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Autonomic indicators of self-transcendence: insights from the numadelic VR paradigm.

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The 3 matches
  1. [1] § Materials and Methods › Physiological data collection › Data preprocessing ↔ 3ECG_RespProcessing_UPDATED.ipynb, lines 452–498 · score 0.66 · moving median, signal envelope, outliers, window, amplitude
  2. [2] § Materials and Methods › Physiological data collection › Data preprocessing ↔ 3ECG_RespProcessing_UPDATED.ipynb, lines 43–46 · score 0.65 · ecg_process, peak detection, ECG signal, preprocessing
  3. [3] § Materials and Methods › Physiological data collection › Data preprocessing ↔ 3ECG_RespProcessing_UPDATED.ipynb, lines 43–46 · score 0.53 · peak detection, ECG signals, preprocessed, quality

Paper

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The authors' code

Jupyter notebook · 521 lines · 16 KB · no license · 3 matches

  1. # %%
  2. #pip install
  3. #pip install
  4. #pip install neurokit2 hrv-analysis PyWavelets mne astropy numpy
  6. # %%
  7. import neurokit2 as nk
  8. import pandas as pd
  9. import matplotlib.pyplot as plt
  10. import numpy as np
  11. import os
  12. import scipy.io
  13. from pathlib import Path
  14. from scipy.stats import variation
  15. import multiprocessing
  16. plt.rcParams['figure.figsize'] = 15, 5
  17. f=180 #sampling frequency
  22. # %% [markdown]
  23. # ## ECG data preprocessing ##
  25. # %%
  26. Type = 'VR' # VR or Control
  27. Session = 'Session4'
  28. Shimmer = 'S4'
  29. Section = 'Baseline'
  30. #Section= 'Medit'
  31. #Section ='PostMedit'
  33. relative_dir = os.path.join('..', 'Ripple','Processed_data', Type, Session, Shimmer, Section)
  36. # Read the CSV file using the relative path
  37. csv_file_path = os.path.join(relative_dir, f'{Section}.csv')
  38. data = pd.read_csv(csv_file_path)
  39. data.columns = data.columns.str.extract(r'(ECG.*)', expand=False)
  40. ecg=data['ECG_LA_RA_24BIT_BPF_CAL'] # NB: change to LL_RA instaed of LA_RA for Session 4, S3 due to inverted electrodes
  43. # %%
  44. #This function runs different preprocessing steps: Cleaning (using ecg_clean()), peak detection (using ecg_peaks()), heart rate calculation (using signal_rate()), signal quality assessment (using ecg_quality()), QRS complex delineation (using ecg_delineate()), and cardiac phase determination (using ecg_phase()).
  45. ecg_signals, info = nk.ecg_process(ecg, sampling_rate=f) # default method 'neurokit'
  46. nk.ecg_plot(ecg_signals, info)
  48. # %%
  49. #Zhao et al. (2018) method of quality assessment
  50. ecg_clean=ecg_signals['ECG_Clean']
  51. nk.ecg_quality(ecg_clean,
  52. sampling_rate=f,
  53. method="zhao2018",
  54. approach="fuzzy")
  57. # %% [markdown]
  58. # ## Extract HR and RR times ##
  60. # %%
  61. #detect ECG R peaks and check data
  62. Rpeaks, info = nk.ecg_peaks(ecg_signals['ECG_Clean'], sampling_rate=f, method='neurokit', correct_artifacts=True, show=True)
  65. # %%
  66. #Identify and Correct Peaks using "Kubios" Method
  67. rpeaks_uncorrected = nk.ecg_findpeaks(ecg_signals['ECG_Clean'], method="pantompkins", sampling_rate=f)
  69. info, rpeaks_corrected = nk.signal_fixpeaks(
  70. rpeaks_uncorrected, sampling_rate=f, iterative=True, method="Kubios", show=True)
  72. # %%
  73. # Visualize Artifact Correction
  74. rate_corrected = nk.signal_rate(rpeaks_corrected, desired_length=len(ecg))
  76. rate_uncorrected = nk.signal_rate(rpeaks_uncorrected, desired_length=len(ecg))
  78. nk.signal_plot(
  79. [rate_uncorrected, rate_corrected],
  80. labels=["Heart Rate Uncorrected", "Heart Rate Corrected"]
