ウェーブレットエントロピーを使用して心理学の練習が不規則な大脳と心臓活動の間の調整をどのように増加させるかを実証する

The overall goal of this EEG experiment is to use the wavelet entropy index to analyze high-density EEG and ECG data. We show that the irregularity of cerebral and cardiac activities became more coordinated during mindfulness-based stress reduction practice. This method can help answer the questions in neuroscience and psychology about how to measures and compare the chaotic activities of mind and heart during the practice of mindfulness.

The uniqueness of this method is the creation of a common index using wavelet entropy analysis to measure brain and heart chaotic activities so that their relationship can be effectively analyzed and compared. A variety of religious and spiritual traditions believe that the body and mind are somehow coordinated. However, scientists have struggled to prove it because they cannot find a common index to compare the two.

This method can also provide insight into the working mechanism of how mindfulness and other mental trainings can modulate the activities of the brain and the heart. We first had the idea to use wavelet entropy to explore the correlation between the brain and heart when we found that wavelet entropy is sensitive to the actions on the different mental conditions. In addition to Junling and Jicong, Hang Kin, from my laboratory, will assist with the demonstration of these procedures.

He will also be our subject during the demonstrations. Begin by escorting the participant to a quiet electroencephalography, or EEG, room. To conduct the recording, gather a 128-channel EEG system consisting of an EEG cap, amplifier, headbox, and desktop computer.

Next, use alcohol swabs to clean the face area and mastoid of the participant. Measure the participant's head circumference with measuring tape, 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 at mid-distance between the two ears, with a soft marker pen. Next, set the electrode positions according to the 10-5 electrode system. 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.

Fill the electrode holders with gel using a blunt-point syringe. Then, place the ECG electrodes at both the left and right infraclavicular fossae. Keep the impedance under 20 kiloohm for each electrode.

Reduce the impedance by adjusting the electrode placement to increase contact with the scalp, and add more gel if necessary. Next, record EEG data at the beginning of the mindfulness-based stress reduction, or MBSR, course. Have the participant perform a brief body scan to relax the whole body by asking them to pay attention to his or her breath while breathing in and breathing out.

Finally, have each participant perform 10 minutes of MBSR mindful breathing and 10 minutes of normal rest during EEG data collection to generate a pre-MBSR training dataset with two conditions. Then repeat this EEG procedure after two months to generate a post-MBSR training dataset with two conditions. Begin by opening the EEG software and loading the dataset, and then select Tools and Change sampling rate to resample the data.

Then, select Tools, Filter the data, and Basic FIR filter to use the Finite Impulse Response filter for band-pass filtering with a 0.5 to 100-hertz passband. To reduce noise due to the mains alternating current, select Tools, Filter the data, and Short non-linear IIR filter to use the short non-linear Infinite Impulse Response filter for notch filtering with a 47 to 53-hertz stopband. Next, select Plot and Channel data within the EEG software to visually scroll through and inspect the EEG signal.

Then, left-click and drag the mouse over bad segments to highlight and delete EEG segments that contain obvious muscle noise and any other strange events. Following any bad segment deletions, determine if there are any bad channels. Reconstruct each bad channel using the spherical interpolation method by selecting Tools and Interpolate channel.

Next, select Tools and Run ICA to perform an Independent Component Analysis on the data. Then, visually identify good components, and discard components of eye movement and blinking, muscle movements, and components of other possible noise by selecting Tools, Reject data using ICA, followed by Reject components by map, then Tools, and Remove components. Finally, select Tools and Re-reference to re-reference the data to the average of all channels before further analysis.

Then, use the formula seen here to calculate the wavelet coefficients, to define relative energy, and calculate wavelet entropy. In the spectrum analysis of EEG data, compared to normal rest, there were enhanced alpha and beta and reduced delta waves during MBSR mindful breathing. Further, source analysis shows that the major brain regions affected by the MBSR mindfulness training were in the left occipital gyrus, right precuneus, middle temporal gyrus, and left fusiform.

Lastly, the entropies of the brain and heart were significantly correlated during MBSR mindful breathing but not during normal rest. Once mastered, the procedure can be done in one week if it is performed properly. When you are attempting this procedure, it's worth noting that all the proper sensing steps are essential to achieve the expected results.

Following this procedure, we can find out whether other mental trainings and practices, such as chanting, prayer, yoga, tai chi, and similar contemplative exercises, can improve the entrainment of the body and the mind. After watching this video, you should have a good understanding of how to use wavelet entropy to measure and explore the relationship between the electrical activities of brain and heart during mindfulness meditations.