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. 2022 Jan 16;16(5):1003–1011. doi: 10.1007/s11571-021-09766-y

Time-estimation process could cause the disappearence of readiness potential

Lipeng Zhang 1,2, Haikun Ren 1,2, Rui Zhang 1,2, Mingming Chen 1,2, Ruiqi Li 1,2, Li Shi 3,4, Dezhong Yao 1,2,5, Jinfeng Gao 1,2, Yuxia Hu 1,2,✉
PMCID: PMC9508310  PMID: 36237414

Abstract

Generally, the readiness potential (RP) is considered to be the scalp electroencephalography (EEG) activity preceding movement. In our previous study, we found early RP was absent among approximately half of the subjects during instructed action, but we did not identify the mechanism causing the disappearance of the RP. In this study, we investigated whether the time-estimation process could cause the disappearance of the RP. First, we designed experiments consisting of motor execution (ME), motor execution after time estimation (MEATE), and time estimation (TE) tasks, and we collected and preprocessed the EEG data of 16 subjects. Second, we compared the event related potential (ERP) waveform and scalp topography between ME and MEATE tasks. Then, to explore the influence of time-estimation, we analyzed the difference in ERP between MEATE and TE tasks. Finally, we used source imaging to probe the activation of brain regions during the three tasks, and we calculated the average activation amplitude of eight motor related brain regions. We found that the RP occurred in the ME task but not in the MEATE task. We also found that the waveform of the difference in ERP between the MEATE and TE tasks was similar to that of the ME task. The results of source imaging indicated that, compared to the ME task, the activation amplitude of the supplementary motor area (SMA) decreased significantly for the MEATE task. Our results suggested that the time estimation process could cause the disappearance of the RP. This phenomenon might be caused by the counteraction of neural electrical activity related to time estimation and motor preparation in the SMA.

Keywords: EEG, Readiness potential, Time estimation, Source imaging, Supplementary motor area

Introduction

Readiness potential (RP) is a slowly rising wave that can be recorded by scalp electrodes above the medial frontal cortical areas before voluntary movement (Kornhuber 1965). Recently, some researchers reported the phenomenon that some subjects did not show the RP during voluntary movement (Parés-Pujolràs 2019; Schurger et al. 2012). Our previous study also found more than half of the subjects did not show early RP in instructed movement (Zhang 2020). Generally, the RP is considered to reflect the pre-movement preparation process (Schmidt et al. 2016; Russo et al. 2017; Travers and Haggard 2021) and is typically used in brain–computer interfaces (BCIs) to decode pre-movement EEG patterns (Wang 2020; Jeong 2020). Hence, research on the RP disappearance could not only help us understand the generation mechanism of the RP but also facilitate the improvement of pre-movement decoding for BCI.

Extensive research has been conducted on the influencing factors of the RP waveform. Jo et al. proposed that the amplitude of the RP was related to the subject’s concentration (Jo et al. 2015). Similarly, a more difficult decision-making task could lead to a larger amplitude of the RP (Lin et al. 2016). However, patients with cognitive impairment (Cid-Fernandez et al. 2017) or attention deficit and hyperactivity disorder (ADHD) (Seo et al. 2017; Jarczok et al. 2019) show weaker RP. Jochumsen et al. suggested that training could influence motor-related cortical potential (MRCP) (Jochumsen et al. 2017). In addition, visual (Vercillo et al. 2018) and auditory (Wen et al. 2018) feedback could modulate the amplitude of the RP. Although the RP might be influenced by the above factors, these factors could not result in the disappearance of the RP.