  81. )
  86. # %%
  89. # Extract R peaks indices
  90. r_peak_indices = rpeaks_corrected
  91. r_peak_times = r_peak_indices / f # Convert indices to time by dividing by the sampling rate
  92. r_peak_amplitudes = ecg_signals['ECG_Clean'].iloc[r_peak_indices]
  93. HR = ecg_signals['ECG_Rate'].iloc[r_peak_indices]
  95. # Refine R peak amplitudes, in case some R peaks are not detected at maximum peak amplitude
  96. for i, index in enumerate(r_peak_indices):
  97. window_start = max(0, index - int(0.3 * f)) # 0.3 seconds before
  98. window_end = min(len(ecg_signals['ECG_Clean']), index + int(0.3 * f)) # 0.3 seconds after
  99. local_maxima = np.max(ecg_signals['ECG_Clean'].iloc[window_start:window_end])
  101. if r_peak_amplitudes.iloc[i] < local_maxima:
  102. r_peak_amplitudes.iloc[i] = local_maxima
  104. # Create a DataFrame
  105. df_rpeaks = pd.DataFrame({
  106. 'Time': r_peak_times,
  107. 'Amplitude': r_peak_amplitudes,
  108. 'HR': HR
  109. })
  111. # Calculate RR intervals
  112. df_rpeaks['rri'] = df_rpeaks['Time'].diff()
  114. # Function to filter R peaks based on local median RR intervals
  115. def filter_r_peaks(df, window_size=10, threshold=0.5):
  116. filtered_indices = []
  117. for i in range(len(df)):
  118. if i < window_size:
  119. filtered_indices.append(i)
  120. continue
  122. local_median_rri = df['rri'].iloc[i-window_size:i].median()
  123. if df['rri'].iloc[i] >= threshold * local_median_rri:
  124. filtered_indices.append(i)
  126. return df.iloc[filtered_indices].reset_index(drop=True)
  128. # Apply the filtering function
  129. df_rpeaks_filtered = filter_r_peaks(df_rpeaks)
  131. # Update r_peak_indices based on the filtered DataFrame
  132. filtered_indices = df_rpeaks_filtered.index.to_list()
  133. r_peak_indices_filtered = r_peak_indices[filtered_indices]
  137. # %%
  138. # apply R peaks correction to ECG_R_peaks column in ECG signal
  139. ecg_signals['ECG_R_Peaks']=0
  140. # Set the values to 1 at the R-peaks indices
  141. ecg_signals.loc[r_peak_indices_filtered, 'ECG_R_Peaks'] = 1
  143. # %%
  144. # Plotting in 1-minute chunks for visualiation of R peaks detection
  145. chunk_size = f * 60 # Number of samples per 1 minute
  146. num_chunks = int(len(ecg_signals['ECG_Clean']) / chunk_size)
  147. time = np.linspace(0, len(ecg_signals['ECG_Clean']) / f, len(ecg_signals['ECG_Clean']))
  149. plt.figure(figsize=(12, 6 * num_chunks))
  151. for i in range(num_chunks):
  152. start = i * chunk_size
  153. end = (i + 1) * chunk_size
  154. chunk_time = time[start:end]
  155. chunk_signal = ecg_signals['ECG_Clean'][start:end]
  157. chunk_r_peaks = df_rpeaks_filtered[(df_rpeaks_filtered['Time'] >= chunk_time[0]) & (df_rpeaks_filtered['Time'] < chunk_time[-1])]
  159. plt.subplot(num_chunks, 1, i + 1)
  160. plt.plot(chunk_time, chunk_signal, label='ECG Signal')
  161. plt.plot(chunk_r_peaks['Time'], chunk_r_peaks['Amplitude'], 'ro', label='R Peaks')
  162. plt.xlabel('Time (s)')
  163. plt.ylabel('Amplitude')
  164. plt.legend()
  165. plt.title(f'ECG Signal with R Peaks (Minute {i + 1})')
  167. plt.tight_layout()
  168. plt.show()
  170. # %%
  171. #Visualise RR intervals after correction
  172. plt.plot(df_rpeaks_filtered['rri'], label='RR intervals')
  174. # %%
  175. #save Rpeaks files
  178. csv_file_path = os.path.join(relative_dir, 'HRV', 'Rpeaks_time_HR_rri.csv')
  179. df_rpeaks_filtered.to_csv(csv_file_path, index=False)
  181. #save Rpeaks time in text format for SAI/PAI indexes estimation
  182. Rpeaks_time= df_rpeaks_filtered['Time'].values # delete the first nan value before saving as txt
  184. txt_file_path = os.path.join(relative_dir, 'HRV', 'RPeaks.txt')
