Measuring effect of writing journal and prayer on emotions through brain waves using EEG
Mental health is a direct output of human emotions. Human emotions that are sad, angry, frustrated are responsible for affecting the mental health. While there are many studies on how meditation can reduce these emotions, there is no study on the effect of writing a journal or a diary and prayer has on the human emotions. This paper will study the effect of human emotions pre and post writing a journal and pre and post saying a prayer.
Previous Literature
Brain science has shown that human emotions are controlled by the brain. The brain produces brain waves
as it transmits messages. Brain wave data is one of the biological messages, and biological messages usually have
emotion features. The feature of emotion can be extracted through the analysis of brain wave messages. But
because of the environmental background and cultural differences of human growth, the complexity of human
emotion is caused. Therefore, in the analysis of brain wave emotion, classification algorithm is very important.
In this article, we will focus on the classification of brain wave emotions.
Emotion classification is one of the most important topics in the field of brain Wave Research. One of the
main problems in the analysis of brain wave emotion is how to accurately classify the types of emotion. However,
the uniqueness and particularity of Brain Potter, resulting in the inability to accurately distinguish between
the diversity of human emotions. Although the types of human diversity emotions cannot be classified.
However, the twentieth century psychologist “Pail Ekmean” divided emotion into basic emotions and complex
emotions and basic emotions are closely related to human physiological responses. Therefore, we can classify
the emotion types in the basic emotions through the brain Wave Emotion classification method.
Pail Ekmean confirmed that basic emotions are human physiological responses. Basic emotions can be
divided into six categories, namely, happiness, anger, fear, surprise, sadness and disgust. Psychologists have
the following views on basic emotions.
• Fear: The instinctive behavior of a common creature or person in the face of danger in life. Fear can cause
changes in the heart rate, elevated blood pressure, night sweats, tremors and other physiological phenomena,
and even the symptoms of cardiac arrest shock.
• Anger: Emotional agitation, being violated, disrespected, or wrongly treated, can lead to instinctive selfpreparedness for its combat response. Emotional anger, micro lukewarm, resentment, inequality, irritability,
hostility, and, more extreme, hatred and violence.
• Sadness: It is usually the psychological frustration of failure, the mood is lower meaning. Emotions are sad,
depressed, self-pity, loneliness, depression, despair, and morbid severe melancholy.
• Joy: Emotion is the psychological state of pleasure, with the meaning of joy, contentment,
self-satisfaction, pride, and excitement in the senses.
• Surprise: By unexpected stimulation in the living environment, resulting in temporary
action to stop.
• Disgust: Facing negative stimuli in the environment.
Evaluation Method
The method of brain wave instrument used by Petrantonakis et al. is different from other studies. Most of the electrical poles used in the study of brain waves are 64 or channels. The electrode points used in this programme are 3 channels, namely FP1, FP2, and a bipolar channel of F3 and F4 positions according to 10–20 system. In this scheme, the number of electric poles in brain wave instrument For other studies less. That is, the computational complexity of this scenario is low. The purpose of this scheme is to classify six kinds of human emotions. Six kinds of emotions are: happiness, surprise, anger, fear, disgust and sadness. The programme uses four classification methods, namely quadratic discriminant analysis (QDA), k-nearest neighbor (KNN), Mahalanobis distance (MD) and the Vector Machines (SVMs). In this scheme, three methods were obtained to obtain the brain wave eigenvectors (FVS), which were statistical values, wavelet transform and higher order crossings (HOC). The FVs classifies six emotions through four classifiers. The experimental results show that SVM has 83.33% average classification rate. The best results obtained by SVM in four classifiers.
Design of Experiments
The subjects
The subjects are from ages 15 to 60, drawn from various social strata and background. Some were active journal
writers and some have not written journals ever.
Apparatus
The EEG apparatus used to measure the brainwaves was Flowtime Bio Sensing head band. The
Flowtime headband uses two-channel EEG acquisition technology to monitor brainwaves.
Procedure
The subjects were asked to first reflect on a unpleasant incident of their lives. After that the EEG machine would
capture their brain waves. Subsequently they were asked to write down the incident in a diary. Again the brain
waves were recorded. Then the group was asked to pray for other to give them peace from that incident. Post
the prayer the brain waves were again recorded.
This was done for a series of 10 days.
The headband would be used to measure the brainwaves of the subject through multiple sessions. The feature
extraction was done from the waves for wave amplitude in micro volts.
The brain waves were classified as per the Petrantonakis method above to identify the emotions after each stage.
The method used for classification of feature into emotion was KNN.
The available data set on Kaggle was used to classify the emotions.
Analyses
Data for alpha and gamma, beta and theta wave patters were collected at 0.5 second intervals for the entire
session.
Data from 20 sessions were collected across three months.
