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. 2017 Mar;4(1):13-25.
doi: 10.1007/s40708-016-0055-1. Epub 2016 Jul 18.

Workload Regulation by Sudarshan Kriya: An EEG and ECG Perspective

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Free PMC article

Workload Regulation by Sudarshan Kriya: An EEG and ECG Perspective

Sushil Chandra et al. Brain Inform. .
Free PMC article

Abstract

Sudarshan Kriya Yoga (SKY) is a type of rhythmic breathing activity, trivially a form of Pranayama that stimulates physical, mental, emotional, and social well-being. The objective of the present work is to verify the effect of meditation in optimizing task efficiency and regulating stress. It builds on to quantitatively answer if SKY will increase workload tolerance for divided attention tasks in the people sank in it. EEG and ECG recordings were taken from a total of twenty-five subjects who had volunteered for the experiment. Subjects were randomly assigned to two groups of 'control' and 'experimental.' Their objective scores were collected from the experiment based on NASA's multi-attribute task battery II and was utilized for workload assessment. Both the groups had no prior experience of SKY. The experimental group was provided with an intervention of SKY for a duration of 30 min everyday. Pre- and post-meditation data were acquired from both groups over a period of 30 and 90 days. It was observed that subjective score of workload (WL) was significantly reduced in the experimental group and performance of the subject increased in terms of task performance. Another astute observation included a considerable increase and decrease in the alpha and beta energies and root mean square of the EEG signal for the experimental group and control group, respectively. In addition to this sympathovagal balance index also decreased in experimental group which indicated reduction in stress. SKY had an effect on stress regulation which in turn enhanced their WL tolerance capacity for a particular multitask activity.

Keywords: Physiological signals; Stress; Sudarshan Kriya Yoga (SKY); Workload.

Figures

Fig. 1
Fig. 1
Mean subjective score for low workload level (LWL) and high workload level (HWL) with standard error as error bar (±1SE) a and b for control and experimental groups, respectively. C1 Pre-control group at 0-day time period, C2 post-control group after 30 days period, E1 pre-experimental group prior exposure to SKY, E2 post-experimental group after 30 days period, E3 post-experimental group after 90 days period. 0-day time period was defined as the time period prior to experiment beginning
Fig. 2
Fig. 2
Mean root mean square deviation (RMSD) for low workload level (LWL) and high workload level (HWL) with standard error as error bar a and b for control and experimental groups, respectively. It is deviation from center point in pixel units. C1 Pre-control group at 0-day time period, C2 post-control group after 30 days period, E1 pre-experimental group prior exposure to SKY, E2 post-experimental group after 30 days period
Fig. 3
Fig. 3
Mean response time (RT) for low workload level (LWL) and high workload level (HWL) with standard error as error bar a and b for control and experimental groups, respectively. C1 Pre-control group at 0-day time period, C2 post-control group after 30 days period, E1 pre-experimental group prior exposure to SKY, E2 post-experimental group after 30 days period
Fig. 4
Fig. 4
Mean value of sympathovagal balance index (SVI) for baseline (BL) condition in Experiment with standard error as error bar. Data were compared among groups for the time periods of 0 and 30 days. C1 _BL Pre-control group at 0-day time period, C2 _BL post-control group after 30 days period, E1 _BL pre-experimental group prior exposure to SKY, E2_BL post-experimental group after 30 days period
Fig. 5
Fig. 5
Mean value of sympathovagal balance index (SVI) for low workload (LWL) and high workload (HWL) condition in experiment with standard error as error bar. Data were compared within the control group for the time periods of 0 and 30 days. C1 Pre-control group at 0-day time period, C2 post-control group after 30 days period
Fig. 6
Fig. 6
Mean value of sympathovagal balance index (SVI) for low workload (LWL) and high workload (HWL) condition in experiment with standard error as error bar. Data were compared within the control group for the time periods of 0 and 30 days. E1 pre-experimental group prior exposure to SKY, E2 post-experimental group after 30 days period
Fig. 7
Fig. 7
Alpha energy (4–8 Hz) average plotted for baseline (BL) condition between groups as to demonstrate energy distribution between pre- and post-conditions to highlight effect of SKY with standard error as error bar. Units were displayed as a magnitude of 1*100000. BL_AF3, BL_F7, BL_F3, BL_FC5, BL_AF4, BL_F8, BL_F4, BL_FC6 represented mean energy variance for baseline condition for EEG channels AF3, F7, F3, FC5, AF4, F8, F4, FC6, respectively
Fig. 8
Fig. 8
Beta energy (13–30 Hz) average plotted for baseline (BL) condition between groups as to demonstrate energy distribution between pre- and post-conditions to highlight effect of SKY with standard error as error bar. Units were displayed as a magnitude of 1*100000. BL_AF3, BL_F7, BL_F3, BL_FC5, BL_AF4, BL_F8, BL_F4, BL_FC6 represented mean energy variance for baseline condition for EEG channels AF3, F7, F3, FC5, AF4, F8, F4, FC6, respectively
Fig. 9
Fig. 9
Gamma energy (30–45 Hz) average plotted for baseline (BL) condition between groups as to demonstrate energy distribution between pre- and post-conditions to highlight effect of SKY with standard error as error bar. Units were displayed as a magnitude of 1*100000 in vertical axis. BL_F3, BL_FC5, BL_F4, BL_FC6 represented mean energy variance for baseline condition for EEG channels F3, FC5, F4, FC6, respectively
Fig. 10
Fig. 10
Alpha RMS average plotted for baseline (BL) condition between groups as to demonstrate energy distribution between pre- and post-conditions to highlight effect of SKY with standard error as error bar. BL_AF3, BL_F7, BL_F3, BL_FC5, BL_AF4, BL_F8, BL_F4, BL FC6 represented mean RMS values for baseline condition for EEG channels AF3, F7, F3, FC5, AF4, F8, F4, FC6, respectively
Fig. 11
Fig. 11
Engagement index (EI) values had been displayed in comparison for both groups
Fig. 12
Fig. 12
Bar plots showing percentage classification accuracy for LWL and HWL conditions between groups using support vector machine (SVM) algorithm
Fig. 13
Fig. 13
Bar plots showing percentage classification accuracy for LWL and HWL conditions between groups using neural network(NN) classifiers

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