This study was supported by the SPF-201209176152 fund at The University of Hong Kong. We thank Dr. Helen Ma for teaching the course and recruiting MBSR participants.

This research study was approved by The University of Hong Kong Institutional Review Board. All participants provided their written informed consent prior to participating in this study.
1. Experimental Design
<img alt="Table 1" src="/files/ftp_upload/55455/table01.jpg" /> 
Table 1. MBSR Course Overview. The 8-week MBSR training course taken by the participants.
<ol>
	<li>Recruit participants. 
	NOTE: Eleven healthy adults (five females) from a local MBSR course participated in this EEG study and were 28 - 52-year-old. Participant with depression based on the Beck Depression Inventory were excluded from the study. The course followed standard MBSR training (Table 1), and participants had to commit to the training structure.</li>
	<li>EEG recording system.
	<ol>
		<li>Conduct EEG recording in a quiet room using a 128-channel EEG system consisting of an EEG cap, amplifier, headbox, and desktop computer.</li>
	</ol>
	</li>
</ol>
2. EEG and ECG Data Acquisition
NOTE: EEG data collection followed a standard procedure similar to the method described previously14. Ask each participant to wash his/her hair and scalp clean before coming to the experimental site. Inform the participant about the experimental procedures, mainly that he/she will rest normally for 10 min and do mindful breathing for another 10 min while being recorded with EEG and ECG devices.
<ol>
	<li>Clean the face area and mastoid of the participant with alcohol swabs.</li>
	<li>Using a measuring tape, measure the participant&#39;s head circumference and then choose an appropriately sized cap. Take one measurement from the nasion to the inion and another measurement across the top of the ears and over the scalp. Mark the vertex (the point at mid-distance between the nasion and inion and mid-distance between the two ears) with a soft marker pen.</li>
	<li>Set the electrode positions according to the 10-5 electrode systems15. Position the cap in such a way that the Cz electrode is above the vertex, the Nz electrode is at the nasion, the Lz electrode is at the inion, the RM electrode is at the right mastoid, and the LM electrode is at the left mastoid.
	<ol>
		<li>Place the ECG electrodes at both the left and right infraclavicular fossae.</li>
		<li>Fill the electrode holders with gel using a blunt-point syringe.</li>
		<li>Keep the impedance under 20 k&#937; for each electrode. Reduce the impedance by adjusting the electrode placement to increase contact with the scalp; add more gel if necessary.</li>
		<li>Tighten the chinstrap and ask the participant to remain still.</li>
		<li>Set the sampling rate of the EEG device to 1,000 Hz. Use the left mastoid as the original reference point.</li>
	</ol>
	</li>
	<li>Record EEG data (step 1.2.1) at the beginning of the MBSR course (within 2 weeks).
	<ol>
		<li>Have the participant perform a brief body scan to relax the whole body. Have the participant pay attention to his/her breath while breathing in and breathing out. Have each participant perform 10 min of MBSR mindful breathing (MBSR mindfulness condition) and 10 min of normal rest (control condition) during EEG data collection. This generates a pre-MBSR training dataset with two conditions. 
		NOTE: Counterbalance the sequence of mindful breathing and normal rest among the participants.</li>
	</ol>
	</li>
	<li>Record EEG data again about 1 month after the participants complete the MBSR course.
	<ol>
		<li>Ask each participant to perform 10 min of mindful breathing and 10 min of normal rest during EEG data collection. This generates a post-MBSR training dataset with two conditions.</li>
	</ol>
	</li>
</ol>
3. EEG and ECG Data Analysis
NOTE: The first four steps are for data preprocessing, and the remaining steps are for wavelet entropy computation and correlation analysis. Here, an open-source software named EEGLAB16 was used for EEG analysis, but the operations should be similar across different software. In the following, the operations that are based on EEGLAB will be provided as an example. Refer to the EEGLAB manual for details (https://sccn.ucsd.edu/wiki/EEGLAB_Wiki#EEGLAB_Tutorial).
<ol>
	<li>Use the EEG software to resample the data at 250 Hz by selecting Tools &#62; Change sampling rate. Use the Finite Impulse Response (FIR) filter for band-pass filtering with a 0.5 - 100 Hz passband by selecting Tools &#62; Filter the data &#62; Basic FIR filter.
	<ol>
		<li>To reduce noise due to the mains alternating current that is country-specific, use the short, nonlinear, Infinite Impulse Response (IIR) filter for notch filtering with a 47 - 53 Hz stopband by selecting Tools &#62; Filter the data &#62; Short non-linear IIR filter. This covers all relevant frequencies in the spectrum analysis while removing unnecessary noise.</li>
	</ol>
	</li>
	<li>Use the EEG software to visually scroll through and inspect the EEG signal by selecting Plot &#62; Channel data (scroll).
	<ol>
		<li>Identify and delete EEG segments that contain obvious muscle noise and any other strange events. Left-click and drag the mouse over bad segments to highlight them and then &#34;delete&#34; or &#34;reject&#34; the segments using the EEG software. 
		NOTE: Muscle activities such as swallowing or facial expression often generate discontinuous and irregular signals. Strange events are usually caused by head or body movements, which often lead to significant shifts (at low frequencies) of the EEG signal.</li>
		<li>Determine if there is any bad channel. Reconstruct each bad channel using the spherical interpolation method by selecting Tools &#62; Interpolate channel.</li>
	</ol>
	</li>
	<li>Use the EEG software to perform Independent Component Analysis17 (ICA) on the data by selecting Tools &#62; Run ICA. Have an experienced EEG operator visually identify and discard components of eye movement and blinking, muscle movements, and components of other possible noise by selecting Tools &#62; Reject data using ICA &#62; Reject components by map; Tools &#62; Remove components. The EEG software will reconstruct the data automatically using the retained components.</li>
	<li>Use the EEG software to rereference the data to the average of all channels before further analysis by selecting Tools &#62; Re-reference.</li>
	<li>Spectrum analysis.
	<ol>
		<li>Use the EEG software command line function &#34;spectopo&#34; to compute the spectra of EEG using Welch&#39;s power spectral density estimation18 and obtain the log powers (dB) of delta (1 - 4 Hz), theta (4 - 8 Hz), alpha (8 - 12 Hz), beta (12 - 30 Hz), and gamma (30 - 80 Hz) waves.</li>
		<li>Calculate the relative power of each type of wave18 (i.e.&#160;the proportion of power in the respective frequency band relative to the total spectrum power).</li>
	</ol>
	</li>
	<li>Perform EEG source analysis using the Statistical Parametric Mapping (SPM) approach.
	<ol>
		<li>Use minimum norm estimation19 to transform the EEG signals of the scalp electrodes into 3D brain source signals. 
		NOTE: Here, the original channel number was 122, and it was transformed to 8,196 channels distributed in the three-dimensional brain model. The significance level was set at p = 0.01. Please refer to the source analysis in the SPM manual (www.fil.ion.ucl.ac.uk/spm/doc/spm8_manual.pdf).</li>
	</ol>
	</li>
	<li>Calculate the wavelet entropy for each EEG channel (as well as for the estimated three-dimensional brain source signal) using the following procedure:
	<ol>
		<li>Given a signal <img alt="Equation 1" src="/files/ftp_upload/55455/55455eq1.jpg" />, calculate the wavelet coefficients as <img alt="Equation 2" src="/files/ftp_upload/55455/image002.jpg" />, where <img alt="Equation 3" src="/files/ftp_upload/55455/image003.jpg" /> is an orthonormal basis of the Haar wavelet family and <img alt="Equation 4" src="/files/ftp_upload/55455/image004.jpg" /> represents the decomposition levels (N = 10 in this study).</li>
		<li>Define the relative energy as <img alt="Equation 5" src="/files/ftp_upload/55455/image005.jpg" />, with <img alt="Equation 6" src="/files/ftp_upload/55455/image006.jpg" />.</li>
		<li>Calculate the wavelet entropy as <img alt="Equation 7" src="/files/ftp_upload/55455/image007.jpg" />. Average the wavelet entropy for all channels to obtain the EEG wavelet entropy for each participant.</li>
	</ol>
	</li>
	<li>Use the software package to perform a peak analysis on the ECG data to obtain the heartbeat signal, which consists of the number of ECG wave peaks and the interval between the peaks. 
	NOTE: Assuming a normal heartrate of 60-100 beats/min in both the normal rest and mindful breathing states, the number of heartbeat instances N for a 10 min duration should be relatively small, satisfying 600 &#60; N &#60; 1,000.
	<ol>
		<li>To obtain reliable wavelet entropy from the heartbeat signal, use a sliding window of 500 points and a step increment of 10 points to compute a series of wavelet entropies using the same procedure as for EEG, elaborated in section 3.7. Use the mean value as the final wavelet entropy.</li>
	</ol>
	</li>
	<li>Analyze the brain and heart correlation across subjects between the wavelet entropy of ECG and that of EEG at every channel of the scalp using any statistical software that has a Pearson correlation function.
	<ol>
		<li>Use the EEG software command line function &#34;topoplot&#34; to construct a statistical scalp map for the correlation. Plot a linear correlation graph with the heart rate entropy and the average EEG entropy of only those channels that are significantly correlated with heartrate entropy (see the Representative Results section).</li>
	</ol>
	</li>
</ol>

