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Biofield Frequency Bands: Definitions and Group Differences

Jens Rowold, Ph.D., and Paul D Hewson

Abstract Background:

In the biofield literature, it is suggested that electromagnetic energy is part of the biofield. However, little is known about the exact definition of potential electromagnetic biofield frequency bands (FBs).

Primary Study Objective: The current study sought to identify biofield FBs and test potential group differences.

Methods/Design: High-frequency (i.e. >200 Hertz) voltage was measured at body parts along the spine and the brain.

"God Lives in the Spine" ~ Paramahansa Yogananda


While the current neuroscientific paradigm focuses on the brain as the primary source of cognitive experiences, the statement above, a quote from an important yoga leader in the west, emphasizes the potential importance of body parts other than the brain in helping our understanding of spiritual (and other psychological or cognitive) subjective experiences.

The importance of the whole body and its biofield for our subjective experiences and well-being is emphasized in many cultures.1,2 For example, it is hypothesized in the Hindu and yoga literature that ‘energy centers’ (Sanskrit chakras) along the spine (a) form the basis of the electromagnetic aspects of the biofield, and (b) process energy which is responsible for physical, emotional, mental and spiritual experiences.3,4

However, to date, no research has provided a peer-reviewed and replicable method of assessing the electromagnetic frequency bands (FB) which can be conceptualized as a biofield marker.5,6 Thus, given the premise that subjective experiences are processed both in the brain and ‘in the spine’, the purpose of the present study is to explore such a measurement approach and to test whether group-specific differences exist within these FBs.

Definition of the Biofield:

The biofield has been defined as a “… complex organizing energy field engaged in the generation, maintenance, and regulation of biological homeodynamics,”1(p8) and “… living systems coexist within and co-contribute to a biofield, which we define in terms of electric, magnetic, and electromagnetic fields ….”7(p1081) Thus, neuroscientific studies which include electroencephalographic (EEG) measurements of brain activity, can be seen as only one aspect of biofield research.5,8 Other aspects include the electromyographic (EMG) measurement of muscle activity. For example, it has been demonstrated in a case study that a biofield practitioner (BP) was able to alter a client’s EMG significantly.8 The key point here is that while current neuroscientific research tends to separate various aspects of the biofield (EEG, EMG, and others) in order to study them in isolation, the biofield, per definition, neither distinguishes between these aspects nor does it focus exclusively on certain body parts, such as the brain.2,5

This makes sense, since from an evolutionary perspective all living beings, even monocytes, have an electromagnetic field surrounding their body.9 Moreover, it has already been demonstrated that monocytes communicate with their biofield.10 In line with Yogananda’s quote above, this demonstrates that the biofield surrounds the whole body (including the spine), and not just the brain. Therefore, it should be studied using (multiple) measurement locations both inside and outside the brain area. This idea is in line with earlier research conducted by Becker,11 whose findings supported the notion that a biologically relevant electromagnet field exists around all living beings.

It has long been known that the brain sends signals (e.g. between 20 and 100 Hz) to muscles, and that simultaneous measurements of the brain and muscles show coherent energy patterns,12 suggesting that there is one connected (bio-)field, rather than only local mechanisms. In general, current neuroscience does not deny the importance of neurological processes outside the brain. For example, several ganglions located along the spine have been known to emit biologically and psychologically relevant electromagnetic energy in frequencies not just within, but also above, the 0-200Hz range which is typically studied.13,14 However, what the empirical neuroscientific literature lacks, is an integrative study which considers more than two measurement locations simultaneously. Such a study would be in line with the biofield definitions quoted above.

Potential Assessment of the Biofield:

The present study focuses on body parts that are all related to nerve centers, such as ganglions, along the spine. Both electromagnetic nerve impulses and biochemical processes charged with their respective electromagnetic signatures (e.g. hormones such as oxytocin, cf. Moreno-López et al.15) are important and, potentially, could form the basis of what is measured as an overall output of electromagnetic energy in these nerve centers.7 Thus, they can be conceptualized as energy centers. It should be noted that these energy centers are not small dots like, for example, acupuncture points, but body areas because (a) the underlying tissue, such as ganglions, have a considerable size (e.g. several centimeters3) and (b) by definition, the electromagnetic energy radiated by this tissue cannot be only local.4 This has been empirically supported by research focusing on (electro)magnet fields around the heart, in which it was demonstrated that the field emitted by the heart muscles could be measured several feet away from the body.16 A recent empirical study confirmed that BPs were able to assess intra- and inter-individual differences between energy centers along the spine.17