  186. np.savetxt(txt_file_path, Rpeaks_time, fmt='%f', header='', comments='')
  190. # %% [markdown]
  191. # ## Estimate HRV parameters ##
  193. # %%
  194. import statistics
  195. HR=df_rpeaks['HR']
  196. hrv_indices = nk.hrv(ecg_signals['ECG_R_Peaks'], sampling_rate=f, show=True)
  197. hrv_indices['HR'] = statistics.mean(HR)
  198. hrv_indices['SD HR']=statistics.stdev(HR)
  199. hrv_indices
  201. # %%
  202. csv_file_path = os.path.join(relative_dir,'HRV', 'HRVmetrics.csv')
  203. hrv_indices.to_csv(csv_file_path, index=False)
  206. # %% [markdown]
  207. # ## Preprocessing of respiratory signal ##
  209. # %%
  210. resp=data['ECG_RESP_24BIT_LPF_CAL'] # name to be updated
  211. #def normalize_resp(resp_series):
  212. #return (resp_series - np.mean(resp_series)) / np.std(resp_series)
  213. #resp_norm = normalize_resp(resp)
  215. # %%
  216. #This is the processing of Respiratory signal
  217. Resp, info = nk.rsp_process(resp, sampling_rate=f, method='khodadad2018', method_rvt='harrison2021', report="text")
  219. fig = nk.rsp_plot(Resp, info)
  222. # %%
  223. nk.signal_plot(Resp['RSP_Raw'])
  224. nk.signal_plot(Resp['RSP_Clean'])
  226. # %%
  227. csv_file_path = os.path.join(relative_dir, 'Resp', 'RespTimeserie.csv')
  228. Resp.to_csv(csv_file_path, index=False)
  231. # %% [markdown]
  232. # ## Analysis of repiratory signal parameters ##
  234. # %%
  235. # for Baseline and post Baseline/riple analysis is done on 5 min readings and 1 min epochs
  236. # for Ripple / Baseline, analysis on 1min epochs
  238. rsp_analysis= nk.rsp_analyze(Resp, sampling_rate=f)
  239. #rsp_analysis= nk.rsp_intervalrelated(Resp, sampling_rate=f)
  240. RespAmp=Resp['RSP_Amplitude']
  241. rsp_analysis['RSP_Amplitude'] = statistics.mean(RespAmp)
  244. csv_file_path = os.path.join(relative_dir, 'Resp', 'RespParameters.csv')
  245. rsp_analysis.to_csv(csv_file_path, index=False)
  246. rsp_analysis
  249. # %% [markdown]
  250. # ## Repiratory sinus Arythmia (RSA) ##
  252. # %%
  253. #Run only if respiratory signal is good quality
  254. rsa_metrics= nk.hrv_rsa(ecg_signals, Resp, sampling_rate=f, continuous=False) #
  255. rsa_metrics_df=pd.DataFrame({'RSA_P2T_Mean': rsa_metrics['RSA_P2T_Mean'],
  256. 'RSA_P2T_Mean_log': rsa_metrics['RSA_P2T_Mean_log'],
  257. 'RSA_P2T_SD': rsa_metrics['RSA_P2T_SD'],
  258. 'RSA_P2T_NoRSA': rsa_metrics['RSA_P2T_NoRSA'],
  259. 'RSA_PorgesBohrer': rsa_metrics['RSA_PorgesBohrer'],
  260. 'RSA_Gates_Mean': rsa_metrics['RSA_Gates_Mean'],
  261. 'RSA_Gates_Mean_log':rsa_metrics['RSA_Gates_Mean_log'],
  262. 'RSA_Gates_SD':rsa_metrics['RSA_Gates_SD']
  264. }, index=[0])
  265. rsa_metrics_df
  268. # %%
  269. # Get RSA as a continuous signal
  270. rsa = nk.hrv_rsa(ecg_signals, Resp, sampling_rate=f, continuous=True)
  274. # %%
  275. csv_file_path = os.path.join(relative_dir,'HRV', 'rsa_average.csv')
  276. rsa_metrics_df.to_csv(csv_file_path, index=False)
  278. csv_file_path = os.path.join(relative_dir,'HRV', 'rsa_continuous.csv')
  279. rsa.to_csv(csv_file_path, index=False)
  282. # %% [markdown]
  283. # ## Combine data of interest into 1 dataframe ##
  285. # %%
  286. Resp_REvents= Resp.iloc[r_peak_indices_filtered]
  287. Resp_REvents=Resp_REvents.reset_index(drop=True)
  288. rsa_REvents= rsa.iloc[r_peak_indices_filtered]
  289. rsa_REvents=rsa_REvents.reset_index(drop=True)
  291. # %%
  292. #Create a data frame for R events with R time, (RRI), HR, Resp, Resp ecg (EDR), resp rate, RSA
  293. Index= np.arange(len(Resp_REvents))
  296. rsa_REvents_df= pd.DataFrame(rsa_REvents)
  297. REvents_variables=[]