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The authors declare that they have no competing financial interests.

This EEG study provides concrete evidence that, compared to normal rest, MBSR mindful breathing enhances the alpha waves in the frontal and occipital lobes. This is in line with the majority of reports on increased alpha waves during meditation, especially in beginners21. Alpha waves usually indicate a state of arousal and vigilance and freedom from any specific task. Such a mental state is one of the objectives of meditation; that is, keeping the mind clean and sharp, free from either flights of fancy or from falling asleep. The enhanced alpha wave synchrony during MBSR practice may also enhance the coordination between mind and body22.
The electronic activities of the vast number of neurons in the brain form a nonlinear hierarchical system, and its irregularity can be well measured by entropy8. We found that during MBSR practice, the irregularity of brain electronic activities decreased across a large part of the scalp EEG, especially in the frontal pole and bilateral occipital brain regions. Further source analysis of wavelet entropy showed that the irregularity of occipital and precuneus activities was reduced during MBSR practice. Other areas involved include the right-middle cingulate and the superior temporal lobe. This is in line with another study which indicates that gray matter increases in the precuneus after MBSR training23. Advanced meditation practitioners usually have higher parieto-occipital EEG gamma waves during non-rapid eye movement sleep1. One important feature of MBSR training is to be nonjudgmental of the mind/brain information flow. This may potentially reduce the irregularity of brain activities. Because a main information source in the brain is the visual cortex, it is plausible that the visual-related brain regions are more affected by MBSR practice than are other regions. This enables the mind to become less responsive to irrelevant information and to pay more attention to mindful breathing.
Wavelet entropy analysis showed less irregularity of heartrate during mindful breathing compared to the normal rest state, which implies a more refined balance of the autonomic nervous system due to relatively unwavering interactions between parasympathetic and sympathetic tones. Mindful breathing may also affect the heartrate because the lung sensory afferents send inhibitory projections to the cardiac vagal motor neurons in the mid-brain, which inhibit the heartrate via the vagus nerve24. The cardiorespiratory interaction is mediated by the brainstem and limbic system in the brain25. Given the connection between the central nervous system and cardiac activity, it is plausible that, by training the mind, MBSR or other meditation practices can also influence other body functions, such as resetting the baroreflex sensitivity, improving gas exchange efficiency in the lungs, and balancing the autonomic nervous system26.
The mind-body connection is emphasized in many Eastern traditions. In this respect, our study provides the first objective evidence in terms of entropy. The irregularities of brain and heart activities became more coordinated during MBSR practice, and the coordination was greatest in the bilateral somatosensory regions. This region is directly in charge of the body&#39;s sensory input and movement, and this increased coordination implies greater body awareness during MBSR practice. Mindfulness training was found to increase the activities of the viscerosomatic areas in a functional MRI study, which has better spatial resolution than EEG27. Our results suggest that the brain and the heart may become more entrained during MBSR practice, offloading unnecessary entropy in the system28. Because the heartbeat is regulated by the autonomic nervous system, wherein the autonomic tone is modulated by the interactions between the ganglion; sino-auricular node; vagus nerves; and higher-order brain structures, such as the medulla oblongata, insula, and the limbic system, the autonomic and central nervous systems may well become more coordinated during MBSR practice.
One important step in a study such as ours is to choose the right entropy index to measure the irregularity of EEG and ECG. Unlike Renu Madhavi et al., who used approximate entropy29, we have chosen to use wavelet entropy to focus on the complexity among the energies in different scales. Because the brain electronic activities originate from a vast number of neurons that have different characteristics in different scales and spectrums, wavelet entropy should be more suitable for measuring the irregularity of EEG30. Similarly, instantaneous heartrate also has different features under different time scales, so it is reasonable to apply wavelet entropy to ECG31. Furthermore, Shannon&#39;s entropy and approximate entropy are sensitive to sampling frequency, and approximate entropy requires large computation times that increase exponentially with data length, this in addition to the calculation in the source space.
Several limitations of the current study are worth noting. First, the spatial resolution of scalp EEG is relatively poor, despite its high temporal resolution. To address this, source analysis was applied in our study. EEG source reconstruction is an ill-posed problem, and an alternative approach is to have spatial priors from previous functional Magnetic Resonance Imaging (fMRI) studies, which can help to improve the reconstruction accuracy32,33. Another limitation is that the coordination between the brain and heart activities was assessed between subjects; within-subject coordination is left to future studies. Additionally, the entropy of ECG is not derived directly from the ECG signal, but rather from the heartrate interval, because the amplitude of ECG can be greatly influenced by the electrode position and skin impedance and is thus not comparable between subjects.
Previous studies usually performed a spectrum analysis of the EEG signal, dismissing irregularity in EEG as noise. In contrast, we propose to use wavelet entropy as a more sensitive index to measure the chaotic electronic activities of the brain as a reflection of the mind state. The proposed methodology provides objective evidence of the traditional assumption that the mind and heart are more coordinated during meditation.
Because wavelet entropy can monitor meditation state quite sensitively, it may be used in the mental training field, just as entropy is used in clinical practice to measure the level of anesthesia during surgery. Using an index similar to wavelet entropy, wireless EEG devices can be used to conveniently monitor an individual&#39;s daily meditation practice or other mental training for educational purposes. Nonetheless, because a wireless EEG can usually only stably measure the channels in the forehead, we need to explore whether these channels are sensitive enough to monitor state of mind.
The critical steps of this procedure include keeping the skin impedance for each electrode low for better signal quality and less noise; appropriately and properly conducting MBSR training; ensuring a good, non-fatigued mental state before the experiment, as meditation requires great energy; choosing the proper entropy index (wavelet entropy); and being familiar with SPM source analysis of EEG data.

With advances in neuroscience and medicine1, we are in a much better position to understand the non-pharmacological benefits of mindfulness and meditation on mental and physical diseases. Numerous studies have shown that Mindfulness-based Stress Reduction (MBSR) training as a kind of mental exercise can actively reduce unnecessary stress in the mind and the body2. During the practice of meditation, the body and mind may become more entrained. These issues have attracted the interest of both the academic community and the general public and have motivated us to design a methodology to explore potential coordination between mind and body (or more specifically, between the brain and the heart) under different conditions to assess the effect of MBSR training and practice.
The irregularity of electroencephalograms (EEG) is usually regarded as noise in the conventional EEG spectrum analysis, but it may contain useful information and can be measured by signal entropy. EEG entropy has been used as an index of clinical conditions such as epilepsy, Alzheimer&#39;s disease, and anesthesia, and some evidence suggests that the entropy of EEG may be a more sensitive measure for monitoring brain states than the traditional EEG spectrum analysis3,4. Entropy has long been used in thermodynamics as an index to describe the chaotic state of a system. The brain waves measured by EEG are known for their dynamic activity due to brain networks operating between order and chaos5. Among the various kinds of entropies, wavelet entropy offers a method to calculate the entropy of energies under different observation scales, which is well-suited for analyzing EEG signals with highly varied features in different scales. For a completely random signal, the relative energies at different resolution levels will be the same, and the entropy will reach its maximum. Hence, the wavelet entropy serves as a measure of chaotic activities in EEG and can be used as an index for differentiating different mind states6.
Previous electrocardiogram (ECG) studies provided consistent evidence of meditation-related changes in heartrate variability when compared to pre-meditation states7. Heartbeat is regulated by the autonomic (including the sympathetic and parasympathetic) nervous system. The autonomic tone is modulated by interactions between the ganglion; sino-auricular node; vagus nerves; and higher-order brain structures, such as the medulla oblongata, the insula, and the limbic system. These structures form a hierarchical system, and its nonlinear activity can be measured through entropy8. For example, the neural bursts in higher-order brain structures can influence the heartrate rhythm9. In clinical conditions, brain disorders such as epileptic seizures disrupt the heart rhythm10. Neurocardiology research also suggests a close interplay between the heart and the brain11.
One difficulty in examining the association between brain and heart signals is that they are quite different in signal content and time scale. Hence, a major challenge is to identify a common measure that is not only applicable to both brain and heart signals, but also meaningful for interpretation in the context of mind-body coordination. In this paper, we propose the use of wavelet entropy to detect possible coordination between irregularities in brain and heart activities by correlating the entropies of EEG and heartbeat signals, which can then be evaluated by way of an MBSR experiment.
Because MBSR involves mindfulness practice in both body and mind, we hypothesize that MBSR mindful breathing practice may affect not only brain activity, but also the heartbeat. Previous studies mainly investigated the effect of mindfulness training and/or practice on the brain or the heart separately and based on different aspects; the simultaneous changes in these two closely related systems remain largely unexplored. Only a few studies reported better coordination of the body and mind after mindfulness training12. Using the proposed methodology, the representative results and a previous study13 showed that MBSR mindfulness training may increase the coordination of the body and mind in terms of their chaotic activities, which may offer new insight into the effects of mindfulness training on the central and peripheral nervous systems.