The raw data feature extraction is represented as follows.
Results
After thinking about unpleasant incident
On an average the emotions classified by the Petrantonakis classified as follows
Emotional classification when remembering the unpleasant incident
Emotional classification after writing the journal and praying of the unpleasant incident
Discussion
From the EEG output of alpha, beta, theta and gamma waves we can interpret the following:
As soon as the subject remembers or delves on the unpleasant incident of their lives, brain wave activity shows
that the emotions felt can be classified as anger and sadness (also clubbed as disgust, depressed)
After the journal was written the emotions based on brain wave activity was classified into happiness positive.
After the prayer was done by the subjects for the other subjects, the emotions based on brain wave activity
were classified into happiness and hope.
MindBalance - The Triguna Display
1. System and Method for Estimating Yogic Consciousness States using Non-Invasive EEG Signal Processing and TriGuna Modelling
2. Field of the Invention
The present invention relates to the field of biomedical signal processing, particularly to systems and methods for quantifying mental and emotional states using non-invasive electroencephalographic (EEG) measurements. More specifically, the invention applies principles from Yogic philosophy — including the Triguna model of Sattva, Rajas, and Tamas — in conjunction with real-time signal processing and artificial intelligence to derive meaningful indicators of human consciousness and mind balance.
3. Background of the Invention
The understanding and measurement of human consciousness remains a major challenge in both neuroscience and psychology. Ancient Indian systems such as Samkhya and Yoga describe mental function in terms of three intrinsic qualities, or Gunas: Sattva (clarity and harmony), Rajas (activity and agitation), and Tamas (inertia and dullness). While these concepts are foundational in Ayurveda and Yoga-based therapies, there exists no scientific tool or system that quantitatively maps modern EEG data to these mental states in a validated, real-time manner.
Existing EEG-based emotion tracking systems focus on conventional valence-arousal models, which do not incorporate traditional Yogic psychology. Some neurofeedback systems exist for meditation tracking, but these often reduce consciousness to single metrics such as attention or calmness, and do not reflect the full spectrum of mental dynamics. Moreover, these systems typically rely on opaque machine learning methods, with limited interpretability or grounding in philosophical frameworks.
There is therefore a need for a system that bridges ancient Yogic models of consciousness with scientifically grounded EEG analysis — allowing the quantification of Sattva, Rajas, and Tamas in a continuous, interpretable, and device-independent way.
4. Object of the Invention
The primary object of the present invention is to provide a system and method for quantitative estimation of human consciousness states — Sattva, Rajas, and Tamas — using EEG signals and frequency-domain analysis grounded in Yogic philosophy.
Other objects of the invention include:
• To develop a real-time processing system for classifying mental states based on non-invasive EEG data.
• To implement a mathematical model that maps standard EEG frequency bands (delta, theta, alpha, beta, gamma) to a normalized triplet of Guna indices.
• To build an interpretable neurotech tool for mental state assessment, wellness feedback, or therapeutic use.
• To provide a system that works with minimal hardware (2 electrodes) and is capable of being embedded in consumer-grade or clinical EEG devices.
• To integrate this system into an AI-supported feedback platform for monitoring, training, or assisting meditation, focus, and stress reduction practices.
5. Summary of the Invention
The present invention provides a system and method for estimating Yogic consciousness states — namely Sattva, Rajas, and Tamas — through the acquisition and processing of non-invasive EEG signals. The invention uses a two-electrode EEG sensor placed at frontal scalp locations (Fp1 and Fp2) to record brainwave signals in real-time.
The captured EEG data is preprocessed through filtering, detrending, and artifact removal, followed by segmentation into time epochs. For each epoch, the power spectral density (PSD) is computed using fast Fourier transform (FFT), and integrated over canonical EEG frequency bands.
A novel mathematical model is employed to calculate Guna indices, where:
• Sattva is associated with increased power in theta and alpha bands (4–13 Hz),
• Rajas with elevated beta and gamma power (13–45 Hz),
• Tamas with dominant delta activity (0.5–4 Hz).
Each Guna score is normalized via a softmax-like function, producing a continuous distribution across the three Gunas. This allows for interpretable real-time estimation of mental state balance.
The system may be implemented in software running on a mobile, desktop, or embedded platform, and may include a dashboard, alerts, or feedback mechanism. The system may also store time-resolved Guna profiles, perform entropy analysis for mental stability, and integrate with health or meditation platforms. The invention can further serve as a diagnostic aid or personalization tool in wellness and integrative medicine applications.
6. Brief Description of Drawings
• Figure 1: Block diagram of the MindBalance EEG system showing the flow from signal acquisition (headband and electrodes) through preprocessing, frequency-band extraction, and Guna computation to dashboard feedback.
