The Science Behind

Measuring the effect of writing journal and prayer on emotions through brain waves using EEG

Mental health is a direct outcome of human emotions. Emotions that are sad, angry, frustrated and reduce these emotions are to shape offering journal or a prayer and putting a journal and prayer or a prayer 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 features of emotion can be extracted through the analysis of brain wave signals. Because of the unresponsiveness and brainwave signal to human growth, therefore of brainwave emotion is caused.
Emotion classification is one of the most important topics in the field of brain science. One of the main problems in the analysis of human emotion is how to accurately classify the types of emotion. However, the uniqueness and particularity of Brain Potter, resulting in the inability to accurately identify the person’s emotions in brain wave analysis. Although the type of human emotion classification varies according to standard, this research is based on the twentieth century psychologist Paul Ekman’s divided emotion into six basic emotion categories and emotions were classified according to the brain wave feature extraction method.
Paul Ekman 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 behaviour of a common creature or person in the face of danger in life. Fear can change in the heart, elevated blood pressure, tight breasts and other physiological phenomena, and even the person's emotions.

Anger

Emotional agitation, being violated, disrespected, or wronged, can lead to physiological responses for its conflict response — irritability, more nervous energy, irritability and other emotional expressions.

Sadness

Usually the psychological function of failure, the mood is lower awareness, sad standard emotions, self-pity, depression, despair, and mental state responses — help, low self-esteem, and satisfaction, pride, and closeness.

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 measurement used by Pematonians et al. is different from other studies. Most of the electrical poles used in the study of brain waves are 64 channels. This electrode points used in this programme are 3 channels, namely a bipolar channel of F3 and F4, according to the 10-20 system. In the other scheme, the number of electric poles in brain wave instrument for other studies was less. In this computational complexity of the low sample, is used to measure the levels of human emotions by kinds of human emotions, namely happiness, surprise, sadness, anger, fear, disgust and disgust. The programme uses four classification methods, namely QDA, Manhattan distance (MD) and the Vector Machine (SVM), In this scheme, the four methods were compared and their extraction analysis, KNN systems and vector machine (SVM). For this scheme, the experimental results show that SVM has 83.33% average classification results through four classifiers, and the best results obtained is with the four classifiers.

Feature Extraction & Classification Pipeline

EEG Signal

↓
Statistical-Based Features
Wavelet-Based Features
Proposed DOC-Based Features
↓
Feature Vectors
↓
QDA
KNN
MD
SVMs
↓
Emotion

Design of Experiments

The Subjects

The subjects are ages 15 to 60, drawn from various social strata and backgrounds. Some were active journal writers and some had never written a journal before.

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 an unpleasant incident of their lives. After that, the machine would capture the brain waves. Subsequently, they were asked to write down the incident in a diary. Again the brain waves were recorded. Then the subjects were asked to pray for the brain waves upon which their prayer state was recorded. This was done for a variety of ten days. The headband would be used to measure the brainwaves of the subject throughout multiple sessions.

Analyses

Data for alpha and gamma, beta and theta wave patterns 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 average the emotions classified by the Pematonians were as follows:

Emotional classification when remembering the unpleasant incident

Anger
Sadness

Emotional classification after writing the journal and praying on the unpleasant incident

Joy
Pleasure

Discussion

From the EEG signals of alpha, beta, theta and gamma waves we can interpret the following: As soon as the subject remembers or dwells on the unpleasant incident of their lives, the more intense emotional activity registers — anger and sadness dominate. However, after the subject writes the incident in a journal and prays, the emotions shift toward happiness and positivity, and the emotions classified lean towards joy and pleasure.

MindBalance — The Trigona Display

MindBalance is a system and method for estimating topic consciousness states using non-invasive EEG signal processing and TriGuna modelling. 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 electroencephalography (EEG) measurements. More specifically, the invention describes an approach that integrates the classical Indian framework of Triguna — Satva, Rajas, and Tamas — with computational EEG analysis to provide real-time mind and consciousness insight.
The system employs headband-worn dry-electrode EEG sensors positioned at the frontal and temporal regions to capture ambient neural oscillations. These oscillations are processed using spectral decomposition techniques, mapping the resulting frequency bands (delta, theta, alpha, beta, gamma) to a proprietary mental state model that is trained on paired subjective self-reports and objective EEG data.
Signals are pre-processed for filtering, denoising and artifact removal, followed by extraction of power spectral density and coherence features across electrode pairs. For each of the resulting feature sets, a proprietary trained model computes a normalised Guna score — Satva, Rajas and Tamas — that reflects the balance of clarity, activity and inertia within the subject’s current mental state.
The invention further discloses methods for real-time visual feedback delivered via a mobile or wearable display, guiding the individual toward practices — including journaling and prayer — that are associated with a shift toward a more Satvic (balanced, clear) state, and away from Rajasic (agitated) or Tamasic (dull, inert) states, as measured by the accompanying EEG signal.
Note: this section summarises technical background material referenced in the original research documentation. Full methodological detail, including electrode placement specification, filtering parameters and classifier training data, is available on request.
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