<table><tbody><tr><td>Synamps 2/RT 128-channel EEG/EP/ERP</td><td>Compumedics NeuroScan</td><td>Model 8050</td><td>Amplifiers and&lt;br/&gt; EEG acquisition</td></tr><tr><td>Quik-CapEEG 128 electrodes</td><td>Compumedics NeuroScan</td><td></td><td>EEG caps</td></tr><tr><td>Quik-Gel</td><td>Compumedics NeuroScan</td><td></td><td>conductive electrode gel</td></tr><tr><td>EEGLAB</td><td>Swartz Center for Computational Neuroscience</td><td></td><td>EEG analysis software</td></tr><tr><td>Matlab R2011a</td><td>MathWorks</td><td></td><td>EEGLAB is based on Matlab</td></tr><tr><td>SPM</td><td>Wellcome Trust Centre for Neuroimaging</td><td></td><td>EEG source analysis software</td></tr></tbody></table>

using wavelet entropy to demonstrate how mindfulness practice increases coordination between irregular cerebral and cardiac activities

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One difficulty in proving this objectively is that the natures of brain and heart activities are quite different. In this paper, we propose a methodology that uses wavelet entropy to measure the chaotic levels of both electroencephalogram (EEG) and electrocardiogram (ECG) data and show how this may be used to explore the potential coordination between the mind and heart under different experimental conditions. Furthermore, Statistical Parametric Mapping (SPM) was used to identify the brain regions in which the EEG wavelet entropy was the most affected by the experimental conditions. As an illustration, the EEG and ECG were recorded under two different conditions (normal rest and mindful breathing) at the beginning of an 8-week standard Mindfulness-based Stress Reduction (MBSR) training course (pretest) and after the course (posttest). Using the proposed method, the results consistently showed that the wavelet entropy of the brain EEG decreased during the MBSR mindful breathing state as compared to that during the closed-eye resting state. Similarly, a lower wavelet entropy of heartrate was found during MBSR mindful breathing. However, no difference in wavelet entropy during MBSR mindful breathing was found between the pretest and posttest. No correlation was observed between the entropy of brain waves and the entropy of heartrate during normal rest in all participants, whereas a significant correlation was observed during MBSR mindful breathing. Additionally, the most well-correlated brain regions were located in the central areas of the brain. This study provides a methodology for the establishment of evidence that mindfulness practice (i.e., mindful breathing) may increase the coordination between mind and heart activities.