  298. REvents_variables=pd.DataFrame({'HR': df_rpeaks_filtered['HR'],
  299. 'Rpeaks_time': df_rpeaks_filtered['Time'],
  300. 'Rpeaks_amplitude': df_rpeaks_filtered['Amplitude'],
  301. 'rri': df_rpeaks_filtered['rri'],
  302. 'Resp': Resp_REvents['RSP_Clean'],
  303. 'Resp rate': Resp_REvents['RSP_Rate'],
  304. 'Resp amp': Resp_REvents['RSP_Amplitude'],
  305. 'RSA_P2T': rsa_REvents_df['RSA_P2T'],
  306. 'RSA_Gates': rsa_REvents_df['RSA_Gates'] })
  308. REvents_variables = REvents_variables.reset_index(drop=True)
  310. REvents_variables
  312. # %%
  313. # Step added to filter out resp data out of normal range (i.e points >1)
  314. # Replace values in 'Column1' that are greater than 1 with NaN
  315. REvents_variables['Resp amp'] = REvents_variables['Resp amp'].apply(lambda x: np.nan if x > 1 else x)
  320. # %%
  321. nk.signal_plot([REvents_variables['rri'], REvents_variables['Resp'], REvents_variables['RSA_Gates']], standardize=True)
  323. # %% [markdown]
  324. # ## CALCULATE RMSSD WITH SLIDING WINDOWS ##
  326. # %%
  327. #RMMSSD computed on 30 data points (R peaks), with a step of one R peak
  328. window_size = 30 # 30
  329. step_size = 1 # 1
  331. rri=REvents_variables['rri']
  333. def calculate_rmssd(rr, window_size, step_size):
  334. rmssd = {}
  335. hr = 60000/rr
  336. for start in range(0, len(rr) - window_size, step_size):
  337. window = rr[start:start + window_size]
  338. rmssd[start]= np.sqrt(np.mean(np.square(np.diff(window*1000))))
  339. return rmssd
  340. RMSSD_windowed=calculate_rmssd(rri, 30, 1)
  342. RMSSD_windowed_df=pd.DataFrame({'RMSSD':RMSSD_windowed})
  344. RMSSD_windowed_df
  346. # Add 30 Nan values at the begining
  348. # Create a DataFrame of 30 NaN values
  349. nan_values = pd.DataFrame(np.nan, index=range(30), columns=RMSSD_windowed_df.columns)
  350. # Concatenate the NaN values DataFrame with the original DataFrame
  351. RMSSD_windowed_df_nan = pd.concat([nan_values, RMSSD_windowed_df], ignore_index=True)
  353. RMSSD_windowed_df_nan
  355. # %%
  356. # Add RMSSD time serie to R events variables of interest
  357. REvents_variables['RMSSD']=RMSSD_windowed_df_nan['RMSSD']
  358. REvents_variables
  362. # %% [markdown]
  363. # ## Code to calculate the SAI/ PAI indexes ##
  365. # %%
  366. import requests
  367. import re
  368. import time
  370. # Define the relative directory and the input/output file paths
  371. txt_file_path = os.path.join(relative_dir, 'HRV', 'RPeaks.txt')
  372. output_file_path = os.path.join(relative_dir,'HRV', 'SAI_PAI.txt')
  374. url = 'https://neurostat-mit.appspot.com/'
  375. values = {'worker': 'saipai'}
  377. # Step 1: Open and read the input file
  378. with open(txt_file_path, 'r') as file:
  379. files = {'myFile': file}
  381. # Step 2: Upload the file and get the response URL
  382. r = requests.post(url + 'upload', files=files, data=values)
  383. if not r.ok:
  384. raise Exception("Failed to upload the file.")
  386. resp_url = re.findall(r'url=([^"]*)"', r.text)
  387. if len(resp_url) != 1:
  388. raise Exception("Failed to retrieve the response URL.")
  390. # Step 3: Poll the server to get the result
  391. while True:
  392. r = requests.get(url + resp_url[0])
  393. if not r.ok:
  394. raise Exception("Failed to retrieve the result.")
  395. if not r.text.startswith('<'):
  396. result = r.text
  397. break
  398. time.sleep(1)
  400. # Step 4: Save the result to the output file
  401. with open(output_file_path, 'w') as output_file:
  402. output_file.write(result)
  404. print(f"Result saved to: {output_file_path}")
  408. # %%
  409. ## add SAI and PAI to the key variables
  410. file_path = os.path.join(relative_dir,'HRV', 'SAI_PAI.txt')