Frequency-Band Specific Biofield Research:

Another limitation, in addition to the current neuroscientific focus on the brain, is the predominant focus on a limited set of frequencies when measuring cognitive (and motor) processes. In neuroscientific electroencephalographic (EEG) studies, frequencies below 100 Hertz (Hz) are typically utilized in brain research, while frequencies below 200 Hz are utilized in electromyography (EMG) research. These FB limitations can also be found in biofield research: For example, in a case (N =1) study,8 EMG frequencies up to 65 Hertz (Hz) were measured at various acupuncture points. It was found that the EMG amplitude was higher when a BP gave the subject a biofield treatment, compared to baseline levels.

While these first biofield research studies provide some insight into biofield assessment, frequencies above 200 Hz are potentially relevant for our understanding of cognitive (and other) processes.18,19 For example, an empirical study revealed that neuron stimulation at 500 Hz inhibited the information processing in the cat hypothalamus.20 With regard to biofield research, it was found in the Hunt et al. case (N =4) study,21 that the high-frequency (i.e., above 200 Hz) EMG which was emitted from areas located on the spine, as well as from the brain, was related to the BPs’ description of the biofield. Several FBs were identified and labeled in accordance with the BP’s description of their subjective visual perceptions. For example, EMG frequencies around 300 Hz were described as having a “green color”. It was claimed that this and, in turn other, colors were related to subjective physical, emotional, mental and spiritual experiences.

While this initial case study claimed to provide a detailed description of the biofield’s EMG-based characteristics in the range above 200 Hz, it had considerable limitations. First, only four subjects were analyzed. While the biofield literature describes seven body parts which are important for the biofield (cf. Method section and Table 1 below), for each subject, only one to four body parts were utilized for EMG measurement. Thus, it was not possible to compare the subjects with each other. Next, it was unclear which body part had been used to conduct the EMG measurement for each subject. Third, the description of the assessment methods, as well as the subsequent statistical analyses, was at best, vague. Together, these limitations prevented replication of the study’s results.21,22 Table 1. Measurement Specifications

. Research Goals:

It is possible to conclude from the review of the biofield literature provided above, that high-frequency EMG could be conceptualized as a potential biomarker for the biofield.23 However, the few empirical studies that exist to date have considerable limitations. The present study aims to close this gap in the biofield literature, by pursuing the primary goal of testing whether FBs above 200 Hz which are common to all body parts can be identified empirically.

It is suggested in the biofield scientific2427 and practitioners’28,29 literature that various exercises (such as yoga and praying) are conducted in order to change, or strengthen, the biofield. Thus, for the purpose of the present study, a sample of biofield practitioners (BPs) performed a wide range of biofield practices so that potential biofield FBs could be detected from the resulting EMG data. Thus, in this study, the main idea behind biofield assessment is that if the biofield is relevant to human (e.g., cognitive and spiritual) experiences, a diverse set of biofield practices performed by different practitioners, should yield common response types, in the form of FBs.

If the biofield mirrors human experiences which are common to all humans, not only BPs, then other samples are important for the detection of potential biofield markers, such as FBs. For example, students (ST) must apply for jobs and therefore encounter situations such as job interviews. Thus, a second, independent sample (i.e. students) was acquired. Both samples, the BPs and the STs, were utilized to pursue the first research goal of identifying potential biofield FBs.

In the context of the second research goal, if biofield FBs exist, there should be meaningful differences between the groups. For example, BPs regularly perform exercises specifically to strengthening their biofields, so that they can use it for healing purposes.19,26,30 Therefore, BPs should have stronger (i.e. higher spectral power) biofields than STs at baseline levels. The second research goal was to test this assumption.


Participants: Sample 1 BPs were acquired through the Crucible Program (see, a long-term biofield training which has been previously scrutinized in peer-reviewed studies.17,26 A total of twenty healthy BPs volunteered to take part in the present study. The average age was 57.10 years (SD =7.45), of which 15% were males, and all were Caucasian. Their education ranged from junior high school (15%), secondary school (35%), up to university degree (50%).