Spectrum Analysis
In the spectrum analysis of EEG data, compared to normal rest, there were enhanced alpha (8 - 12 Hz) and beta (12 - 30 Hz) and reduced delta (1 - 4 Hz) waves during MBSR mindful breathing. The increment of alpha waves was globally significant, especially in the frontal and occipital lobes, whereas the increment of beta waves was mainly in the frontal lobe. Decreased delta waves were noted in the central-parietal areas (Figure 1). However, we did not find significant change between pre- and post-MBSR training.
<img alt="Figure 1" src="/files/ftp_upload/55455/55455fig1.jpg" /> 
Figure 1. Spectrum Analysis of MBSR Mindful Breathing and Normal Rest Conditions. Spectrum analysis shows that MBSR practice can change brain activities, as indicated by the different spectrums of EEG. Original source: Reference 13. <a href="http://cloudflare.jove.com/files/ftp_upload/55455/55455fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Figure 2 illustrates the defined region of interest: the occipital lobe (channels N19 - 21, N41 - 46, N67 - 72, N96 - 100, and N119), the middle frontal lobe (channels N53 - 61 and N79 - 83), and the middle parietal lobe (channels N48 - 50, N64 - 66, and N74 - 76). Table 2 shows the power of each spectrum and the entropy under MBSR mindful breathing and normal rest conditions, along with the p-value of the t-test of the difference between the two conditions for each region of interest. We combined the pre- and post-MBSR training EEG data since there was no difference between the two stages.
<img alt="Figure 2" src="/files/ftp_upload/55455/55455fig2.jpg" /> 
Figure 2. Defined Region of Interest. The defined areas of EEG channels that represent the middle frontal lobe, middle parietal lobe, and occipital lobe. <a href="http://cloudflare.jove.com/files/ftp_upload/55455/55455fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td></td>
			<td>Delta</td>
			<td>Theta</td>
			<td>Alpha</td>
			<td>Beta</td>
			<td>Gamma</td>
			<td>Entropy</td>
		</tr>
		<tr>
			<td>Middle Frontal Lobe</td>
			<td>MBSR</td>
			<td>18.96 &#177; 3.10</td>
			<td>22.29 &#177; 5.86</td>
			<td>30.90 &#177; 7.76</td>
			<td>0.12 &#177; 43.77</td>
			<td>-467.25 &#177; 79.48</td>
			<td>0.753 &#177; 0.060</td>
		</tr>
		<tr>
			<td></td>
			<td>Rest</td>
			<td>22.40 &#177; 6.56</td>
			<td>22.76 &#177; 5.98</td>
			<td>26.91 &#177; 7.14</td>
			<td>-7.11 &#177; 42.27</td>
			<td>-449.76 &#177; 102.92</td>
			<td>0.785 &#177; 0.066</td>
		</tr>
		<tr>
			<td></td>
			<td>p-value</td>
			<td>0.0243</td>
			<td>0.6555</td>
			<td>0.0085</td>
			<td>0.0114</td>
			<td>0.4419</td>
			<td>0.0084</td>
		</tr>
		<tr>
			<td>Occipital Lobe</td>
			<td>MBSR</td>
			<td>15.39 &#177; 3.30</td>
			<td>17.91 &#177; 6.53</td>
			<td>33.38 &#177; 6.50</td>
			<td>-8.03 &#177; 40.72</td>
			<td>-466.23 &#177; 61.46</td>
			<td>0.719 &#177; 0.048</td>
		</tr>
		<tr>
			<td></td>
			<td>Rest</td>
			<td>19.82 &#177; 6.32</td>
			<td>20.38 &#177; 8.11</td>
			<td>29.73 &#177; 5.30</td>
			<td>-11.62 &#177; 40.22</td>
			<td>-439.03 &#177; 102.81</td>
			<td>0.763 &#177; 0.055</td>
		</tr>
		<tr>
			<td></td>
			<td>p-value</td>
			<td>0.0134</td>
			<td>0.1213</td>
			<td>0.0125</td>
			<td>0.0796</td>
			<td>0.8936</td>
			<td>0.0098</td>
		</tr>
		<tr>
			<td>Parietal Lobe</td>
			<td>MBSR</td>
			<td>17.95 &#177; 3.60</td>
			<td>19.46 &#177; 6.54</td>
			<td>32.84 &#177; 6.68</td>
			<td>2.68 &#177; 38.23</td>
			<td>-487.72 &#177; 104.13</td>
			<td>0.738 &#177; 0.072</td>
		</tr>
		<tr>
			<td></td>
			<td>Rest</td>
			<td>21.24 &#177; 6.37</td>
			<td>21.16 &#177; 8.00</td>
			<td>29.79 &#177; 6.60</td>
			<td>-1.27 &#177; 34.42</td>
			<td>-490.01 &#177; 123.83</td>
			<td>0.764 &#177; 0.075</td>
		</tr>
		<tr>
			<td></td>
			<td>p-value</td>
			<td>0.0157</td>
			<td>0.0963</td>
			<td>0.0177</td>
			<td>0.1507</td>
			<td>0.2878</td>
			<td>0.0368</td>
		</tr>
	</tbody>
</table>
Table 2. Spectrum and Entropy across Brain Areas. MBSR mindful breathing and normal rest conditions are compared among the three predefined Regions of Interest (ROI).
Wavelet Entropy Analysis
Analysis of the wavelet entropy of EEG showed decreased EEG entropy during MBSR mindful breathing compared to normal rest, both for pre- and post-MBSR training. Because there was no significant difference between the two stages, they were merged to produce an averaged map (shown in the third row of Figure 3). The main areas with decreased entropy were at the frontal lobe and the parietal-occipital lobe.
<img alt="Figure 3" src="/files/ftp_upload/55455/55455fig3.jpg" /> 