  412. # Read the text file into a DataFrame
  413. # Adjust the delimiter parameter if your file has a different delimiter (e.g., ',' for comma-separated values)
  414. df = pd.read_csv(file_path, delimiter='\t', header=None)
  416. # Rename the columns
  417. df.columns = ['SAI', 'PAI', 'SAI/PAI']
  419. #add a first row of nan vaulues to match the format of REvents variable
  420. # Create a DataFrame with a single row of NaN values
  421. nan_row = pd.DataFrame([[np.nan, np.nan, np.nan]], columns=df.columns)
  422. # Concatenate the NaN row with the original DataFrame
  423. df = pd.concat([nan_row, df], ignore_index=True)
  424. df
  427. # %%
  428. REvents_variables['SAI']= df['SAI']
  429. REvents_variables['PAI']= df['PAI']
  430. REvents_variables['SAI/PAI']= df['SAI/PAI']
  431. REvents_variables
  433. # %% [markdown]
  434. # ## Code to Calculate HRV amplitude ##
  436. # %%
  437. #resample sample every 1 s
  439. # Convert 'Rpeaks_time' to datetime and set it as the index
  440. REvents_variables['Timestamp']= REvents_variables['Rpeaks_time']
  441. REvents_variables['Timestamp'] = pd.to_datetime(REvents_variables['Timestamp'], unit='s')
  442. REvents_variables.set_index('Timestamp', inplace=True)
  444. # Resample the DataFrame to have a 1-second sampling frequency
  445. resampled_df = REvents_variables.resample('1s').mean()
  447. # Interpolate to fill missing values
  448. resampled_df = resampled_df.interpolate(method='linear')
  450. resampled_df
  452. # %%
  453. import numpy as np
  454. import matplotlib.pyplot as plt
  455. from scipy.signal import find_peaks
  457. # Generate a sample signal (sinusoidal with some noise)
  459. signal = resampled_df['rri'].values
  460. t = resampled_df.index
  462. # Find local maxima and minima
  463. peaks, _ = find_peaks(signal)
  464. troughs, _ = find_peaks(-signal)
  466. # Calculate the median of 5 local maxima and minima to avoid outliers
  467. def moving_median(data, window_size=5):
  468. medians = []
  469. for i in range(len(data)):
  470. window = data[max(0, i - window_size // 2):min(len(data), i + window_size // 2 + 1)]
  471. medians.append(np.median(window))
  472. return np.array(medians)
  474. upper_envelope = moving_median(signal[peaks])
  475. lower_envelope = moving_median(signal[troughs])
  477. # Interpolate the envelopes to match the length of the original signal
  478. upper_envelope_interp = np.interp(t, t[peaks], upper_envelope)
  479. lower_envelope_interp = np.interp(t, t[troughs], lower_envelope)
  481. # Compute the width of the envelope at each time point
  482. envelope_width = upper_envelope_interp - lower_envelope_interp
  484. resampled_df['RRIEnveloppe']=envelope_width
  485. resampled_df['LowerEnveloppe']=lower_envelope_interp
  486. resampled_df['UpperEnloppe']= upper_envelope_interp
  488. # Plot the original signal, envelope, and width
  489. plt.figure(figsize=(12, 6))
  490. plt.plot(t, signal, label='Original Signal')
  491. plt.plot(t, upper_envelope_interp, label='Upper Envelope', linestyle='--')
  492. plt.plot(t, lower_envelope_interp, label='Lower Envelope', linestyle='--')
  493. plt.fill_between(t, lower_envelope_interp, upper_envelope_interp, color='gray', alpha=0.3, label='Envelope Width')
  494. plt.xlabel('Time')
  495. plt.ylabel('Amplitude')
  496. plt.legend()
  497. plt.title('Signal Envelope and Width')
  498. plt.show()
  500. # %%
  501. resampled_df
  503. # %%
  504. csv_file_path = os.path.join(relative_dir,'REvents_ALLvariables.csv')
  505. resampled_df.to_csv(csv_file_path, index=False)
  508. # %%
  511. # %%
  514. # %%
  517. # %%
  520. # %%