The BPs had, on average, 13.69 years (SD =9) of formal training in biofield techniques and had worked for 13.81 years (SD =9.13) as BPs (i.e. as medical doctors (15%), healing practitioners (25%), massage therapists (5%), hands-on healers (20%), and others (35%)), and had, on average, 5.49 (SD =7.22) clients per week. Sample 2

Students (ST) from a German University were acquired by the first author’s research assistants. The STs had a secondary school (100%) qualification, and were in their fifth or sixth semester of management studies. A total of 45 healthy STs volunteered to participate in the present study. The average age was 22.50 years (SD =2.06), of which 44% were males, and all were Caucasian.


After receiving information about the studýs goals and having provided written informed consent, each participant was prepared for the biofield measurement, as described below. Procedure for BPs

Each BP provided a baseline-measurement, followed by a self-chosen set of biofield exercises. The only requirement was that these were exercises that the respective BP performed regularly to strengthen their biofield. Since, to the best of the authors’ knowledge, no survey providing an overview or categorization of biofield exercises exists, the authors wanted to allow the BPs to choose which exercises were most effective for their own biofields. This is in line with a recent paper23 which emphasizes the importance of an expert-centered view, i.e. the BPs were viewed as experts for their own biofields (energy).

Each participant performed between 4 and 11 exercises (M =7.2). In total, the BPs performed NBP = 139 exercises (more information on these exercises can be found in Supplement S1). Thus, it can be concluded that they performed a considerable variety of exercises that, at least from their own subjective experience, had an impact on their biofields. Examples of the exercises included physical exercises, such as push-ups or yoga, spiritual exercises, such as prayer, eliciting positive emotions, such as compassion, and mental efforts, such as reading job-related publications. These categories of exercises have repeatedly been reported on in the biofield literature1,17,23 and were therefore suitable for the purposes of the present study. Procedure for Students

Like the BPs, each student (ST) provided a baseline-measurement and then performed a set of exercises. In contrast to the BPs, the set of exercises was pre-determined because the overall setting was a simulated assessment center (which was part of an extra-curricular university course) that aimed to provide the ST with a realistic outlook regarding potential future personnel selection experiences. Since STs need to apply for internships or jobs, the assessment center represents a setting typical of a ST’s life, and thus, was suitable for the present study.

Three exercises were performed in this assessment center: First, the Trierer Social Stress Test,31,32 a standardized procedure, which focused on a self-presentation. Second, a mindfulness exercise which required the ST to focus on subjective perceptions, such as posture and breathing.33 Thirdly, twenty-four students were assigned the role of ‘manager’ and twenty that of ‘subordinate’ in a one-on-one role play (see Supplementary Table S1), aiming to simulate a typical, job-related task (i.e., goal-setting), as it is commonly done in management training.34 Exercises such as self-presentation and role play are routinely included in assessment centers.35 The mindfulness exercise was included since it allowed the ST to regain their (e.g. mental and physical) resources after the self-presentation. Problems with the data assessment occurred during several of the measurements (e.g. broken cable, excessive sweating due to heat). These measurements were not included in the final data set, which contained NST = 203 data sets.

Data Acquisition: The respective measurement was taken between two exercises, with the aim of assessing the previous exercise’s effect on the biofield. The participant sat comfortably on a chair during the measurement and was instructed not to move.

EMG was recorded at the body parts described in the biofield literature.3,17 In line with prior studies,21,24 two electrodes per respective body part were placed 2.5 cm from the frontal line. Table 1 summarizes the electrode locations and their relationships to the nervous system.

In line with the EMG methodological literature, bipolar electrodes (11 mm/2 mm Ag/AgCl electrodes, pre-filled with soft gel) were utilized,14 in accordance with the recommended guidelines.36,37 An electrode cap with 13 mm/4 mm Ag/AgCl electrodes was used for the two brain areas.

In addition to the seven bipolar electrode pairs, a ground electrode was placed on the wrist for the following reasons. First, the grounding electrode is supposed to be placed on an inactive site,37,38 and since the present study is aimed at measuring biofield ‘energy’, the wrist is a distal position. Second, placing the grounding electrode on the wrist is in line with the previous biofield studies.21 Furthermore, and in line with the methodological neurophysiological literature,37,38 a reference electrode was placed on the earlobe.