Figure 3. Wavelet Entropy Analysis. Rest 1 and MBSR 1 denote pre-MBSR training, whereas Rest 2 and MBSR 2 denote post-MBSR training. Wavelet entropy analysis shows that MBSR practice can reduce the irregularity of brain electronic activities. Original source: Reference13. <a href="http://cloudflare.jove.com/files/ftp_upload/55455/55455fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Source Analysis of Wavelet Entropy
Source analysis of the EEG signals can improve the spatial resolution by deconvolving the scalp EEG into electrical activities over the cortical surface20. The analysis shows that the major brain regions affected by the MBSR mindfulness training were in the left-middle occipital lobe, precuneus, superior temporal lobe, and left fusiform (Figure 4). In Table 3, for the four anatomical labels obtained from the source analysis, we provide t-test results for the difference in the entropies of two different states, MBSR mindfulness state versus normal rest.
<img alt="Figure 4" src="/files/ftp_upload/55455/55455fig4.jpg" /> 
Figure 4. Source Analysis. The source analysis shows that the entropies of various brain regions (highlighted in red) decrease during the MBSR mindful breathing state. <a href="http://cloudflare.jove.com/files/ftp_upload/55455/55455fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Anatomical label</td>
			<td>voxels</td>
			<td>x, y, z</td>
			<td>t-value</td>
		</tr>
		<tr>
			<td>middle temporal gyrus L</td>
			<td>1,728</td>
			<td>-44 -60 16</td>
			<td>3.77</td>
		</tr>
		<tr>
			<td>precuneus R</td>
			<td>1,324</td>
			<td>&#160; 2 -56 28</td>
			<td>3.63</td>
		</tr>
		<tr>
			<td>occipital L</td>
			<td>749</td>
			<td>&#160;-2 -102 -12</td>
			<td>3.61</td>
		</tr>
		<tr>
			<td>fusiform L</td>
			<td>142</td>
			<td>-102</td>
			<td>3.51</td>
		</tr>
	</tbody>
</table>
Table 3. Source Analysis Report. Anatomical brain regions with significant differences in entropy between MBSR mindful breathing and normal rest conditions. L, the left side of brain. R, the right side of the brain (p &#60; 0.001, t-test, uncorrected).
Heart Rate Wavelet Entropy
The analysis revealed no significant difference in average heartrate, but the heartrate entropy was lower during MBSR mindful breathing, both pre- and post-MBSR training (Table 4).
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>Rest1</td>
			<td>MBSR1</td>
			<td>Rest2</td>
			<td>MBSR2</td>
		</tr>
		<tr>
			<td>Heart Rate (beats per minute)&#160;</td>
			<td>68.2 &#177; 9.5</td>
			<td>67.7 &#177; 9.3</td>
			<td>71.8 &#177; 8.1</td>
			<td>70.7 &#177; 8.4</td>
		</tr>
		<tr>
			<td>Heart Rate Wavelet Entropy</td>
			<td>0.89 &#177; 0.05</td>
			<td>0.79 &#177; 0.11*</td>
			<td>0.89 &#177; 0.07</td>
			<td>0.80 &#177; 0.12#</td>
		</tr>
	</tbody>
</table>
Table 4. Heartrate and Heartrate Wavelet Entropy of MBSR Mindful Breathing and Normal Rest Conditions. The two conditions are compared at both pre- and post-MBSR training. *Significant difference (p &#60;0.05) between Rest 1 and MBSR 1. #Significant difference (p &#60;0.05) between Rest 2 and MBSR 2.
Correlation between Heart and Brain Activities
Given the potential connection between the brain and the heart, we analyzed the correlation between the whole-brain EEG entropy (i.e.&#160;the average of EEG entropies over all EEG channels for a single subject) and the heartrate entropy during MBSR mindful breathing and normal resting states. The entropies of the brain and heart were significantly correlated during MBSR mindful breathing but not during normal rest. As shown in the upper part of Figure 5, the significance of the correlation between the entropy of the EEG in each channel and the heartrate entropy is most prominent in the central part of the brain. The lower plot shows the correlation between the EEG entropy in the central regions (average of those dotted channels that are significantly correlated with heartrate entropy) and heartrate entropy.
<img alt="Figure 5" src="/files/ftp_upload/55455/55455fig5.jpg" /> 
Figure 5. Correlation between the Wavelet Entropies of Electronic Activities of the Heart and Brain. The wavelet entropy of brain activity is the average of the significant channels in the central region, as shown in the upper-right figure. The wavelet entropy of the heart is based on the heartrate interval. The correlation (lower panel) is calculated across all subjects for both pre- and post-MBSR training results. Original source: Reference 13. <a href="http://cloudflare.jove.com/files/ftp_upload/55455/55455fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