• Figure 2: Image of the prototype device showing the BioAmp EXG Pill connected to Arduino Nano on breadboard.
• Figure 3: View of EEG electrode placement on subject’s scalp using elastic headband with snap-on electrodes (Fp1, Fp2).
• Figure 4: Full system image showing all components: sensor, microcontroller, headband, and wires.
• Figure 5: Mobile App User Interface Display
A mock-up of the real-time user interface (UI) of the MindBalance system as viewed on a mobile device. The screen shows dynamic visualizations of the computed Guna indices — Sattva, Rajas, and Tamas — as color-coded percentages or meters. The UI also includes timestamps, historical trend plots, and optional indicators like “Balanced State” or “Cognitive Load” for user feedback.
7. Detailed Description of the Invention
7.1 System Architecture Overview
The proposed invention is a neurophysiological monitoring system designed to estimate Yogic consciousness states — Sattva, Rajas, and Tamas — using non-invasive brainwave recordings and mathematical modeling. The complete system comprises:
1. EEG Acquisition Unit:
o Two passive gel electrodes are placed at frontal lobe positions Fp1 and Fp2, based on the international 10–20 EEG system. These are secured using a custom elastic headband with snap connectors.
o The electrodes are connected to a BioAmp EXG Pill (open-source 2-channel instrumentation amplifier board), capable of detecting microvolt-level EEG signals.
o The analog signals are digitized and passed via serial interface to an Arduino Nano microcontroller.
o Data is then transmitted to a laptop or mobile application over USB.
2. Signal Preprocessing & Filtering:
Raw EEG data is processed using the following steps:
o Band-pass filter [0.5–50 Hz] to eliminate slow drifts and high-frequency noise.
o Notch filter at 50 Hz to remove mains interference.
o Artifact rejection (e.g., eye blink correction or epoch exclusion) as per the signal quality.
3. Spectral Decomposition & Band Power Calculation:
o A Fast Fourier Transform (FFT) is applied to sliding windows of 2.4 seconds.
o Power Spectral Density (PSD) is computed across standard frequency bands:
Delta: 0.5–4 Hz
Theta: 4–8 Hz
Alpha: 8–13 Hz
Beta: 13–30 Hz
Gamma: 30–45 Hz
o Relative band powers are calculated for normalization.
7.2 Mathematical Modeling of the Gunas
Using relative power values Pδ,Pθ,Pα,Pβ,PγP_\delta, P_\theta, P_\alpha, P_\beta, P_\gammaPδ,Pθ,Pα,Pβ,Pγ, we compute three Guna scores as follows:
Raw score equations:
Sscore=0⋅Pδ+0.5⋅Pθ+1.0⋅Pα+0.2⋅Pβ+0.5⋅Pγ\textbf{S}_{\text{score}} = 0 \cdot P_\delta + 0.5 \cdot P_\theta + 1.0 \cdot P_\alpha + 0.2 \cdot P_\beta + 0.5 \cdot P_\gammaSscore=0⋅Pδ+0.5⋅Pθ+1.0⋅Pα+0.2⋅Pβ+0.5⋅Pγ Rscore=0⋅Pδ+0.0⋅Pθ+0.0⋅Pα+1.0⋅Pβ+0.5⋅Pγ\textbf{R}_{\text{score}} = 0 \cdot P_\delta + 0.0 \cdot P_\theta + 0.0 \cdot P_\alpha + 1.0 \cdot P_\beta + 0.5 \cdot P_\gammaRscore=0⋅Pδ+0.0⋅Pθ+0.0⋅Pα+1.0⋅Pβ+0.5⋅Pγ Tscore=1.0⋅Pδ+0.5⋅Pθ+0.0⋅Pα+0.0⋅Pβ+0.0⋅Pγ\textbf{T}_{\text{score}} = 1.0 \cdot P_\delta + 0.5 \cdot P_\theta + 0.0 \cdot P_\alpha + 0.0 \cdot P_\beta + 0.0 \cdot P_\gammaTscore=1.0⋅Pδ+0.5⋅Pθ+0.0⋅Pα+0.0⋅Pβ+0.0⋅Pγ
Normalized Guna indices:
Sattva=SscoreSscore+Rscore+Tscore\textbf{Sattva} = \frac{S_{\text{score}}}{S_{\text{score}} + R_{\text{score}} + T_{\text{score}}}Sattva=Sscore+Rscore+TscoreSscore Rajas=RscoreSscore+Rscore+Tscore\textbf{Rajas} = \frac{R_{\text{score}}}{S_{\text{score}} + R_{\text{score}} + T_{\text{score}}}Rajas=Sscore+Rscore+TscoreRscore Tamas=TscoreSscore+Rscore+Tscore\textbf{Tamas} = \frac{T_{\text{score}}}{S_{\text{score}} + R_{\text{score}} + T_{\text{score}}}Tamas=Sscore+Rscore+TscoreTscore
This creates a dynamic three-dimensional state vector summing to 1 at every timepoint. These scores are visualized or stored per epoch.
