Participants

In Experiment 1, 20 participants (10 female; 20 right-handed; mean age: 26 ± 1.3 years) took part in the study. In Experiments 2 and 3, 34 participants (15 female; 31 right-handed; mean age: 26.5 ± 5.1 years) conducted the experiments. Two participants were excluded from analysis owing to the excessive movement artifacts contaminating > 50% of both respiration and EEG signals. In addition, one participant was excluded from cardiac phase analysis owing to noisy ECG signals, and two participants were excluded from resting state data analysis owing to missing trigger signals. All participants reported no history of neurological or psychiatric disorders nor cardiovascular diseases. All subjects signed a written informed consent form and were paid for their participation. All procedures were approved by the local ethics committee (Commission Cantonale d’Ethique de Genève).

Paradigm

In Experiment 1, participants performed the Kornhuber task3,21. An experimental session consisted of three blocks of 8 min. Participants were instructed to press a button on a keypad voluntarily using their right index finger. To produce one voluntary movement per roughly three respiration cycles, participants were asked to press the button every 8–12 s. Importantly, participants were asked (1) not to count any numbers (e.g., seconds) to estimate the time, (2) to avoid making regular or rhythmic button presses3,9 to maximize the spontaneity of the task. Before the real recordings, participants conducted a short training session (∼1 min) and the experimenter gave feedback on the interval and regularity of their button presses, so that participants could adjust them. Throughout the experiment, participants were acoustically isolated with continuous white noise via insert earphones (MX 365, Sennheiser), and closed eyes. We excluded the first trials in each block from the analysis (<3%), as participants often involuntarily pressed the key at the beginning of white noise. Inclusion of such trials did not affect the results. At the end of Experiment 1, resting state EEG data (for 3 min) were acquired while participants had their eyes closed.

An independent group of participants conducted Experiments 2 and 3. In Experiment 2, participants performed the Libet task5. A trial was initiated when a red dot appeared at a random location of the clock face (radius: 2˚ of visual angle). The red dot then rotated at 2560 ms per cycle. Participants were instructed to wait for at least one full rotation and after that to press the button at any time they wanted by using their right index finger. In-line with previous studies, we asked participants to avoid (1) pre-planning the location of the dot for the button press, and (2) using same intervals across trials (for previous work and instructions see refs. 5,9,37). After the participant pressed the button, the red dot disappeared immediately. After 4 s, the red dot reappeared at the same location, and the participants indicated the clock position which they first felt the conscious urge (or intention) to press the button using the keypad. After a random inter-trial interval (i.e., 4–8 s), the next trial began.

In the externally triggered task of Experiment 3, participants were asked to press the button as fast and accurately as possible after detecting a green dot, which appeared for 200 ms at the fixation point, while the red dot was rotating (i.e., as in the Libet task). Once the button was pressed the red dot disappeared immediately. After a random inter-trial interval (i.e., 4–8 s), the next trial began. The interval of the green dot appearance was based on each participant’s performance during the previous Libet experiment. Participants performed three blocks (i.e., 75 trials in total; one participant conducted 90 trials) for Experiments 2 and 3. In order to collect the individual interval data for the green dot appearance in Experiment 3, the experiment always began with the Libet task, whereas the remaining five blocks were pseudo randomized. After participants had completed Experiments 2 and 3, we acquired EEG resting state data for 5 min, while participants were asked to fixate the center of the screen.

Respiration recording and analysis

Continuous respiration signal was collected using a respiration belt (Biopac MP36, Biopac System Inc) at a sampling rate of 2000 Hz. Preprocessing and averaging were conducted using the Fieldtrip toolbox38. To determine the instantaneous respiration phase at the timing of button presses, we first bandpass filtered the continuous respiration signal between 0.2 and 0.8 Hz, and then applied the Hilbert transform (Supplementary Fig. 1). We detected inspiration peaks by correlating respiration signal with a template defined on a subject-by-subject basis10. For the MI analysis, continuous respiration data were down sampled to 512 Hz.

EEG recording and analyses

EEG signals were collected using a 64-channel active electrode EEG system (ActiveTwo system, Biosemi) at a sampling rate of 2048 Hz and online low-pass filtered at 400 Hz. Continuous EEG data were down sampled to 512 Hz and offline filtered between 0.1 and 40 Hz, following a recent observation that applying a high pass filtering at 0.1 Hz effectively reduces infra slow oscillations when computing slow brain potentials such as the RP39. EEG data were re-referenced to a common average reference, as in a recent RP study40.

