We will test if there are differences in electroencephalogram (EEG), heart rate variability (HRV), and galvanic skin response (GSR) during the actual treatment of live cancer cells versus dead cancer cells versus no cells. This could be occurring as a result of interaction between the therapist's brain and the cells. Hypothesis: Biofield therapy targeting cancer cells involves one or more specific correlations between activity in the therapist’s brain and/or peripheral nervous system and cancer-relevant cellular processes. To test this hypothesis, we will: 1. Measure cancer-relevant cellular processes in treated versus untreated cancer cells, either in real time during treatment or post-treatment as experimentally feasible. 2. Measure therapist EEG, HRV, and GSR during treatment of cancer cells, comparing treatment of dead cancer cells (controls) versus cells that are alive. 3. Perform both classical and quantum statistical correlation analysis on pairs or quartets of variables that show significant variation between experiment and control conditions. To test this hypothesis, we will compare EEG, HRV and GSR in two or three conditions: a condition when the treatment occurs delivered to live or dead cancer cells of the same type, a treatment condition with just medium, and a control condition where there is no treatment, but that is as close as possible to the treatment conditions. The simple way to achieve this with petri dishes is to have some dishes where treatment is possible (live cancer cells) and other dishes where it is not. For the dishes where treatment is not possible, we will present the therapist petri dishes with dead cells of the same type as the treatable cells and petri dished with just medium in a double-blind protocol. In a double-blind protocol, the dishes are marked in advance, and neither the experimenter interacting with the therapist nor the therapist knows which dishes contain dead cells, live cells, or medium. The therapist will be informed that petri dishes contain cells with varying health, but not that some include dead cells or no cells. For the trials with live cells, we will also have matched incubator controls to assess cell outcomes. **Procedure.** In an experimental design, it is crucial to have enough statistical power. This is usually done with a power analysis where, based on the expected effect size, we calculate the number of trials for each intervention. However, for cases where the effect size is not known, a rule of thumb is to have no less than 20 independent trials per condition, in this case live versus control conditions. The therapist will need to perform 60 trials, 40 with live cancer cells and 10 with dead cancer cells (control 1) and 10 with just medium (control 2). Based on the extensive prior cell work with Panc-1 cancer cells, a given trial is 15 minutes. We will assume 60 trials in blocks of 6 (4 hour each). There will be ten such blocks, spread over several days, corresponding to about 40 hours of data collection. **One trial is organized as shown below. During the EEG collection, the therapist should always have eyes open** Time synchronization with EEG computer (collecting EEG, HRV, and GSR) and camera computer or other cell recording devices will be done manually. We will use event markers as necessary on computer to indicate when image was taken. The therapist is to remain still and not talk during the baseline and treatment periods. Counterbalance randomly the order of presentation of live cells, dead cells, and medium across a given day. Person bringing cells to the treatment area is blinded to the condition type. - 2-minutes of rest and EEG baseline eyes open fixating an object in the room prior to being presented the cells (period 1) - 15 Minutes of Treatment, providing treatment the whole time, but with period in the middle with possible movement and talking: - 5-minutes of treatment concentration/connected (period 2) (indicate with finger tap when first connected and then also when max connected). The technician will enter these events in the EEG. At end of 5 minutes, the therapist will indicate on a visual analogue scale where 0 = not at all and 10 = extreme how strong the energy flow was during the first 5 minutes. - 5-minutes “rest” where movement and talking is allowed – treatment continues; EEG not processed (period 3). Even though the EEG, HRV, and GSR data will not be usable during this period, it will allow the therapist to move around. - 5-minutes of treatment concentration (period 4): indicate with finger tap when first connected and then also when max connected). At end of 15 minutes, the therapist will indicate 0-10 how strong the energy flow was during the last 5 minutes. - 2-minutes of recording after cells are removed fixating on an object in the room - Questionnaire completed at end of each 15-minute segments asking about the subjective experience. - Break to