This manuscript describes how to use the wavelet entropy index to analyze high-density electroencephalography (EEG) and electrocardiography (ECG) data. We show that the irregularity of cerebral and cardiac activities became more coordinated during mindfulness-based stress reduction practice.

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One ...

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14.6K Views.  The University of Hong Kong. This manuscript describes how to use the wavelet entropy index to analyze high-density electroencephalography (EEG) and electrocardiography (ECG) data. We show that the irregularity of cerebral and cardiac activities became more coordinated during mindfulness-based stress reduction practice.

Video: Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Theta activity is generated in the septohippocampal system and can be recorded using deep intrahippocampal electrodes and implantable electroencephalography (EEG) radiotelemetry or tether system approaches. Pharmacologically, hippocampal theta is heterogeneous (see dualistic theory) and can be differentiated into type I and type II theta. These individual EEG subtypes are related to specific cognitive and behavioral states, such as arousal, exploration, learning and memory, higher integrative functions, etc. In neurodegenerative diseases such as Alzheimer&#39;s, structural and functional alterations of the septohippocampal system can result in impaired theta activity/oscillations. A standard quantitative analysis of the hippocampal EEG includes a Fast-Fourier-Transformation (FFT)-based frequency analysis. However, this procedure does not provide details about theta activity in general and highly-organized theta oscillations in particular. In order to obtain detailed information on highly-organized theta oscillations in the hippocampus, we have developed a new analytical approach. This approach allows for time- and cost-effective quantification of the duration of highly-organized theta oscillations and their frequency characteristics.

Theta activity in the hippocampus is related to specific cognitive and behavioral stages. Here, we describe an analytical method to detect highly-organized theta oscillations within the hippocampus using a time-frequency (i.e., wavelet analysis)-based approach.

Theta activity is generated in the septohippocampal system and can be recorded using deep intrahippocampal electrodes and implantable ...

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain signal. Typically, researchers represent the brain's response as the mean across repeated experimental trials and disregard signal fluctuations over time as "noise". However, it is becoming clear that brain signal variability conveys meaningful functional information about neural network dynamics. This article describes the novel method of multiscale entropy (MSE) for quantifying brain signal variability. MSE may be particularly informative of neural network dynamics because it shows timescale dependence and sensitivity to linear and nonlinear dynamics in the data.

Neuroimaging researchers typically consider the brain's response as the mean activity across repeated experimental trials and disregard signal variability over time as "noise". However, it is becoming clear that there is signal in that noise. This article describes the novel method of multiscale entropy for quantifying brain signal variability in the time domain.

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain ...

