Analysis of Meditation vs. Sensory Engaged Brain States Using Shannon Entropy and Pearson’s First Skewness Coefficient Extracted from EEG Data
Abstract
:1. Introduction
2. Materials
- Meditation (MED): Participants were asked to engage in the meditation of their choice for 7 min with their eyes closed. Alternatively, if people were unfamiliar with meditating, they were asked to relax with their eyes closed. After the proper preparation, each participant pressed the space bar key on the keyboard to signal the beginning of the meditation period, which continued until the preprogramed end signal informed them of the end of the session.
- Video (VDO): In this final modality, the participants were presented with a video containing a sequence of ambiguous images with the song ‘Imagine’ by John Lennon, playing throughout the video. The ambiguous images aimed to evoke mental responses similar to the well-known Necker cube. The ambiguous images used in this task were designed by Oleg Shupliak, and they can be found in [39]. The duration of this experiment was 1 min and 50 s for each participant. There was no task the participants were asked to perform besides watching the video and listening to the song. After reading the instructions, the participants pressed the space bar key on the keyboard to signal the beginning of the video-watching period, until the video finished playing.
3. Methods
3.1. Preprocessing
- 8 ms for anti-alias filter, which essentially accounts for the required time of the amplifier to do the conversion;
- 14 ms for the screen refresh rate, adding to a total of a 22 ms shift to match the recorded event markers with the actual event time.
3.2. Entropy and Information Theoretical Indices
- 3.
- Entropy measure (H), as introduced by Shannon [46], providing us with the degree of randomness in an EEG signal.
- 4.
- PSk as a measure of information derived from the frequency distribution structure represented by the degree of asymmetry, which was derived by [47] and discussed by [48]. The version of the PSk (1st order skewness coefficient) described and formulated in [49] in terms of the mean, standard deviation and mode (dominant frequency or frequency band) is used for this study.
3.3. Computation of the H and PSk Indices
3.4. Analysis of Multi-Variate EEG Data
4. Results
4.1. Qualitative Analysis of the EEG Data
4.2. Detailed Quantitative Analysis of Brain Dynamics
5. Discussion
5.1. Discrimination across Modalities and Brain Regions for a Representative Participant (P10)
5.2. Discrimination Results in Populations of Meditators and Non-Meditator Participants
6. Conclusions
- We conjecture that more relaxed states showing alpha dominance, accompanied with lower values of H and PSk, are achieved by: (1) masterful meditators, (2) people who practice relaxation techniques and (3) people who are naturally more relaxed (less stressed), who might be able to mitigate environmental signals and demands when existing in such relaxed emotional and coherent mental states. This we can derive from the data associated with the modality VDO when contrasted with the one of MED for both groups: Meditator and Non-Meditator. It is relevant to note that the Meditator group showed lower values for H than the Non-Meditator group, which indicates that meditative states are more likely different than relaxed states when participants have their eyes closed.
- We conjecture that meditators may carry these relaxed states into other activities, possibly due to the lasting psychophysiological effects derived from meditative practices [28,57,58,59] translating and continuing into other areas of life. This will require further investigation and studies with a larger sample size.
- When comparing the overall mean values for each modality, MED displays the smallest values of H and PSk, and VDO displays the largest for both groups. The overall distribution and values, however, are significantly different. These findings indicate that there is a distinct difference between meditators and non-meditators in brain dynamics, and that H and PSk taken together are a useful element to analyze and differentiate various cognitive states. Statistical hypothesis tests indicate that the H index is useful to discriminate between Meditator and Non-Meditator participants during MED over both the PF and OCC areas (p = 0.03), while the PSk index is useful to discriminate Meditators from Non-Meditators based on the PF areas for both MED and VDO (p = 0.05).
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Group/Modality | MED | VDO |
---|---|---|
Meditator | 0.87 ± 0.033 | 0.99 ± 0.007 |
Non-Meditator | 0.92 ± 0.043 | 0.98 ± 0.016 |
Group/Modality | MED | VDO |
---|---|---|
Meditator | 0.72 ± 0.049 | 0.98 ± 0.016 |
Non-Meditator | 0.76 ± 0.124 | 0.99 ± 0.021 |
Test | p-Value | H0: μ1 = μ2 |
---|---|---|
Meditators vs. Non-Meditators for MED (H) | 0.033 | Reject |
Meditators vs. Non-Meditators for MED (PSk) | 0.255 | Accept |
Meditators vs. Non-Meditators for VDO (H) | 0.5 | Accept |
Meditators vs. Non-Meditators for VDO (PSk) | 0.955 | Accept |
Modality and Brain Region | Mean Value Correlation Coefficient (r) | Lower Bound | Upper Bound |
---|---|---|---|
MED-PF | 0.8649 | 0.832 | 0.908 |
MED-OCC | 0.7355 | 0.669 | 0.811 |
VDO-PF | 0.6011 | 0.5 | 0.7 |
VDO-OCC | 0.5792 | 0.475 | 0.685 |
Test | p-Value | H0: μ1 = μ2 |
---|---|---|
Meditators vs. Non-Meditators for MED (PF) | 0.0218 | Reject |
Meditators vs. Non-Meditators for MED (OCC) | 0.0290 | Reject |
Meditators vs. Non-Meditators for VDO (PF) | 0.0841 | Accept |
Meditators vs. Non-Meditators for VDO (OCC) | 0.1718 | Accept |
Test | p-Value | H0: μ1 = μ2 |
---|---|---|
Meditators vs. Non-Meditators for MED (PF) | 0.0437 | Reject |
Meditators vs. Non-Meditators for MED (OCC) | 0.0936 | Accept |
Meditators vs. Non-Meditators for VDO (PF) | 0.0112 | Reject |
Meditators vs. Non-Meditators for VDO (OCC) | 0.8738 | Accept |
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Davis, J.J.J.; Kozma, R.; Schübeler, F. Analysis of Meditation vs. Sensory Engaged Brain States Using Shannon Entropy and Pearson’s First Skewness Coefficient Extracted from EEG Data. Sensors 2023, 23, 1293. https://doi.org/10.3390/s23031293
Davis JJJ, Kozma R, Schübeler F. Analysis of Meditation vs. Sensory Engaged Brain States Using Shannon Entropy and Pearson’s First Skewness Coefficient Extracted from EEG Data. Sensors. 2023; 23(3):1293. https://doi.org/10.3390/s23031293
Chicago/Turabian StyleDavis, Joshua J. J., Robert Kozma, and Florian Schübeler. 2023. "Analysis of Meditation vs. Sensory Engaged Brain States Using Shannon Entropy and Pearson’s First Skewness Coefficient Extracted from EEG Data" Sensors 23, no. 3: 1293. https://doi.org/10.3390/s23031293