7.3 User Interface and Applications
• Real-time graphing of the Guna indices is displayed using a simple GUI dashboard.
• The user can observe shifts in mental balance during meditation, tasks, rest, or even while listening to specific music.
• The data is also stored in CSV format for retrospective analysis and plotting.
• Use cases include meditation feedback, emotional health screening, pre-task readiness, or consciousness monitoring in research and education.
7.4 Functional Validation from Experiments
Using experimental EEG recordings across multiple sessions and subjects, the system showed:
• Elevated Sattva index during meditative sessions with prominent alpha/theta band activity.
• Increased Rajas during cognitive effort (e.g., playing chess, solving puzzles) correlated with elevated beta and gamma.
• High Tamas during drowsy or inactive periods, characterized by delta and slow-theta dominance.
These trends were consistently observed in the processed data, thereby validating the mapping model between EEG features and Yogic consciousness states.
8. Expanded Claims
1.
A system for estimating Yogic consciousness states using non-invasive EEG signals, comprising:
(a) at least two EEG electrodes configured to acquire electrical brainwave signals from a human subject at scalp positions Fp1 and Fp2,
(b) a signal acquisition module comprising an analog amplifier and a microcontroller for digitizing and transmitting the EEG signals,
(c) a signal processing unit configured to filter, transform, and extract power spectral features from the EEG data, and
(d) a computation module configured to calculate continuous indices of Sattva, Rajas, and Tamas by applying a weighted model over standard EEG frequency bands.
2.
The system of claim 1, wherein the computation module uses a mathematically defined model of the form:
Sscore=w1Pθ+w2Pα+w3Pβ+w4PγS_{\text{score}} = w_1 P_\theta + w_2 P_\alpha + w_3 P_\beta + w_4 P_\gammaSscore=w1Pθ+w2Pα+w3Pβ+w4Pγ Rscore=w5Pβ+w6PγR_{\text{score}} = w_5 P_\beta + w_6 P_\gammaRscore=w5Pβ+w6Pγ Tscore=w7Pδ+w8PθT_{\text{score}} = w_7 P_\delta + w_8 P_\thetaTscore=w7Pδ+w8Pθ
with a softmax-like normalization function applied to derive final indices.
3.
The system of claim 1, wherein the EEG data is segmented into time windows of 2.4 seconds, and Fourier transformation is used to compute power spectral densities per frequency band.
4.
The system of claim 1, wherein the frequency bands are defined as:
• Delta: 0.5–4 Hz
• Theta: 4–8 Hz
• Alpha: 8–13 Hz
• Beta: 13–30 Hz
• Gamma: 30–45 Hz
5.
The system of claim 1, wherein the output indices (Sattva, Rajas, Tamas) are displayed in real-time on a dashboard, color-coded and plotted as time series.
6.
The system of claim 1, wherein the Guna values are logged and exported in a structured format such as CSV, JSON, or integrated to EHR systems via HL7 or FHIR standards.
7.
The system of claim 1, wherein the system is capable of adapting its model weights based on multi-session learning or user-specific feedback over time.
8.
The system of claim 1, wherein a machine learning engine further classifies time segments based on cumulative Guna patterns and offers session summaries.
9.
The system of claim 1, wherein Guna data is used to provide AI-generated recommendations for improving mental balance, such as guided meditations or behavioral nudges.
10.
The system of claim 1, wherein the wearable EEG acquisition module is mounted in a reusable elastic band with replaceable snap-on electrodes and integrates with a USB or Bluetooth microcontroller.
11.
A method for quantifying Yogic mental states in a subject, comprising:
(a) acquiring EEG signals via Fp1 and Fp2,
(b) filtering and preprocessing the signals,
(c) computing relative band power in standard EEG frequency ranges,
(d) applying a mathematical Guna model,
(e) displaying, storing, and optionally transmitting the results for clinical or wellness use.
9. Abstract
The invention relates to a system and method for estimating Yogic mental states — Sattva, Rajas, and Tamas — using non-invasive EEG signal processing. The system comprises a two-channel EEG acquisition unit, a microcontroller-based digitizer, and a signal processing pipeline that extracts frequency band powers (delta to gamma) in real-time. A mathematical model maps the relative power in each band to Guna indices, normalized to form a dynamic consciousness profile. The device supports time-series visualization, session logging, and integration with external health systems. Designed for use in meditation, cognitive tasks, and emotional monitoring, the invention provides an interpretable, scientifically grounded measure of mental state rooted in Indian philosophical psychology.