Neuroscience

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard scores from paper and pencil symptom- measures traditionally used to track disease progression are too coarse (discrete) to capture the granularity of the clinical phenomena under consideration, in the face of tremendous symptom diversity. For this reason, sensors, wearables, and mobile devices are increasingly incorporated into PD research and routine care. These digital measures, while more precise, yield data that are less standardized and interpretable than traditional measures, and consequently, the two types of data remain largely siloed. Both of these issues present barriers to the broad clinical application of the field&#8217;s most precise assessment tools. This protocol addresses both problems. Using traditional tasks to measure cognition and motor control, we test the participant, while co-registering biophysical signals unobtrusively using wearables. We then integrate the scores from traditional paper-and-pencil methods with the digital data that we continuously register. We offer a new standardized data type and unifying statistical platform that enables the dynamic tracking of change in the person&#8217;s stochastic signatures under different conditions that probe different functional levels of neuromotor control, ranging from voluntary to autonomic. The protocol and standardized statistical framework offer dynamic digital biomarkers of physical and cognitive function in PD that correspond to validated clinical scales, while significantly improving their precision.&#160;

This protocol offers a digitization of portions of traditional clinical tasks commonly used to measure cognition and motor control in Parkinson&#8217;s disease. Clinical tasks are digitized while biophysical rhythms are co-registered from different functional levels of the nervous systems, ranging from voluntary, spontaneous, automatic to autonomic.

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard ...

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data and have been widely used to investigate the neural mechanisms in some brain disorders. The goal of this article is to describe the protocol underlying EEG microstate and omega complexity analyses step by step. The main advantage of these two measures is that they could eliminate the reference-dependent problem inherent to traditional spectrum analysis. In addition, microstate analysis makes good use of high time resolution of resting-state EEG, and the four obtained microstate classes could match the corresponding resting-state networks respectively. The omega complexity characterizes the spatial complexity of the whole brain or specific brain regions, which has obvious advantage compared with traditional complexity measures focusing on the signal complexity in a single channel. These two EEG measures could complement each other to investigate the brain complexity from the temporal and spatial domain respectively.

This article describes the protocol underlying electroencephalography (EEG) microstate analysis and omega complexity analysis, which are two reference-free EEG measures and highly valuable to explore the neural mechanisms of brain disorders.

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of ...

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory

Although there has been recent interest in how mindfulness meditation can affect episodic memory as well as brain structure and function, no study has examined the behavioral and neural effects of mindfulness meditation on episodic memory. Here we present a protocol that combines mindfulness meditation training, an episodic memory task, and EEG to examine how mindfulness meditation changes behavioral performance and the neural correlates of episodic memory. Subjects in a mindfulness meditation experimental group were compared to a waitlist control group. Subjects in the mindfulness meditation experimental group spent four weeks training and practicing mindfulness meditation. Mindfulness was measured before and after training using the Five Facet Mindfulness Questionnaire (FFMQ). Episodic memory was measured before and after training using a source recognition task. During the retrieval phase of the source recognition task, EEG was recorded. The results showed that mindfulness, source recognition behavioral performance, and EEG theta power in right frontal and left parietal channels increased following mindfulness meditation training. In addition, increases in mindfulness correlated with increases in theta power in right frontal channels. Therefore, results obtained from combining mindfulness meditation training, an episodic memory task, and EEG reveal the behavioral and neural effects of mindfulness meditation on episodic memory.

Here we present a protocol for combining mindfulness meditation training, an episodic memory task, and EEG to understand the behavioral and neural effects of mindfulness meditation on episodic memory.

Although there has been recent interest in how mindfulness meditation can affect episodic memory as well as brain structure and function, no study has ...

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

The dynamics between coupled brains of individuals have been increasingly represented by inter-brain synchronization (IBS) when they coordinate with each other, mostly using simultaneous-recording signals of brains (namely hyperscanning) with fNIRS. In fNIRS hyperscanning studies, IBS has been commonly assessed through the wavelet transform coherence (WTC) method because of its advantage on expanding time series into time-frequency space where oscillations can be seen in a highly intuitive way. The observed IBS can be further validated via the permutation-based random pairing of the trial, partner, and condition. Here, a protocol is presented to describe how to obtain brain signals via fNIRS technology, calculate IBS through the WTC method, and validate IBS by permutation in a hyperscanning study. Further, we discuss the critical issues when using the above methods, including the choice of fNIRS signals, methods of data preprocessing, and optional parameters of computations.&#160;In summary, using the WTC method and permutation is a potentially standard pipeline for analyzing IBS in fNIRS hyperscanning studies, contributing to both the reproducibility and reliability of IBS.

The dynamics between coupled brains of individuals have been increasingly represented by inter-brain synchronization (IBS) when they coordinate with each ...

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Social interaction is of vital importance for human beings. While the hyperscanning approach has been extensively used to study interpersonal neural synchronization (INS) during social interactions, functional near-infrared spectroscopy (fNIRS) is one of the most popular techniques for hyperscanning naturalistic social interactions because of its relatively high spatial resolution, sound anatomical localization, and exceptionally high tolerance of motion artifacts. Previous fNIRS-based hyperscanning studies usually calculate a time-lagged INS using wavelet transform coherence (WTC) to describe the direction and temporal pattern of information flow between individuals. However, the results of this method might be confounded by the autocorrelation effect of the fNIRS signal of each individual. For addressing this issue, a method termed partial wavelet transform coherence (pWTC) was introduced, which aimed to remove the autocorrelation effect and maintain the high temporal-spectrum resolution of the fNIRS signal. In this study, a simulation experiment was performed first to show the effectiveness of the pWTC in removing the impact of autocorrelation on INS. Then, step-by-step guidance was offered on the operation of the pWTC based on the fNIRS dataset from a social interaction experiment. Additionally, a comparison between the pWTC method and the traditional WTC method and that between the pWTC method and the Granger causality (GC) method was drawn. The results showed that pWTC could be used to determine the INS difference between different experimental conditions and INS's directional and temporal pattern between individuals during naturalistic social interactions. Moreover, it provides better temporal and frequency resolution than the traditional WTC and better flexibility than the GC method. Thus, pWTC is a strong candidate for inferring the direction and temporal pattern of information flow between individuals during naturalistic social interactions.