prepare for next 15-block - Repeat - (for the last trial, have another 2-minute baseline at the very end of the block) Testing the main hypothesis: we will examine brain differences between the treatment of live and dead cells or petri dishes with medium. This may involve spectral power and/or brain dynamics and synchronization between brain regions. We will correct for multiple comparisons when we test for a collection of hypotheses. **Setup:** The therapist is fitted with the EEG system as well as HRV and GSR recorders. On each day, two blocks of 4 trials (2-minute baseline and 15-minute treatment each) are recorded, corresponding to 4+ hours of recording and 6 trials a day in random order: 2 with live Panc-1 cells and 2 with live Panc-1-actin cells (with 2 sets of live Panc-1 cells and live Panc-1-actin cells) and 1 trial with dead Panc-1 cells and 1 trial with medium alone. The total number of trials over 10 days of recording will be 60 – 40 trials with live cells (and 60 matched Panc-1 or Panc-1-actin cancer cells as controls) and 10 trials with dead cells and 10 trials with medium alone. During a given day with 6 trials, there will be a 5-minute breaks between session 2 and 3 and between session 4 and 5. This may vary based on therapist or lab needs. EEG, HRV, GSR recordings will be continuous for each trial but interrupted in between each trial. A file naming convention will be provided to the research assistant. The research assistant will provide verbal instruction for the baseline and connection/disconnection periods and will mark these transitions by manually adding events to the physiological record (pressing a key on the data collection computer). Additional details will be provided (such as the key to press for a given transition). Additional event markers may be added when images of cells are captured. **Cell measures.** Realtime cytoskeleton changes (actin and tubulin; A-cell and T-cell) will be recorded every 1 minute and calcium influx cell membrane v (I-cal) recorded every 1 minute with pictures. Cell cycle, cell invasion, and protein or gene expression will be assayed at end of 15 minutes or later and we may use this information to regress the EEG. Two different microscope set ups will be used to assess real-time cytoskeleton every 1 minute and calcium influx every 1 minute. Equipment. 64 Channel BrainAmps system will be used to collect the EEG, HRV, and GSR all collected on one computer system so that the measures will be time synchronized Cytosmart and Cell voltage measurement equipment: All cells will be maintained on desktop incubators so the environment remains the same with no shifts. **Control cells.** We will have 60 sets of cancer cell controls to housed in the same to match the live treated cells. **Statistical approach** Testing for violation of Bell inequality within this scope requires computing correlations between 4 binary variables (2 for the cells, and 2 for the therapist’s EEG/HRV/GSR). We will discuss how to include such variables in the current setup. 1. Therapist EEG, HRV, GSR data **EEG analysis.** Raw EEG data will be imported into the EEGLAB processing software (Delorme and Makeig, 2004). EEG raw data will be downsampled to 256 Hz, then high-pass filtered with EEGLAB’s linear non-causal Finite Impulse Response (FIR) filter of the FIRFILT plugin (filter order = 1129; transition bandwidth = 0.75 Hz; passband edge = 0.75 Hz; -6 dB cutoff frequency = 0.375 Hz). Files will be inspected visually for abnormal channels (bad connection, impedance, very high noise, flat sections from disconnections, etc.) and artifactual segments (eye and muscle artifacts, high-frequency bursts, etc.). Artifactual regions and channels will be rejected. Power spectral density (PSD) will be computed using the pwelch function in MATLAB (The MathWorks Inc., MA, United States) for each EEG channel on a 1-second hamming window, with 50% overlap and 200% padding (considering data discontinuity due to excluded artifactual regions). Mean PSD will be extracted for each channel for each frequency band: delta (1-3 Hz), theta (3-7 Hz), alpha (8-13 Hz), beta (14-30 Hz), and gamma (30-55 Hz). Spectral power will be compared between experimental conditions. Because each channel will be analyzed separately over a range of frequencies, it will be necessary to correct for multiple comparisons. We will use the Cluster method included in EEGLAB to correct for multiple comparisons and build scalp topography maps of significant differences between conditions of interest. Once a difference or correlation is significant at the channel level, we will analyze the activity at the source level. We will be using 2 methods to do so: eLoreta and Beamforming. These two methods are distributed source localization methods and allow us to determine where in the brain a difference – observed at the scalp level – originates. We will not compare these results to normative databases, as this is done in qEEG analyses since the results acquired in these conditions depend on the experiment conditions (variability in the experimental setup and task has an important effect on the EEG but is not considered in qEEG analyses). Instead, we will focus on analyzing the subject’s result compared to his baseline. We will focus specifically on frontal theta and occipital alpha differences. **HRV analysis.