DESC: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 Pd,P?,Pa,Pß,P?P_\delta, P_\theta, P_\alpha, P_\beta, P_\gammaPd,P?,Pa,Pß,P?, we compute three Guna scores as follows:
Raw score equations:
Sscore=0·Pd+0.5·P?+1.0·Pa+0.2·Pß+0.5·P?\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·Pd+0.5·P?+1.0·Pa+0.2·Pß+0.5·P? Rscore=0·Pd+0.0·P?+0.0·Pa+1.0·Pß+0.5·P?\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·Pd+0.0·P?+0.0·Pa+1.0·Pß+0.5·P? Tscore=1.0·Pd+0.5·P?+0.0·Pa+0.0·Pß+0.0·P?\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·Pd+0.5·P?+0.0·Pa+0.0·Pß+0.0·P?
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.
,CLAIMS: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=w1P?+w2Pa+w3Pß+w4P?S_{\text{score}} = w_1 P_\theta + w_2 P_\alpha + w_3 P_\beta + w_4 P_\gammaSscore=w1P?+w2Pa+w3Pß+w4P? Rscore=w5Pß+w6P?R_{\text{score}} = w_5 P_\beta + w_6 P_\gammaRscore=w5Pß+w6P? Tscore=w7Pd+w8P?T_{\text{score}} = w_7 P_\delta + w_8 P_\thetaTscore=w7Pd+w8P?
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.