3ECG_RespProcessing_UPDATED.ipynb at commit 4614383, no license · at the source

Overview

Authors: Valerie Bonnelle1,2, Giulia Parola3, Catherine Andreu1, Joseph L Hardy2,4, Justin Wall2, Christopher Timmermann5,6, Ausiàs Cebolla7, Maja Wrzesien7, David R Glowacki1
ORCID iDs: Valerie Bonnelle, Giulia Parola
  1. Intangible Realities Laboratory, Centro Singular de Investigación en Tecnoloxías Intelixentes, Rúa de Jenaro de la Fuente Domínguez, Campus Vida of the Universidade de Santiago de Compostela (USC), 15782 Santiago de Compostela, A Coruña, Spain
  2. Numadelic Labs, 2253 Carquinez Avenue, El Cerrito, CA 94530, United States
  3. Polibienestar Research Institute, University of Valencia, Edificio de Institutos de Investigación Calle Serpis, 2946022 Valencia, Spain
  4. aNUma, Inc., 4040 Civic Center Dr., San Rafael, CA 94903, United States
  5. Department of Experimental Psychology, University College London, 26 Bedford Way, London WC1H 0AP, United Kingdom
  6. Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, The Commonwealth Building, Hammersmith Hospital, Du Cane Road, London W12 0NN, United Kingdom
  7. Department of Personality, Evaluation and Psychological Treatments, University of Valencia, Avenida Blasco Ibáñez, 21, 46010 Valencia, Spain
Institutions: Universidade de Santiago de Compostela (Spain); University College London (United Kingdom); Universitat de València (Spain); Hammersmith Hospital (United Kingdom); Imperial College London (United Kingdom)
Journal: Neuroscience of consciousness, volume 2026, issue 1, article niag009
Dates: received 24 September 2025; accepted 10 March 2026; published online 22 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/nc/niag009 · PMID 42028122 · PMCID PMC13099402 · OpenAlex W4416154274
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.11.11.686736
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Connectivity, Smoothing, state filtering, decompositions, Evoked potentials, Physiology & signal measures
Keywords: consciousness, embodiment, states of consciousness
Journal subjects: Aware & Alive: Embodied and Phenomenological Perspectives on Consciousness
Topic: Psychedelics and Drug Studies (Clinical Psychology, Psychology), according to OpenAlex
Funding: Generalitat Valenciana (CISEJI/2022/46)
Citations: not cited yet (Europe PMC); 136 references in the paper

