greater numbers of significant differences were present after than during stimulation, with beneficial changes evident particularly after 10 pps TEAS. Most (and greatest) differences from sham were found for 10 pps TEAS at low amplitude (particularly for PNS-like measures and indices). Findings with potentially useful clinical applications are that: (1) At all frequencies, strong TEAS may be experienced as stressful ; (2) TEAS at 10 pps is more likely to decrease the stress response than at 2.5 or 80 pps , particularly following stimulation. Proviso. However, further analysis is required to clarify questions raised by the current study, including (1) the effects of baseline values of HRV and mood measures, (2) the relevance of participant preference for stimulation at particular frequencies, (3) the usefulness of pulse rate variability (PRV) in addition to HRV, and (4) the benefits of using more refined HRNL measures.

Background Heart rate variability (HRV) is increasingly used as a measure of autonomic health (PNS/SNS balance), including in acupuncture research. Our aim here was to ascertain if transcutaneous electroacupuncture stimulation (TEAS) has frequency-specific effects on HRV. With 66 participants attending for four sessions each, to our knowledge this is the largest study published to date that investigates our research question. Methods Ethics approval was obtained. Following an initial 5-minute baseline recording, TEAS was applied for 20 minutes to each hand between acupuncture point LI4 ( hegu ) and the ulnar border. In each session, stimulation was at a different frequency – 2.5, 10 or 80 pps (pulses per second) at a ‘strong but comfortable’ amplitude, or as ‘sham’ at 160 pps and zero amplitude. After stimulation, recording was continued for 15 minutes to assess post-stimulation changes (making eight 5-minute recordings for each session). ECG (electrocardiograph) data was collected using a Mitsar-EEG-202 amplifier and down-sampled to 500 Hz. After pre-processing in Matlab and Kubios HRV software, 1988 clean 5-minute recordings were available for analysis in Excel and SPSS using nonparametric methods. Fifty-six different HRV measures were considered as indices of parasympathetic or sympathetic functioning and subgroups of measures selected for further examination according to criteria described in background information available online (see http://electroacupuncture.qeeg.co.uk/hrv2 ). Results Significant differences for stimulation frequency were found for many of the measures during successive 5-minute epochs. Most differences were observed during rather than after stimulation, particularly for HRV measures RMSSD, SD2 and DFA alpha1. Other HRV measures showing significant differences for frequency or over time were PNS and SNS indices, absolute LF and HF powers, NNxx and pNNxx, mean RR and multiscale entropy (MSE) at various scales, with other measures – particularly the ratio of LF and HF contributions to the HR spectrum – showing marked changes over time rather than frequency-specific effects. A finding with potentially useful clinical applications was that stimulation at both 2.5 and 80 pps appeared to increase rather than decrease the stress response, whereas sham and 10 pps did so somewhat less. Indeed, 10 Hz stimulation reduced the Kubios HRV ‘Stress index’, and changes in several other measures (SNS, HF_abs, RR, pNNxx) suggest that 10 Hz stimulation was experienced as less stressful during and after stimulation than at the other frequencies used. Discussion We had hoped to find definitive differences in the effects of stimulation frequency on HRV, and our new findings are a little disappointing in that we only found a scattering of indications of how the effects of stimulation might vary with frequency (especially 10 Hz), and also – an unwelcome finding – that the overall effect of taking part in the study, whatever the frequency of stimulation, was to stimulate the SNS rather than (or in addition to) the PNS. However, our HRV data only became available a month ago, and further analysis using data already collected is planned to clarify questions raised by the current study, including on the effects of stimulation amplitude, participant preference for stimulation at particular frequencies, measures at baseline, and pulse rate variability (PRV) in addition to HRV.

Background: Previous research has considered the impact of personal and situational factors on treatment responses. This article documents the first phase of a four-stage project on patient characteristics that may influence responsiveness to acupuncture treatment, reporting results from an exploratory practitioner survey. Methods: Acupuncture practitioners from various medical professions were recruited through professional organisations to complete an online survey about their demographics and attitudes as well as 60 questions on specific factors that might influence treatment. They gave categorical (“Yes”, “No”, and “Don’t know”) and free-text responses. Quantitative and qualitative (thematic) analyses were then conducted. Results: There were more affirmative than negative or uncertain responses overall. Certain characteristics, including ability to relax, exercise and diet, were most often considered relevant. Younger and male practitioners were more likely to respond negatively. Limited support was found for groupings between characteristics. Qualitative data provide explanatory depth. Response fatigue was evident over the course of the survey. Conclusions: Targeting and reminders may benefit uptake when conducting survey research. Practitioner characteristics influence their appreciation of patient characteristics. Factors consistently viewed as important included ability to relax, exercise and diet. Acupuncture practitioners may benefit from additional training in certain areas. Surveys may produce more informative results if reduced in length and complexity.