A g.GammaBox from g.tec (Guger Technologies, Austria; see was utilized as a pre-amplifier to ensure a high common-mode rejection ratio and a gUSBamp amplifier (Guger Technologies, Austria) was used to amplify the pre-amplified signal (38.4 kHz internal sampling). All devices were battery driven, in order to minimize power line artifacts. The laboratory was electromagnetically shielded. The respective body parts’ skin was abraded and cleaned with alcohol before the measurements. The impedance was kept below 30 KOhm (M =1.53, SD =1.82),36,39 and the sampling rate was 4800 Hz. The raw data were processed off-line with the help of MATLAB (V. 2016a). Each measurement included 5 minutes of unfiltered data.

Removal of Artifacts:

The total data set contained the measurements from the BPs (NBP = 139) and the STs (NST = 203) sample (total N =342). The data sets were first detrended and the baseline was removed, then the data were checked for potential movement artifacts. Thus, based on their respective kurtosis, probability and power spectrum, bad channels were rejected, and, subsequently, interpolated.39,40 Since EEGLAB is not designed for electrode positions along the spine, the positions were approximated by positions near Cz. Thereafter, EEGLAB’s41 cleanline tool39,42 was used to purge the data sets from potential instrumental or line noise.

Each data set was epoched (2 seconds) to check for problematic time segments and, thereafter, EEGLAB’S automatic (a) spectrum rejection, and (b) artifact epoch detection and rejection algorithms were both performed, thus rejecting improbable epochs. Finally, the data sets were re-referenced.

Power Spectra

The goal of the first step of the analysis was to analyze the power spectrum separately for each body part. More specifically, for each body part and exercise separately, the power spectral density (PSD) was calculated using Welch’s43 algorithm. A Hamming window4446 was utilized, together with a 50% overlap. Thereafter, each PSD was smoothed


Identification of FBs

In line with the present study’s primary research goal, the second step in the analysis was to identify potential biofield FBs. Since each of the seven body parts can be seen as biofield measurement points, the goal of the FB identification was to focus on the part of the biofield that was consistent across the body parts and exercises, and to ignore any information in the PSD that was either very weak or noisy, or specific to one body part. Neuroscientific research often looks for local PSD peaks in order to identify FBs. For example, alpha brain waves can be recognized by a local spectral peak between 8 and 12 Hertz.

One way of identifying FBs in PSDs across multiple measurement locations is to use parallel factor analysis (parafac, cf. Tomasi and Bro47 and Andersson and Bro48). For example, it was used in EEG research to detect spectral bands (e.g. EEG alpha rhythm) in a 3-dimensional (i.e. EEG-Channels x Time x Frequency) data-matrix.49 Parafac makes no assumptions on the factor solutions known from classical factor analyses, such as orthogonality of factors, etc. Therefore, it is suitable for detecting unique spectra in three-way data. For the purpose of our study, parafac was used to detect peaks within spectra (i.e. frequency domain), across body parts and exercises; a 3-dimensional (i.e. body parts x exercises x frequencies) data-matrix was used as an input to the present parafac analysis. Parafac was performed with MATLAB’s N-way toolbox,48 resulting in two factors which accounted for 81.39% of the variance in the data. For parafac, the model’s fit is regularly evaluated by checking the core consistency, which should ideally be 100% to indicate a valid model.47 In the present analysis, the core consistency was 100%, indicating a valid model. Figure 1 shows the parafac result in the frequency domain (results for the exercise and body locations domains, respectively, can be found in Supplement S2).

Figure 1. Results of Parafac Analysis. Note. Results from parafac analyses in the frequency domain: The first factor (blue line) represents a 1/f-factor, while the second factor (red line) contains potential biofield information. Due to the large sample size, the 95% confidence intervals (from 1000 bootstrap samples with 50% sample size of the total sample) are very tight around the mean. The overall shape of the first factor (see Figure 1, blue line) followed the 1/f ‘power law’, which is typical for neuroscientific data.50 This 1/f-trend in PSDs is typically ignored in the EEG literature, since biologically relevant information is usually contained in deviations from the 1/f-trend.51 The second factor (see Figure 1, red line) revealed several clearly identifiable peaks. Following the methodological literature,52 we calculated the 95% confidence interval based on 1000 bootstrap samples, each with 50% of the original sample size (i.e. N =171) (with no replacement). Clearly, the various peaks in the second factor stand out significantly.