This protocol describes partial wavelet transform coherence (pWTC) for calculating the time-lagged pattern of interpersonal neural synchronization (INS) to infer the direction and temporal pattern of information flow during social interaction. The effectiveness of pWTC in removing the confounds of signal autocorrelation on INS was proved by two experiments.

Social interaction is of vital importance for human beings. While the hyperscanning approach has been extensively used to study interpersonal neural ...

A Visuospatial Planning Task Coupled with Eye-Tracker and Electroencephalogram Systems

The planning process, characterized by the capability to formulate an organized plan to reach a goal, is essential for human goal-directed behavior. Since planning is compromised in several neuropsychiatric disorders, the implementation of proper clinical and experimental tests to examine planning is critical. Due to the nature of the deployment of planning, in which several cognitive domains participate, the assessment of planning and the design of behavioral paradigms coupled with neuroimaging methods are current challenges in cognitive neuroscience. A planning task was evaluated in combination with an electroencephalogram (EEG) system and eye movement recordings in 27 healthy adult participants. Planning can be separated into two stages: a mental planning stage in which a sequence of steps is internally represented and an execution stage in which motor action is used to achieve a previously planned goal. Our protocol included a planning task and a control task. The planning task involved solving 36 maze trials, each representing a zoo map. The task had four periods: i) planning, where the subjects were instructed to plan a path to visit the locations of four animals according to a set of rules; ii) maintenance, where the subjects had to retain the planned path in their working memory; iii) execution, where the subjects used eye movements to trace the previously planned path as indicated by the eye-tracker system; and iv) response, where the subjects reported the order of the visited animals. The control task had a similar structure, but the cognitive planning component was removed by modifying the task goal. The spatial and temporal patterns of the EEG revealed that planning induces a gradual and lasting rise in frontal-midline theta activity (FM&#952;) over time. The source of this activity was identified within the prefrontal cortex by source analyses. Our results suggested that the experimental paradigm combining EEG and eye-tracker systems was optimal for evaluating cognitive planning.

The study of cognitive planning combining EEG and eye-tracking systems provides a multimodal approach to investigate the neural mechanisms that mediate cognitive control and goal-directed behavior in humans. Here, we describe a protocol for investigating the role of brain oscillations and eye movements in planning performance.

The planning process, characterized by the capability to formulate an organized plan to reach a goal, is essential for human goal-directed behavior. Since ...

Revolutionizing fNIRS Studies&#58; A Novel Approach to Neural Coupling

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

Despite the growing body of functional near-infrared spectroscopy (fNIRS) hyperscanning studies, the assessment of coupling between two neural signals using wavelet transform coherence (WTC) seems to ignore the directionality of the interaction. The field is currently lacking a framework that allows researchers to determine whether a high coherence value obtained using a WTC function reflects in-phase synchronization (i.e., neural activation is seen in both members of the dyad at the same time), lagged synchronization (i.e., neural activation is seen in one member of the dyad prior to the other member), or anti-phase synchronization (i.e., neural activation is increased in one member of the dyad and decreased in the other). To address this need, a complementary and more sensitive approach for analyzing the phase coherence of two neural signals is proposed in this work. The toolbox allows investigators to estimate the coupling directionality by classifying the phase angle values obtained using traditional WTC into in-phase synchronization, lagged synchronization, and anti-phase synchronization. The toolbox also allows researchers to assess how the dynamics of interactions develop and change throughout the task. Using this novel WTC approach and the toolbox will advance our understanding of complex social interactions through their uses in fNIRS hyperscanning studies.

Author Spotlight: Unlocking New Insights in fNIRS Studies -  A Novel Framework for Inter-Brain Synchrony Analysis 

Wavelet transform coherence (WTC) is a common methodology for assessing the coupling between signals that is used in functional near-infrared spectroscopy (fNIRS) hyperscanning studies. A toolbox for assessing the directionality of the signal interaction is presented in this work.

Despite the growing body of functional near-infrared spectroscopy (fNIRS) hyperscanning studies, the assessment of coupling between two neural signals ...

Unveiling Brain-Heart Interplay with EEG and Cardio Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human physiology and improve health outcomes. However, the multimodal analysis of these signals is challenging due to the lack of guidelines, standardized signal processing and statistical tools, graphical user interfaces (GUIs), and automation for processing large datasets or increasing reproducibility. A further void exists in standardized EEG and heart-rate variability (HRV) feature extraction methods, undermining clinical diagnostics or the robustness of machine learning (ML) models. In response to these limitations, we introduce the BrainBeats toolbox. Implemented as an open-source EEGLAB plugin, BrainBeats integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO) for assessing time-locked brain-heart interplay at the millisecond accuracy; 2) EEG and HRV feature extraction for examining associations/differences between various brain and heart metrics or for building robust feature-based ML models; 3) Automated extraction of heart artifacts from EEG signals to remove any potential cardiovascular contamination while conducting EEG analysis. We provide a step-by-step tutorial for applying these three methods to an open-source dataset containing simultaneous 64-channel EEG, ECG, and PPG signals. Users can easily fine-tune parameters to tailor their unique research needs using the graphical user interface (GUI) or the command line. BrainBeats should make brain-heart interplay research more accessible and reproducible.

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

The BrainBeats toolbox is an open-source EEGLAB plugin designed to jointly analyze EEG and cardiovascular (ECG/PPG) signals. It includes heartbeat-evoked potentials (HEP) assessment, feature-based analysis, and heart artifact extraction from EEG signals. The protocol will aid in studying brain-heart interplay through two lenses (HEP and features), enhancing reproducibility and accessibility.

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human ...