As far as we know, no studies have explained the disappearance of the RP. In our previous study (Zhang 2020), to avoid the influence of the motor task cue on the RP, the subjects were asked to perform the motor task three to six seconds after the cue disappeared. The survey results after the experiment showed that some subjects paid attention to the time estimation process. Therefore, the purpose of this study is to determine whether the time estimation process could cause the disappearance of the RP. Some functional magnetic resonance imaging studies suggested that the time estimation mechanism depends on the activation of a large network, including the supplementary motor area (SMA), basal ganglia, and cerebellum (Merchant et al. 2013). Moreover, the SMA is often used for interval timing (Coull et al. 2011; Harrington et al. 2010; Meck et al. 2008; Wiener et al. 2010). Further analysis of the SMA showed that the anterior SMA (or preSMA) might be responsible for perception-based timing effects on the second scale, and the caudal SMA might be in charge of subsecond sensorimotor temporal effects (Schwartze et al. 2012).

Conventionally, there are two neural pathways for movement generation: (1) the lateral pathway connecting the sensory area and the primary motor cortex through the lateral premotor cortex generates external-stimuli driven movement (Travers and Haggard 2021), and (2) the medial pathway connecting the prefrontal cortex and the primary motor cortex through the supplementary motor area SMA generates endogenous movement (Passingham 1993; Passingham et al. 2010). the RP can be observed for both neural pathways (Jenkins et al. 2000; Russo et al. 2005; Berchicci et al. 2016). Although time estimation and movement depend on different neural pathways, the SMA that mainly generates the RP is an important node in both pathways.

In the current work, we investigated whether the RP would disappear in the time estimation task. Then, we analyzed the event related potential (ERP) of time estimation to assess the influence of time estimation on the RP. Next, we probed the activity of motor-related brain regions with the source imaging method. Therefore, we designed an experimental paradigm to study the influence of time estimation on the RP. EEG signals were recorded and analyzed. It is worth noting that the RP in the present study is generated before instructed movement, and it differs from the RP generated before voluntary movement.

Methods

Experimental design

To study the influence of time estimation on the RP, we designed an experiment consisting of motor execution (ME, task 1), motor execution after time estimation (MEATE, task 2), and time estimation (task 3), as shown in Fig. 1a. In task 1, the subjects needed to press the ‘1’ key when the pointer had rotated one full circle (3 s). In task 2, the subjects were required to estimate time as accurately as they could and then press the key (the pointer remained still). In task 3, the subjects only needed to estimate the time. The clock was still displayed on the screen for task 2 and task 3, but the pointer did not rotate. Hence, we could explore the influence of time estimation on the RP by comparing task 1 and task 2, and we could obtain the ERP of time estimation with task 3. It is worth noting that counting, guessing, and estimating time with rhythmic body movements were instructed not to during the time estimation task. To avoid the subjects’ inattention during the experiment, the examiner could observe the behavior of the subject through the camera and confirm the status of the subject by asking questions. During the experiment, if the subject felt that there was an error in the time estimation process, he or she could mark this trial by pressing the ‘0’ key.

Fig. 1.

Fig. 1

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Experimental design. a Schematic diagram of three tasks. b The timeline of one trial in the experiment

The timeline of one trial is shown in Fig. 1b. At the beginning of each trial, a fixation point “+” appeared. A green fixation point denoted that the subject needed to press the key, and a red fixation denoted that subject did not need to press the key. After 0.5 s, the screen turned black for 0.4–0.8 s. Then a clock appeared, and the subjects were required to perform one of the three tasks: green ‘+’ and rotating pointer denoted task 1, green ‘+’ and still pointer denoted task 2, and red ‘+’ and still pointer denoted task 3. For task 1 and task 2, the trial ended when the key was pressed. In task 3, the trial ended automatically after the clock displayed for 3.5–4 s. The interval between trials was 3–5 s. For each block, there were 15 trials of each task, and the order in which the 45 total trials were displayed was random. Each subject completed six blocks in the experiment. The experimental design was based on MATLAB 2016b and PSYCHTOOLBOX toolbox (Brainard 1997).