RP was computed on EEG signals locked to the onset of the button press. After epoching (−4 to 1 s regarding the movement onset), trials showing excessive noise (i.e., > 3 SD) were excluded from further analysis. After artifact correction, 118 ± 24 (in Experiment 1) and 68 ± 4 epochs (in Experiment 2) were averaged in each subject to compute the RP. Baseline correction was not applied9. RPs are typically observed in fronto-central electrodes3,7,9,40. To maximize the signal-to-noise ratio, we report the RP results from electrodes that showed the highest RP amplitude among fronto-central electrodes (i.e., Cz, FCz, Fz, AFz), defined on a subject-by-subject basis.

ECG recording and analysis

ECG signals were simultaneously recorded using the above-mentioned EEG amplifier and also the same preprocessing was applied to both EEG and ECG signals as described above. Bipolar ECG electrodes were placed over the right shoulder and the bottom of the left side of the abdomen. To compute the phase of ECG signal at the button press, we applied a method based on a peak detection algorithm, as the Hilbert transform cannot be applied to the ECG signal which does not have oscillatory shape. For that, R-peaks were detected by correlating the ECG signal with a template QRS complex defined on a subject-by-subject basis10. Then the phase of the ECG signal was calculated (Supplementary Fig. 3) using the following formula: φ(t) = 2π((t – ta)/(tb − ta)), where ta and tb are the timings of two successive peaks surrounding the current time sample25.

Statistical test of breathing-voluntary action coupling

The significance of the relationship between the timing of button presses and the phase of respiration signals was tested using a permutation-based two-step process. For each participants, we first applied the Hodges–Ajne test (or Omnibus test), which assesses the uniformity of circular data such as a phase distribution without assumptions on the distribution of the data41, as implemented in the Circular Statistics Toolbox42. The Hodges–Ajne test results in a test statistic (i.e., M) defined as the minimum number of data points that can be observed in half of the circle. The null hypothesis of uniform distribution is rejected when the test statistic is smaller than the expected numbers41. Importantly, considering that the expiration duration is longer than inspiration one (see Supplementary Fig. 1), it is expected that even completely random events that are not associated with breathing cycle will be more likely observed during the expiration phase. Thus, as a second step, the computed original statistic (i.e., sum of M across all participants) was compared with the null distribution of surrogate M values that are obtained from phase shifted respiration data. For that the phase of respiration signals was cut into two segments with a random amount, and the order was swapped in each block and subjects27. We created 1000 surrogate M values that define the chance-level coupling between the respiration phase and button presses. Then, a two-sided permutation p value was obtained.

MI between RP amplitude and respiratory phase

For assessing the coupling between the respiratory phase and the RP amplitude, we first computed the RP amplitude and respiration phase in each single trial in a −4 to 0 s time-window (i.e., around one respiratory cycle) regarding the onset of the button press. Then, the RP amplitude was averaged depending on six equally sized respiration phase bins that spanned the 0–2π interval. Trials that did not result in six mean RP amplitudes across six respiration phase bins (<2% of total trials) were excluded from further analysis. Sorted RP amplitudes in each breathing phase bins were normalized (i.e., dividing by the sum of all RP amplitudes in each trial; see Fig. 3). The degree of coupling between the sorted RP amplitude and respiration phase was quantified by computing the MI15,28, which quantifies how much a given distribution of amplitudes across phase bins deviates from a uniform distribution, using mean RP amplitudes across single trials in each six respiration phase bin for each subject. Stronger phase-amplitude coupling (i.e., more deviation from uniform distribution) results in the higher MI values. MI significance was tested using a permutation test for which we first computed grand-averaged MI across all participants, using the original EEG-respiration data. Then the original grand-averaged MI was compared with the null distribution of surrogate MI values whose phase-amplitude association was disrupted by randomly shifting the respiration phase data as explained above. We created 1000 surrogate MI values which define the chance-level coupling between the RP amplitude and respiration phase. Finally, a two-sided permutation p value was obtained.

To compute the MI between respiration phase and resting state EEG amplitude, EEG data were epoched in −2 to 2 s time windows around the inspiration peak (see Fig. 4a). The same procedure was then applied between the resting state EEG amplitude and the concurrently measured respiration phase, as explained above.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.