For the exact definition of FBs, we followed prior biofield research,21,22 which suggested that the relevant biofield information could be found between ∼180 and ∼1100 Hz: Ten peaks were identified within this range of frequencies with the help of MATLAB’s peakfinder.53 As visualized in Figure 2, a FB was defined around each of these peaks by the respective local minima below and above the respective peak. Thus, with regard to the first research goal, it could be confirmed that biofield FBs could be identified across body locations and exercises. Table 2 summarizes the key characteristics of these FBs. As can be seen from Table 2, in general, these FBs are in accordance with earlier case studies.21,22 To enable a more detailed comparison, the 10 FBs were interpreted and labeled as biofield colors in the following way: In the studies conducted by Hunt et al.,21,22 the FBs were assigned colors, since these colors had been perceived by independent BPs who had observed the participants being measured. For example, it was found that power around 200 Hz was associated with a dark blue color. Thus, when the first FB to be revealed by the parafac analysis in the present study was between 135 and 240 Hertz, it was labeled as dark blue. The same was true for the remaining FBs, with two exceptions: Whereas the prior research claimed to have identified one green FB, the present study revealed two FBs. The same was true for red FBs. For purposes of comprehensibility, the color spectrum identified in prior research has been included at the bottom of Figure 2. It should be noted that the prior research conducted by Hunt et al.21,22 was not unequivocal regarding the exact definition of FBs. Thus, the assignment of specific biofield colors to the FBs found in the present study should only be seen as preliminary.

Testing Differences Between BPs and STs:

As was described in the Method section, the respective baseline measurements were the only measurements that were mandatory for both samples, the BPs and the STs. Thus, in order to test group differences (i.e. BPs versus STs), we focused on the BPs’ baseline measurements (N =20) and those of the students (N =24), respectively. In order to remove the 1/f trend in the data, the PSDs were standardized across frequency bins. Thereafter, 95% CIs were calculated around the mean of each group. Figure 3 shows the results.

As can be seen, the BPs had significantly higher power values for most parts of all the FBs. On the other hand, STs revealed higher power values than the BPs for certain frequencies within the red(1), red(2), purple, and white FBs, respectively. With regard to the second research goal, a t-test was performed to determine which group had higher power values across frequencies. The results demonstrated that the BPs (M =0.09, SD =0.15) had significantly (p <e-18) higher (biofield) power than the students (M = –0.07, SD =0.12).

Figure 2. Interpretation of Frequency Bands (FBs). Note. Interpretation of the second factor (red line, cf. Figure 1) of parafac analysis (based on the total sample). Ten distinct peaks with their respective maxima (filled) and minima (circled) were detected. Exact values for the frequencies can be found in Table 2, respectively. For interpretation of the labeling of the newly identified FBs, the color spectrum at the bottom follows the suggestions from earlier research.21,22

Table 2. Definition of Biofield Frequency Bands (in Hz).


The present study is the first systematic, neuroscientific research effort to demonstrate that ten FBs above 200 Hz can be distinguished across participants and body parts. This supports the western/modern theoretical notions5,7 that the human system includes a complex, electromagnetic (bio-)field. While prior research agreed that brainwaves consistently include alpha, beta, etc. FBs, as a part of the biofield7, the present study is the first to demonstrate that ten FBs above 200 Hz can be consistently found across participants and body parts. This also supports the eastern/ancient notions3 that seven body parts (energy centers or chakras) cooperateto produce one single, multifaceted biofield. The results of the present study go beyond Hunt et al.21,22 findings: First, the combination of twenty BPs and forty-four STs provided more than 340 data sets and a huge variety of exercises were performed. The large data set enabled advanced statistical modeling (i.e. parafac) and yielded results with small confidence intervals (see Figure 1). Although neither sample was representative for the general population, well-defined FBs emerged.

Figure 3. Group Differences (BPs Versus STs). Note. Baseline means and 95%CI for BPs (black) and STs (red). Furthermore, the method described here is based on a standardized, simultaneous measurement of seven body parts. This biofield assessment approach is also (a) congruent with current neuroscience and (b) described in detail (see Method section). Thus, for the first time, it is possible for other researchers to replicate the present results.