Subjects

Eighteen subjects were recruited in Zhengzhou University (S1–S18, mean age: 25.5 ± 4.3 years, including six females, all right-handed). All subjects had normal or corrected-to-normal vision and no history of psychiatric or neurological illness. The experiment was in accordance with the principles of the Helsinki declaration and was approved by the ethics committee of Zhengzhou University. All subjects were informed of the experiment process, signed informed consent, and received a monetary reward after the experiment.

Data acquisition

The 64-channel Neuroscan acquisition system was used to collect EEG data. The electrodes were consistent with the international 10–20 standard position. The experiment was completed in a closed magnetic shielding room with suitable light and temperature, and the impedance was kept below 5 kΩ during data acquisition. The sampling rate was set to 1000 Hz, and the data were recorded in DC mode. Then the power frequency interference of 50 Hz was removed, and the reference was located between the Cz and CPz positions. During the experiment, the subjects were asked to keep calm and avoid unnecessary actions that might contaminate EEG signals.

Data preprocessing

The collected EEG data were preprocessed as follows: (1) irrelevant EEG channels, such as EMG and extended channels, were removed; (2) the baseline of data was removed; (3) the data were filtered by a 0.1–45 Hz zero phase shift band-pass filter; (4) any bad channels were replaced by the surrounding channel, and any bad data segments were removed manually; The F3 channel of subject 5 and CP1 channel of subject 11 were replaced by surrounding electrodes. The Cz channel was not interpolated for all the subjects. (5) artifacts such as horizontal and vertical EOG and ECG were manually removed using the independent component analysis (ICA) method (Jung et al. 2000); (6) the reference was converted according to the reference electrode standardization technique (REST) (Yao 2001). The preprocessing was based on MATLAB 2016b and EEGLAB toolbox (Delorme and Makeig 2004). The data of two subjects had serious noise and were excluded, so the cleaned EEG data of 16 subjects were obtained finally.

Behavioral data analysis

In the experiment, we recorded the key press times to quantify the subjects’ time estimation results. Then, we calculated the difference between the actual key press time and the expected key press time. Since there was no key press action in the task 3, we did not conduct behavioral data analysis for task 3.

Event related potential analysis

We performed ERP analysis with the Fieldtrip toolbox (Oostenveld 2011) and MathWorks MATLAB scripts. We segmented the cleaned EEG data from − 2.1 to 1 s with the baseline of − 2.1 to − 2.0 s. We obtained individual ERPs by averaging all the epochs for each task. Grand-averaged ERPs were calculated across subjects. Because the RP could be recorded above the frontal-central area, we selected the Cz channel for further ERP analysis.

Source imaging analysis

Source imaging technology can be used to estimate the location of neural activity in the cerebral cortex based on scalp EEG data (He et al. 2011). We adopted the standard boundary element method (BEM) head model for each subject, which is a three-layer head model of the scalp, skull, and brain (He et al. 1987; Hamalainen and Sarvas 1989). In most cases, the header model and the channel position did not match well, so it was necessary to manually align the channel position to the head model. In this study, we used the minimum norm imaging (MNI) method to solve the inverse model and reconstruct the cortical source activity. The source model was a distributed source model (cortex_8196) consisting of 8196 vertices. Finally, we combined the ERP, head model, and source model to complete the source imaging with the fieldtrip toolbox in MATLAB 2016b.

Statistical analysis

We analyzed the behavioral data and the amplitudes of individual the RP with the paired t-test method; the paired t-test were corrected with the false discovery rate (FDR). Then, we performed cluster-based permutation analysis on the ERP and scalp topography data (Maris and Oostenveld 2007) using FieldTrip toolbox. We performed the cluster-based tests on the individual participant averages using the following parameters: one-tailed dependent samples t-test, time interval [− 2 0] s relative to motor onset, number of draws from the permutation distribution = 10,000. To test whether the two ERPs (task 1 and task 2–task 3) are the same, we calculated the Bayes factors between the ERPs at Cz channel with JASP Software (https://jasp-stats.org).