** We will use heartbeat time intervals over 1 minute to match the cell acquisition time periods, HRV calculations will be carried out with the popular Kubios software (Kubios Oy, Finland). R to R intervals will be resampled at 8hz, and the power spectrum will be calculated over the whole record using an FFT decomposition. Power will be obtained at each frequency by calculating the square value of the FFT absolute amplitude. In the frequency domain, total HRV will be obtained by summing the total spectral power for the low-frequency band (LF) 0.05Hz–0.15Hz and the high-frequency band (HF) 0.15Hz–0.35Hz. The LF/HF ratio will also be calculated. Before performing statistical analyses, a log (Ln) transformation will be applied. Other heart measures calculated in the time domain will be Heart Rate (RR), Root Mean Square of Standard Deviation of R to R intervals (RMSSD), the proportion of R to R intervals larger than 50 msec (pNN50), and Triangular Index of R to R intervals. Statistical analyses of these measures will be performed across experimental conditions and corrected for multiple comparisons using the FDR (False Discovery Rate) function included in the EEGLAB software. 2. Cell data Cell image data will be analyzed by Celleste Image Analysis Software (ThermoFisher Scientific) for images obtained from EVOS M7000 microscope and CytoSmart software for images obtained from CytoSmart microscopes. We will explore ratios between treated and incubator controls and changes in EEGs. Median splits, quartile, and other exploratory techniques will be investigated. 3. Classical correlation analysis Linear (Pearson) and/or rank (Spearman) correlation coefficients will be computed for therapist-therapist, cell-cell, and therapist-cell pairs of variables identified under 1) or 2) above. We will use the Robust Correlation toolbox in MATLAB to perform these analyses (https://github.com/CPernet/Robust-Correlations). This toolbox allows removing outlier automatically when performing correlation analyses. 4. Supraclassical correlation analysis We will only be able to perform these tests if we observe significant classical correlations between cell and therapist variables. 4a. Selection of variables: select one or more pairs (t1, t2) of real-time therapist variables and (c1, c2) of real-time cell variables such that t1 and t2 are not significantly correlated, c1 and c2are not significantly correlated, at least one t-variable, c-variable pair is significantly correlated. We anticipate the c1 and c2 will be I-cal and A-cell. We anticipate therapist EEG and HRV may be least correlated. 4b. Value binning: Bin selected variables to values 1 and -1 representing values above and below the mean, respectively. If signal/noise ratio permits, neglect values within 0.5 standard deviation of the mean. 4c. Time binning: Select time bins of +/- 30 s around 0.5 min, 1.5 min, 2.5 min, 3.5 min, 4.5 min, 10.5 min, 11.5 min, 12.5 min, 13.5 min, and 14.5 min (10 bins total) for both therapist and cell variables over which to compute expectation values. 4d. Define contexts: As all measurements are being recorded in every replicate, we can only define contexts by assignments of pairs of variables for joint analysis to non-overlapping, 1 minute time bins. This is a weakness in the design. For each of first (0 – 5 min) and last (10 – 15 min) 1-minute data recording periods: - Context 1 = t1, c1 - Context 2 = t2, c1 - Context 3 = t1, c2 - Context 4 = t2, c2 - Context 5 = t1, c1 Each context is assigned to a 1-minute time bin for all variables. We will need to try multiple bin-context assignments, i.e. shuffle the variable to context assignments listed above, during each of the recording periods to test for lab-time ordering effects, i.e. significant effects of the time bins chosen for each context. This is possible because all data are recorded in each context. If we detect lab-time ordering effects, we will not be able to perform step 4e. 4e. Test for supraclassical correlations: with these context definitions, compute the measure Δ0 of classical signaling given by Khrennikov (2022), Eq. 32. Then compute the measure of entanglement given by the modified CHSH inequality given by Khrennikov (2022), Eq. 38. References Khrennikov, A. (2022) Contextuality, complementarity, signaling, and Bell tests. arxiv:2208.07425v1 [quant-ph]. A. Delorme and S. Makeig, “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,” J. Neurosci. Methods, vol. 134, no. 1, pp. 9– 21, 2004.