This study demonstrates that BPs have higher biofield power compared to STs. This supports the biofield’s construct validity: BPs who claim to have enough biofield energy to utilize it for healing purposes, should have higher baseline biofield power levels than people who do not focus on their biofields (such as students).

Limitations and Implications for Future Research

If BPs have more biofield power compared to STs, how are the identified FBs related to physical and emotional health? Which health-relevant physiological (such as hormones) and psychological (such as emotions) factors are related to the various FBs? Future research should address these important basic questions.

While collecting a set of self-chosen exercises from the BPs was an advantage because it yielded rich and diverse PSDs, it might be considered a disadvantage when it comes to testing exercise-specific effects: There were simply too few exercises per category. Therefore, future studies should examine the effect of one specific, previously selected exercise (such as Chi Gong) on the biofield colors, in a randomized, controlled trial.54

In contrast to previous research, the present study identified two green and red FBs. Future research including additional samples is needed to validate the FBs identified in this study. Stated differently, as each of the two samples performed exercises typical of their respective everyday lives, the FBs detected at least have internal validity for these two groups. Nevertheless, future research should include other groups, such as children. Ultimately, testing representative samples from various groups – as well as the general population - will yield higher levels of external validity for the reported FBs. While the electromagnetic aspects form one part of the biofield, other indicators such as biophotons5,55 are also important. Thus, future research should include both EMG and biophoton measurements, allowing a clearer understanding of the potential overlap between these two biofield indicators to emerge, and ultimately, making a clearer biofield construct definition possible.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.ORCID iD Jens Rowold


1.Rubik, B, Muehsam, D, Hammerschlag, R, Jain, S. Biofield science and healing: history, terminology, and concepts. Glob Adv Health Med. 2015; 4: online supplement.

Google Scholar | SAGE Journals2.Dossey, L. Samueli conference on definitions and standards in healing research: working definitions and terms. Altern Ther Health Med. 2003; 9:A10.

Google Scholar | Medline3.Motoyama, H. Theories of the Chakras: Bridge to Higher Consciousness. Wheaton, IL: Theosophical Publishing House; 1988.

Google Scholar4.Rubik, B. The biofield hypothesis: its biophysical basis and role in medicine. J Alternat Complement Med. 2002; 8: 703–717.

Google Scholar | Crossref | Medline | ISI5.Hintz, KJ, Yount, GL, Kadar, I, et al. Bioenergy definitions and research guidelines. Altern Ther Health Med. 2003; 9: A13–A30.

Google Scholar | Medline6.Ross, C, Harrison, B. The use of magnetic field for the reduction of inflammation: a review of the history and therapeutic results. Altern Ther Health Med. 2013; 19: 22–29.

Google Scholar | Medline7.Hammerschlag, R, Jain, S, Baldwin, AL, et al. Biofield research: a roundtable discussion of scientific and methodological issues. J Alternat Complement Med. 2012; 18: 1081–1086.

Google Scholar | Crossref | Medline8.Forbes, MA, Rust, R, Becker, GJ. Surface electromyography apparatus as a measurement device for biofield research: results from a single case study. J Alternat Complement Med. 2004; 10: 617–626.

Google Scholar | Crossref | Medline9.Fels, D. Endogenous physical regulation of population density in the freshwater protozoan Paramecium caudatum. Sci Rep. 2017; 7: 13800.

Google Scholar | Crossref | Medline10.Scott-Phillips, TC. Defining biological communication. J Evolut Biol. 2008; 21: 387–395.

Google Scholar | Crossref | Medline | ISI11.Becker, R, Selden, G. The Body Electric: Electromagnetism and the Foundation of Life. New York, NY: Harper Collins; 1998.

Google Scholar12.Mima, T, Hallett, M. Corticomuscular coherence: a review. J Clin Neurophysiol. 1999; 16: 501.

Google Scholar | Crossref | Medline | ISI13.Aoyagi, Y, Stein, RB, Branner, A, et al. Capabilities of a penetrating microelectrode array for recording single units in dorsal root ganglia of the cat. J Neurosci Methods. 2003; 128: 9–20.

Google Scholar | Crossref | Medline14.Fridlund, AJ, Cacioppo, JT. Guidelines for human electromyographic research. Psychophysiology. 1986; 23: 567–589.