Results

Behavioral results

Figure 2a shows the key press times of all subjects in task 1 (M = 3.01, SD = 0.02) and task 2 (M = 3.11, SD = 0.09). Figure 2b shows the difference between the actual key press time and the expected key press time in task 1 (M = 0.04, SD = 0.01) and task 2 (M = 0.29, SD = 0.07). The distribution of task 1 was more concentrated compared to task 2, and there was a significant difference between the two tasks (t =  − 4.28, P = 6.52 × 10−4). Similarly, the average error time of task 1 was significantly lower than that of task 2 (t =  − 13.08, P = 1.31 × 10−9). This result was consistent with the expectation that there would be a higher time error in task 2.

Fig. 2.

Fig. 2

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Behavioral analysis results. a The key press time. b The difference between the actual key press time and the expected key press time (**P < 0.01)

ERP analysis results

Figure 3a shows the waveforms of the grand average ERP at the Cz channel for task 1 and task 2. There was a slowly rising negative potential for 1.5 s before the key press, and the amplitude of the potential reached the maximum when the action was performed. The result was consistent with the RP waveform reported in Schurger et al. (2021). However, in task 2, the RP did not appear before the key press. The results of statistical analysis indicated that the RP waveform showed significant differences in the range of [− 1 0.1] s before key press (P < 0.05). There was a difference in the early RP (− 1.5 to − 1 s), but the difference was not statistically significant, which might be due to the small amplitude and low signal-to-noise ratio in early RP. The upper two rows of Fig. 3e show the scalp topography of the RP in task 1 and task 2. The voltage of the RP in task 1 became negative in − 1.6 s and reached its peak in 0 s above the frontal-central area of the scalp. This was consistent with the reported results of relevant literature about the spatial characteristics of the RP (Schmidt et al. 2016). However, this phenomenon did not appear in task 2. Compared to task 1, the channels on the frontal-central area showed significant difference (P < 0.05) from − 1.0 s to motor onset.

Fig. 3.

Fig. 3

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The ERP analysis results. a Waveforms of grand average RP in task 1 and task 2. The thick gray line indicates the time period with significant differences in amplitude. b Waveforms of grand average RP in task 1, task 2, and task 3. c Waveforms of grand average RP in task 1 and task 2–task 3. d The amplitude of individual RP in four conditions (**P < 0.01). e The scalp topography of the RP in four conditions. The red dot means the channel showed a significant difference (P < 0.05) compared to task 1

The influence of time estimation on readiness potential

Figure 3b shows the waveforms of the grand average ERP at the Cz channel, and Fig. 3e shows the ERP’s scalp topography for task 1, task 2, and task 3. Converse to task 1, there was a positive rising potential in the anterior central area of the scalp for task 3. The activation area was similar to the RP, but the polarity of the potential was opposite. Compared to task 1, the channels on the frontal-central area showed a signicant difference (P < 0.05) from − 1.4 s to motor onset. Then, the difference between task 2 and 3 was calculated, as shown in Fig. 3c. The ERP waveform of task 2–task 3 was similar to task 1. Besides, the scalp topography of task 2–task 3 was similar to task 1 too. The statistical results of all the channels did not show Significant difference. And the Bayes factor between the two ERPs at Cz channel ranges from 1/3 to 3, as shown in Fig. 1 in supplementary material. These results suggested that only weak evidence supports that the two ERPs are not same. According to the above results, we deemed that the amplitude of the RP might be reduced by the positive wave generated by the time estimation process. To further analyze the difference in the amplitude of the RP, the amplitude of individual RP was calculated, as shown in Fig. 3e. There was a significant difference among task 1, task 2, and task 3 (P < 0.01), but there was no significant difference between task 1 and task 2–task 3. These results further confirmed that the disappearance of the RP in Task 2 was due to the cancellation of the positive potential of time estimation process.