Abstract

Self-transcendent experiences (STEs) offer profound and beneficial shifts in perspective, yet remain largely inaccessible outside elite contemplative or pharmacological contexts. Although neural measures have advanced our understanding of these states, their cost and limited ecological validity restrict broader application. This study evaluates heart rate variability (HRV) amplitude, a measure reflecting dynamic sympathovagal engagement, as a cost-effective and sustainable physiological marker of STE during ‘numadelic’ virtual reality (VR) experiences designed to dissolve self-boundaries and foster embodied presence. Building on previous work showing (i) associations between non-ordinary states of consciousness (NOSC) and autonomic activity during psychedelic administration, and (ii) comparable STE intensity in non-drug numadelic VR, we tested whether HRV amplitude reflects STE depth and relates to affective and relational outcomes during numadelic VR. Ninety-six participants engaged in guided meditation either in numadelic VR or a non-VR audio-guided group format. Cardiac and respiratory data were recorded during the session, alongside pre- and post-meditation psychological assessments. Findings show that HRV amplitude measured during numadelic VR correlates with subjective STE ratings, as well as compassion traits, and emotional improvement following practice. Reanalysis of data from a prior psychedelic study further supports the relevance of this measure across different methods of inducing NOSCs. These results advance the psychophysiological mapping of STEs and identify HRV amplitude as a promising real-time biomarker that may help guide participants toward self-transcendent states within adaptive environments. By integrating contemplative science with immersive design, this work contributes to scalable tools that broaden access to and deepen understanding of STEs.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

vbonnelle/Ripple_Study_Bonnelle2025

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 4614383aea0274a035651fae3fa067aa4cab4ea2, 16 December 2025
Languages: Jupyter (1)
Size: 13 files, 1 script
Software Heritage: not archived
Found in: “Data availability”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NeuroKit2 (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
1 file
At the source: github.com/vbonnelle/Ripple_Study_Bonnelle2025

The paper's code and data availability statement is in the Data section.

Tracing map

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  • 1 script, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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Data

Datasets cited

Data availability

ECG data and audio file of the audio of the pre-recorded meditation ‘Ripple’ in English are available at https://doi.org/10.5281/zenodo.17061627.

Code for data-preprocessing, final dataset for statistical analyses (with questionnaires and subjective ratings as well as physiological measures of interest) and VR experience chapters video snapshots are available at https://github.com/vbonnelle/Ripple_Study_Bonnelle2025.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 3 keywords, 1 funder, 127 references.

Cite

This paper

Bonnelle, V., Parola, G., Andreu, C., Hardy, J. L., Wall, J., Timmermann, C., Cebolla, A., Wrzesien, M., & Glowacki, D. R. (2026). Autonomic indicators of self-transcendence: insights from the numadelic VR paradigm. Neuroscience of consciousness, 2026(1), niag009. https://doi.org/10.1093/nc/niag009

BibTeX

@article{bonnelle2026autonomic,
author = {Bonnelle, Valerie and Parola, Giulia and Andreu, Catherine and Hardy, Joseph L and Wall, Justin and Timmermann, Christopher and Cebolla, Ausiàs and Wrzesien, Maja and Glowacki, David R},
title = {{Autonomic indicators of self-transcendence: insights from the numadelic VR paradigm}},
journal = {Neuroscience of consciousness},
year = {2026},
month = apr,
volume = {2026},
number = {1},
pages = {niag009},
publisher = {Oxford University Press},
issn = {2057-2107},
doi = {10.1093/nc/niag009},
url = {https://doi.org/10.1093/nc/niag009},
pmid = {42028122},
pmcid = {PMC13099402}
}

RIS

TY - JOUR
AU - Bonnelle, Valerie
AU - Parola, Giulia
AU - Andreu, Catherine
AU - Hardy, Joseph L
AU - Wall, Justin
AU - Timmermann, Christopher
AU - Cebolla, Ausiàs
AU - Wrzesien, Maja
AU - Glowacki, David R
TI - Autonomic indicators of self-transcendence: insights from the numadelic VR paradigm
T2 - Neuroscience of consciousness
J2 - Neurosci Conscious
PY - 2026
DA - 2026/04/22
VL - 2026
IS - 1
SP - niag009
SN - 2057-2107
PB - Oxford University Press
DO - 10.1093/nc/niag009
UR - https://doi.org/10.1093/nc/niag009
LA - en
ER -

CSL-JSON

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"container-title": "Neuroscience of consciousness",
"author": [
{
"family": "Bonnelle",
"given": "Valerie"
},
{
"family": "Parola",
"given": "Giulia"
},
{
"family": "Andreu",
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{
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],
"container-title-short": "Neurosci Conscious",
"volume": "2026",
"issue": "1",
"page": "niag009",
"DOI": "10.1093/nc/niag009",
"PMID": "42028122",
"PMCID": "PMC13099402",
"ISSN": "2057-2107",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/nc/niag009",
"language": "en",
"issued": {
"date-parts": [
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2026,
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22
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}
}

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