Google Scholar | Crossref | Medline | ISI15.Moreno-López, Y, Martínez-Lorenzana, G, Condés-Lara, M, Rojas-Piloni, G. Identification of oxytocin receptor in the dorsal horn and nociceptive dorsal root ganglion neurons. Neuropeptides. 2013; 47: 117–123.

Google Scholar | Crossref | Medline | ISI16.McCraty, R, Atkinson, M, Tomasino, D, Bradley, RT. The coherent heart. Heart-brain interactions, psychophysiological coherence, and the emergence of system-wide order. Integr Rev. 2009; 5: 10–115.

Google Scholar17.Rowold, J. Validity of the biofield assessment form (BAF). Eur J Integrat Med. 2016; 8: 446–452.

Google Scholar | Crossref18.Buzsáki, G, Draguhn, A. Neuronal oscillations in cortical networks. Science. 2004; 304: 1926–1929.

Google Scholar | Crossref | Medline | ISI19.Warber, SL, Bruyere, RL, Weintrub, K, Dieppe, P. A consideration of the perspectives of healing practitioners on research into energy healing. Glob Adv Health Med. 2015; 4: online supplement.

Google Scholar | SAGE Journals20.Jordan, D, Mifflin, SW, Spyer, KM. Hypothalamic inhibition of neurones in the nucleus tractus solitarius of the cat is GABA mediated. J Physiol. 1988; 399: 389–404.

Google Scholar | Crossref | Medline21.Hunt, V, Massey, W, Weinberg, R, et al. A Study of Structural Integration from Neuromuscular, Energy Field, and Emotional Approaches. Boulder, CO: Rolf Institute of Structural Integration; 1977.

Google Scholar22.Hunt, V. Infinite Mind: Science of the Human Vibrations of Consciousness. Malibu, CA: Malibu Publishing Co; 1996.

Google Scholar23.Warber, SL, Cornelio, D, Straughn, J, Kile, G. Biofield energy healing from the inside. The J Alternat Complement Med. 2004; 10: 1107–1113.

Google Scholar | Crossref | Medline24.Wirth, DP, Cram, JR, Chang, RJ. Multisite electromyographic analysis of therapeutic touch and qigong therapy. J Alternat Complement Med. 1997; 3: 109–118.

Google Scholar | Crossref | Medline25.Lee, M-S, Lee, MS, Kim, H-J, Moon, S-R. Qigong reduced blood pressure and catecholamine levels of patients with essential hypertension. Int J Neurosci. 2003; 113: 1691–1701.

Google Scholar | Crossref | Medline | ISI26.Schwartz, GE, Swanick, S. Biofield detection: role of bioenergy awareness training and individual differences in absorption. J Alternat Complement Med. 2004; 10: 167–169.

Google Scholar | Crossref | Medline27.Oken, BS, Zajdel, D, Kishiyama, S, et al. Randomized, controlled, six-month trial of yoga in healthy seniors: effects on cognition and quality of life. Altern Ther Health Med. 2006; 12: 40.

Google Scholar | Medline | ISI28.Chia, M, Sieburth, A. Life Pulse Massage: Taoist Techniques for Enhanced Circulation and Detoxification. New York, NY: Simon and Schuster; 2015.

Google Scholar29.Wangyal, T. The Tibetan Yogas of Dream and Sleep. Berkeley, CA: Shambhala Publications; 1998.

Google Scholar30.Bruyere, R. Wheels of Light: Chakras, Auras, and the Healing Energy of the Body. New York: Simon & Schuster; 1994.

Google Scholar31.Allen, AP, Kennedy, PJ, Cryan, JF, et al. Biological and psychological markers of stress in humans: focus on the Trier Social Stress Test. Neurosci Biobehav Rev. 2014; 38: 94–124.

Google Scholar | Crossref | Medline | ISI32.Allen, AP, Kennedy, PJ, Dockray, S, et al. The trier social stress test: principles and practice. Neurobiol Stress. 2017; 6: 113–126.

Google Scholar | Crossref | Medline33.Michalak, J, Burg, J, Heidenreich, T. Don’t forget your body: mindfulness, embodiment, and the treatment of depression. Mindfulness (NY). 2012; 3: 190–199.

Google Scholar | Crossref | ISI34.Loughrey, TO, Marshall, GK, Bellizzi, A, Wilder, DA. The use of video modeling, prompting, and feedback to increase credit card promotion in a retail setting. J Organ Behav Manage. 2013; 33: 200–208.