Source imaging analysis results

Figure 4 shows the source imaging results of the three tasks. The activated brain regions of the three tasks were similar, mainly including the SMA, precentral gyrus, postcentral gyrus, and dorsolateral prefrontal cortex (DLPFC). These results were consistent with the brain areas involved in motor preparation activities reported in recent literature (Shibasaki and Hallett 2006; Cui et al. 1999; Cunnington et al. 2003; Fried et al. 2011). The activation amplitudes of these brain areas were the highest in task 3 and the weakest in task 2. The activation amplitudes of these brain regions increased continuously over time and peaked at the moment the key was pressed.

Fig. 4.

Fig. 4

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Source imaging results of the three tasks

To further quantify the activation of the activated brain regions for different tasks, we selected eight brain regions for further analysis, including the central anterior gyrus, DLPFC, SMA, and central posterior gyrus of the left and right hemispheres, as shown in Fig. 5a. The average source profile amplitudes of the eight brain areas of task 1 and task 2 are shown in Fig. 5b. Compared to task 1, the activation amplitude of the SMA of the left brain decreased significantly, from 4.33 to 2.63 nA m (a 39.3% decrease). Similarly, the activation amplitude of the SMA of the right brain decreased from 3.68 to 2.0 nA m (a 45.7% decrease). There were no obvious differences in other brain areas between these two tasks.

Fig. 5.

Fig. 5

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Analysis results of source imaging. a The sketch map of the eight brain areas (the central anterior gyrus, DLPFC, SMA, and central posterior gyrus of the left and right hemispheres). b The average source profile amplitude of the eight brain areas of task 1 and task 2

Discussion

To study the phenomenon of the RP disappearance, we designed an experiment and completed the EEG data collection of 16 subjects. We found that the RP did not appear in the MEATE task. Then, based on the TE task, we analyzed the effect of time estimation on the RP. The ERP of the TE task was a slowly rising wave in the positive direction, which was opposite to the RP’s deflection. We also found the waveform and scalp topography of the ERP’s difference between the MEATE task and TE task was similar to that of the ME task. Finally, the source imaging results showed the activation amplitude of the SMA significantly decreased in the MEATE task.

Our results suggested the RP could disappear in the MEATE task. Many factors can influence the RP waveform, such as training (Jochumsen et al. 2017), meditation (Jo et al. 2015), behavior feedback (Vercillo et al. 2018; Wen et al. 2018), and attention (Houdayer et al. 2014). Besides, some diseases can influence the RP waveforms, such as attention deficit disorder (Klimkeit et al. 2005) or traumatic head injuries (Russo et al. 2005). However, these factors could not lead to the disappearance of the RP. Generally, researchers consider the RP to be a deterministic accompanying signal before movement (Schultze-Kraft, et al. 2021). Although we reported the RP disappearance in the MEATE task, our results are not inconsistent with the classical RP interpretation.

Our analysis indicated the time estimation process might be the main reason for the disappearance of the RP. Contrary to the RP waveform (Schmidt et al. 2016; Russo et al. 2017; Schurger et al. 2021), the ERP of the time estimation process was a positive slow rising wave. Interestingly, the scalp distribution of the ERP coincided with the RP. We supposed that the disappearance of the RP was caused by the cancellation of these two potentials. Then, we found the ERP and scalp topography of the difference between the MEATE task and TE task were similar to that of the ME task. This result further confirmed our hypothesis.

The source imaging results showed the activation amplitude of the SMA obviously decreased in the MEATE task. Related studies reported that the SMA plays an important role in movement preparation (Travers and Haggard 2021; Schurger et al. 2021). Beside, the SMA is also involved in the time control process (Harrington et al. 2010; Schwartze et al. 2012). Because the MEATE task relies on the movement preparation and time control processes, the SMA should show increased neural activities in the MEATE task. Hence, we speculated opposite polarity electrical activity was produced in the SMA, and the RP’s amplitude was cancelled out during the MEATE task. Our results demonstrated the RP disappearance phenomenon was caused by the activity of the SMA during the time estimation process. This result also confirmed that the SMA was the main brain area generating the RP. Therefore, our results could not be used as evidence against the classical interpretation that the RP is a deterministic accompanying signal before movement.

On the whole, the current study suggests that the time estimation process could cause the disappearance of the RP. The time estimation and motor preparation processes are completed with two different neural circuits. The SMA plays an important role in the two neural circuits. The direct cause of the RP’s disappearance is that the two neuroelectric activities cancel each other out in the SMA. Owing to the poor spatial resolution of EEG, future studies should further investigate the specific working mechanism of the above two tasks in the SMA with functional magnetic resonance imaging.

Acknowledgements

This research was supported by Technology Project of Henan Province (No. 202102310210); Key Project of Discipline Construction of Zhengzhou University (No. XKZDQY201905); Chinese National Natural Science Foundation (No. 82001112); Medical science and Technology research project of Henan Province (No. LHGJ20190409); and National Natural Science Foundation of China (No. 61803342). We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

Data availability

The raw EEG data can be acquired by contacting the corresponding author.

Declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. Berchicci M, Spinelli D, Di Russo F. New insights into old waves: matching stimulus- and response-locked ERPs on the same time-window. Biol Psychol. 2016;117:202–215. doi: 10.1016/j.biopsycho.2016.04.007. [DOI] [PubMed] [Google Scholar]
  2. Brainard DH. The psychophysics toolbox. Spat vis. 1997;10(4):433–436. [PubMed] [Google Scholar]
  3. Cid-Fernandez S, Lindin M, Diaz F. Stimulus-locked lateralized readiness potential and performance: useful markers for differentiating between amnestic subtypes of mild cognitive impairment. J Prev Alzheimers Dis. 2017;4(1):21–28. doi: 10.14283/jpad.2016.88. [DOI] [PubMed] [Google Scholar]
  4. Coull JT, Cheng RK, Meck WH. Neuroanatomical and neurochemical substrates of timing. Neuropsychopharmacology. 2011;36(1):3–25. doi: 10.1038/npp.2010.113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cui RQ, et al. Neuroimage of voluntary movement: topography of the bereitschaftspotential, a 64-channel DC current source density study. Neuroimage. 1999;9(1):124–134. doi: 10.1006/nimg.1998.0388. [DOI] [PubMed] [Google Scholar]
  6. Cunnington R, et al. The preparation and readiness for voluntary movement: a high-field event-related fMRI study of the bereitschafts-BOLD response. Neuroimage. 2003;20(1):404–412. doi: 10.1016/s1053-8119(03)00291-x. [DOI] [PubMed] [Google Scholar]
  7. 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(1):9–21. doi: 10.1016/j.jneumeth.2003.10.009. [DOI] [PubMed] [Google Scholar]
  8. Di Russo F, et al. Abnormal motor preparation in severe traumatic brain injury with good recovery. J Neurotrauma. 2005;22(2):297–312. doi: 10.1089/neu.2005.22.297. [DOI] [PubMed] [Google Scholar]
  9. Di Russo F, et al. Beyond the "Bereitschaftspotential": action preparation behind cognitive functions. Neurosci Biobehav Rev. 2017;78:57–81. doi: 10.1016/j.neubiorev.2017.04.019. [DOI] [PubMed] [Google Scholar]
  10. Fried I, Mukamel R, Kreiman G. Internally generated preactivation of single neurons in human medial frontal cortex predicts volition. Neuron. 2011;69(3):548–562. doi: 10.1016/j.neuron.2010.11.045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Hamalainen MS, Sarvas J. Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data. IEEE Trans Biomed Eng. 1989;36(2):165–171. doi: 10.1109/10.16463. [DOI] [PubMed] [Google Scholar]