Google Scholar35.Atkins, PWB, Wood, RE. Self-versus others’ ratings as predictors of assessment center ratings: validation evidence for 360-degree feedback programs. Pers Psychol. 2002; 55: 871–904.

Google Scholar | Crossref | ISI36.Hermens, HJ, Freriks, B, Merletti, R, et al. European recommendations for surface electromyography. Roessingh Res Dev. 1999; 8: 13–54.

Google Scholar37.Criswell, E. Cram’s Introduction to Surface Electromyography. Burlington, MA: Jones & Bartlett; 2010.

Google Scholar38.Zipp, P. Recommendations for the standardization of lead positions in surface electromyography. Eur J Appl Physiol Occup Physiol. 1982; 50: 41–54.

Google Scholar | Crossref | ISI39.Bigdely-Shamlo, N, Mullen, T, Kothe, C, et al. The PREP pipeline: standardized preprocessing for large-scale EEG analysis. Front Neuroinform. 2015; 9: 16.

Google Scholar | Crossref | Medline40.Islam, MK, Rastegarnia, A, Yang, Z. Methods for artifact detection and removal from scalp EEG: a review. Clin Neurophysiol. 2016; 46: 287–305.

Google Scholar | Crossref41.Delorme, A, Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004; 134: 9–21.

Google Scholar | Crossref | Medline | ISI42.Mullen, T. NITRC: CleanLine: Tool/Resource Info. 2012.

Google Scholar43.Welch, P. The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust. 1967; 15: 70–73.

Google Scholar | Crossref44.Yan, A, Zhou, W, Yuan, Q, et al. Automatic seizure detection using Stockwell transform and boosting algorithm for long-term EEG. Epilepsy Behav. 2015; 45: 8–14.

Google Scholar | Crossref | Medline45.Franaszczuk, PJ. Time-Frequency Energy Analysis. Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics. 2016; 95: 95–110.

Google Scholar46.Herman, P, Prasad, G, McGinnity, TM, Coyle, D. Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification. IEEE Trans Neural Syst Rehabilitat Eng. 2008; 16: 317–326.

Google Scholar | Crossref | Medline47.Tomasi, G, Bro, R. A comparison of algorithms for fitting the PARAFAC model. Computat Stat Data Anal. 2006; 50: 1700–1734.

Google Scholar | Crossref48.Andersson, CA, Bro, R. The N-way toolbox for MATLAB. Chemometr Intell Lab Syst. 2000; 52: 1–4.

Google Scholar | Crossref | ISI49.Miwakeichi, F, Martınez-Montes, E, Valdés-Sosa, PA, et al. Decomposing EEG data into space–time–frequency components using parallel factor analysis. NeuroImage. 2004; 22: 1035–1045.

Google Scholar | Crossref | Medline | ISI50.Freeman, WJ, Holmes, MD, Burke, BC, Vanhatalo, S. Spatial spectra of scalp EEG and EMG from awake humans. Clin Neurophysiol. 2003; 114: 1053–1068.

Google Scholar | Crossref | Medline | ISI51.van Aerde, KI, Mann, EO, Canto, CB, et al. Flexible spike timing of layer 5 neurons during dynamic beta oscillation shifts in rat prefrontal cortex. J Physiol. 2009; 587: 5177–5196.

Google Scholar | Crossref | Medline52.Kiers, HAL. Bootstrap confidence intervals for three‐way methods. J Chemom. 2004; 18: 22–36.

Google Scholar | Crossref53.Yoder, N. peakfinder: (x0, sel, thresh, extrema, includeEndpoints, interpolate). Accessed April 23, 2020.

Google Scholar54.Warber, SL, Gordon, A, Gillespie, BW, et al. Standards for conducting clinical biofield energy healing research. Altern Ther Health Med. 2003; 9: A54–A64.

Google Scholar | Medline55.van Wijk, EPA, van Wijk, R, Bajpai, RP. Quantum squeezed state analysis of spontaneous ultra weak light photon emission of practitioners of meditation and control subjects. Indian J Exp Biol. 2008; 46: 345.

Google Scholar | Medline | ISI56.Koessler, L, Maillard, L, Benhadid, A, et al. Automated cortical projection of EEG sensors: anatomical correlation via the international 10–10 system. NeuroImage. 2009; 46: 64–72.


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