  12. Harrington DL, et al. Neural modulation of temporal encoding, maintenance, and decision processes. Cereb Cortex. 2010;20(6):1274–1285. doi: 10.1093/cercor/bhp194. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. He B, et al. Electric dipole tracing in the brain by means of the boundary element method and its accuracy. IEEE Trans Biomed Eng. 1987;34(6):406–414. doi: 10.1109/tbme.1987.326056. [DOI] [PubMed] [Google Scholar]
  14. He B, et al. Electrophysiological imaging of brain activity and connectivity-challenges and opportunities. IEEE Trans Biomed Eng. 2011;58(7):1918–1931. doi: 10.1109/TBME.2011.2139210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Houdayer E, et al. Absent movement-related cortical potentials in children with primary motor stereotypies. Mov Disord. 2014;29(9):1134–1140. doi: 10.1002/mds.25753. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Jarczok TA, et al. Bereitschaftspotential and lateralized readiness potential in children with attention deficit hyperactivity disorder: altered motor system activation and effects of methylphenidate. Eur Neuropsychopharmacol. 2019;29(8):960–970. doi: 10.1016/j.euroneuro.2019.05.003. [DOI] [PubMed] [Google Scholar]
  17. Jenkins IH, et al. Self-initiated versus externally triggered movements, II—the effect of movement predictability on regional cerebral blood flow. Brain. 2000;123:1216–1228. doi: 10.1093/brain/123.6.1216. [DOI] [PubMed] [Google Scholar]
  18. Jeong JH, et al. Decoding movement-related cortical potentials based on subject-dependent and section-wise spectral filtering. IEEE Trans Neural Syst Rehabil Eng. 2020;28:687. doi: 10.1109/TNSRE.2020.2966826. [DOI] [PubMed] [Google Scholar]
  19. Jo HG, et al. Do meditators have higher awareness of their intentions to act? Cortex. 2015;65:149–158. doi: 10.1016/j.cortex.2014.12.015. [DOI] [PubMed] [Google Scholar]
  20. Jochumsen M, et al. Quantification of movement-related EEG correlates associated with motor training: a study on movement-related cortical potentials and sensorimotor rhythms. Front Hum Neurosci. 2017;11:604. doi: 10.3389/fnhum.2017.00604. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Jung TP, et al. Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clin Neurophysiol. 2000;111(10):1745–1758. doi: 10.1016/s1388-2457(00)00386-2. [DOI] [PubMed] [Google Scholar]
  22. Klimkeit EI, et al. Motor preparation, motor execution, attention, and executive functions in attention deficit/hyperactivity disorder (ADHD) Child Neuropsychol. 2005;11(2):153–173. doi: 10.1080/092970490911298. [DOI] [PubMed] [Google Scholar]
  23. Kornhuber HH. Changes in the brain potential in voluntary movements and passive movements in man: readiness potential and reafferent potentials. Pflugers Arch Gesamte Physiol Menschen Tiere. 1965;10:1–17. [PubMed] [Google Scholar]
  24. Lin XQ, et al. Lateralized readiness potential interpret the effects of task difficulty on decision making. IEEE Int Conf Inf Autom (ICIA) 2016;2016:933–937. [Google Scholar]
  25. Maris E, Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods. 2007;164(1):177–190. doi: 10.1016/j.jneumeth.2007.03.024. [DOI] [PubMed] [Google Scholar]
  26. Meck WH, Penney TB, Pouthas V. Cortico-striatal representation of time in animals and humans. Curr Opin Neurobiol. 2008;18(2):145–152. doi: 10.1016/j.conb.2008.08.002. [DOI] [PubMed] [Google Scholar]
  27. Merchant H, Harrington DL, Meck WH. Neural basis of the perception and estimation of time. Annu Rev Neurosci. 2013;36:313–336. doi: 10.1146/annurev-neuro-062012-170349. [DOI] [PubMed] [Google Scholar]
  28. Oostenveld R, et al. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci. 2011;2011:156869. doi: 10.1155/2011/156869. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Parés-Pujolràs E, et al. Latent awareness: Early conscious access to motor preparation processes is linked to the readiness potential. Neuroimage. 2019;202:116140. doi: 10.1016/j.neuroimage.2019.116140. [DOI] [PubMed] [Google Scholar]
  30. Passingham RE. The frontal lobes and voluntary action. Oxford psychology series. New York: Oxford University Press; 1993. p. 299. [Google Scholar]
  31. Passingham RE, Bengtsson SL, Lau HC. Medial frontal cortex: from self-generated action to reflection on one's own performance. Trends Cogn Sci. 2010;14(1):16–21. doi: 10.1016/j.tics.2009.11.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Schmidt S, et al. 'Catching the waves'—slow cortical potentials as moderator of voluntary action. Neurosci Biobehav Rev. 2016;68:639–650. doi: 10.1016/j.neubiorev.2016.06.023. [DOI] [PubMed] [Google Scholar]
  33. Schultze-Kraft M, et al. Suppress me if you can: neurofeedback of the readiness potential. Eneuro. 2021 doi: 10.1523/ENEURO.0425-20.2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Schurger A, Sitt JD, Dehaene S. An accumulator model for spontaneous neural activity prior to self-initiated movement. Proc Natl Acad Sci USA. 2012;109(42):E2904–E2913. doi: 10.1073/pnas.1210467109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Schurger A, et al. What is the readiness potential? Trends Cognit Sci. 2021 doi: 10.1016/j.tics.2021.04.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Schwartze M, Rothermich K, Kotz SA. Functional dissociation of pre-SMA and SMA-proper in temporal processing. Neuroimage. 2012;60(1):290–298. doi: 10.1016/j.neuroimage.2011.11.089. [DOI] [PubMed] [Google Scholar]
  37. Seo BK, et al. Attenuated readiness potential in the absence of executive dysfunction in adults with ADHD. J Atten Disord. 2017;21(4):331–342. doi: 10.1177/1087054713508602. [DOI] [PubMed] [Google Scholar]
  38. Shibasaki H, Hallett M. What is the bereitschaftspotential? Clin Neurophysiol. 2006;117(11):2341–2356. doi: 10.1016/j.clinph.2006.04.025. [DOI] [PubMed] [Google Scholar]
  39. Travers E, Haggard P. The readiness potential reflects the internal source of action, rather than decision uncertainty. Eur J Neurosci. 2021;53(5):1533–1544. doi: 10.1111/ejn.15063. [DOI] [PubMed] [Google Scholar]
  40. Vercillo T, O'Neil S, Jiang F. Action-effect contingency modulates the readiness potential. Neuroimage. 2018;183:273–279. doi: 10.1016/j.neuroimage.2018.08.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Wang K, et al. Enhance decoding of pre-movement EEG patterns for brain-computer interfaces. J Neural Eng. 2020;17(1):016033. doi: 10.1088/1741-2552/ab598f. [DOI] [PubMed] [Google Scholar]
  42. Wen W, et al. The readiness potential reflects the reliability of action consequence. Sci Rep. 2018;8(1):11865. doi: 10.1038/s41598-018-30410-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Wiener M, Turkeltaub P, Coslett HB. The image of time: a voxel-wise meta-analysis. Neuroimage. 2010;49(2):1728–1740. doi: 10.1016/j.neuroimage.2009.09.064. [DOI] [PubMed] [Google Scholar]
  44. Yao DZ. A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiol Meas. 2001;22(4):693–711. doi: 10.1088/0967-3334/22/4/305. [DOI] [PubMed] [Google Scholar]
  45. Zhang LP, et al. Differences in intersubject early readiness potentials between voluntary and instructed actions. Front Psychol. 2020;11:529821. doi: 10.3389/fpsyg.2020.529821. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The raw EEG data can be acquired by contacting the corresponding author.

Articles from Cognitive Neurodynamics are provided here courtesy of Springer Science+Business Media B.V.

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