CleverPoint View Data v2.15 User Guide

Introduction

CleverPoint View is a desktop research tool for analyzing multichannel physiological recordings (EEG-like channels plus cardio/ECG) in the context of experimental sessions. The program reads data from CSV files, splits the recording into sections (episodes), applies standard cleaning and filtering procedures, and displays the results on several thematic screens.

The interface is focused on quickly reviewing signal dynamics over time, spectral characteristics, inter-channel relationships, heart rate variability metrics, and simple “emotional state” indices. These indices are not diagnostic; they are simply computed using the formal formulas described below.

This guide describes the functionality of all application interfaces and provides detailed explanations of the signal transformations for psychophysiology researchers.

0.1. What Are Physiological Signals?

Physiological signals are electrical potentials generated as a result of the activity of living tissues. This application analyzes three main types of signals:

0.1.1. EEG (Electroencephalogram)

Physiological basis: EEG records the electrical activity of the cerebral cortex. This activity arises from synchronized changes in the membrane potentials of large groups of neurons (pyramidal cells).

How it works:

• Neurons generate electrical potentials when transmitting information.
• When thousands of neurons become active synchronously, their potentials sum together.
• Electrodes on the surface of the head record these summed potentials.
• The signal amplitude is typically 10–100 microvolts (µV).

What EEG shows:

• General brain state (wakefulness, sleep, coma)
• Cognitive processes (attention, memory, thinking)
• Emotional states
• Pathological changes (epilepsy, tumors)

EEG frequency ranges (and their physiological meaning):

• Delta (0.5–4 Hz): Deep sleep, pathological conditions, infancy
• Theta (4–8 Hz): Light sleep, meditation, creative thinking, learning
• Alpha (8–13 Hz): Relaxed wakefulness with eyes closed, calm state
• Beta (13–30 Hz): Active thinking, concentration, problem solving
• Gamma (30–100 Hz): High-level cognitive processing, binding information from different brain areas

0.1.2. ECG (Electrocardiogram)

Physiological basis: ECG records the electrical activity of the heart. Each heartbeat (systole) is initiated by an electrical impulse that propagates through the heart’s conduction system.

How it works:

• The sinoatrial node (the pacemaker) generates an electrical impulse.
• The impulse spreads through the atria to the atrioventricular node and then to the ventricles.
• Electrodes on the body record these electrical changes.
• The R-peak in the ECG corresponds to ventricular depolarization (contraction).

What ECG shows:

• Heart rate (pulse)
• Rhythm regularity
• The state of the cardiac conduction system
• Heart rate variability (HRV), an indicator of autonomic regulation

Heart rate variability (HRV):

• A healthy heart does not beat like a metronome—intervals between beats constantly change.
• This variability is controlled by the autonomic nervous system.
• High variability = good adaptability and health.
• Low variability = stress, fatigue, and possible health problems.

0.1.3. EMG (Electromyogram)

Physiological basis: EMG records the electrical activity of muscles. When a muscle contracts, muscle fibers generate electrical potentials.

How it works:

• Motor neurons send signals to muscle fibers.
• Muscle fibers depolarize and contract.
• Electrodes on the skin record this activity.
• In this application, EMG is recorded from facial muscles (Zygoma — cheek muscles).

What EMG shows:

• Muscle tension
• Facial reactions (smile, grimace)
• Stress and emotional reactions (facial muscle tension)

0.2. Autonomic Nervous System (ANS)

The autonomic nervous system controls involuntary body functions, including heart rhythm, breathing, and digestion.

Two branches of the ANS:

Sympathetic nervous system (“fight or flight”):

• Activated during stress, danger, or physical exertion
• Increases heart rate
• Reduces heart rate variability
• Prepares the body for action

Parasympathetic nervous system (“rest and digest”):

• Activated during rest and relaxation
• Decreases heart rate
• Increases heart rate variability
• Promotes recovery and rest

ANS balance:

• A healthy body maintains a dynamic balance between the sympathetic and parasympathetic systems.
• HRV analysis makes it possible to assess this balance.
• Impaired balance may indicate stress, fatigue, or disease.

0.3. Emotions and the Brain

Valence–arousal model:

• Valence: Positive or negative evaluation of an emotion (pleasant/unpleasant)
• Arousal: Level of activation (calm/excited)

Hemispheric asymmetry:

• Left hemisphere (AF3): Associated with positive emotions, approach behavior, and activity
• Right hemisphere (AF4): Associated with negative emotions, avoidance, and caution

Frequency patterns of emotion:

• Alpha activity: Relaxation, calmness
• Beta activity: Active thinking, stress, anxiety
• Beta/alpha ratio: Level of activation/arousal

0.4. Electrodes and Their Placement in the CleverPoint Setup

10–20 system (standard EEG electrode placement)

• Variant 1: Reference electrode at Fz. Unipolar connection.
• Variant 2: For scientific use. Common split reference on the right and left ears. Unipolar connection.

Electrode positions:

• F7, F8: Lateral frontal areas
• AF3: Left anterior frontal area, between Fp1 and F3 (left hemisphere)
• AF4: Right anterior frontal area, between Fp2 and F4 (right hemisphere)
• L Zygoma, R Zygoma: Cheek muscles (facial muscles, for EMG)

What each electrode records:

• Each electrode records the activity of the cortical area beneath it.
• Different brain areas are responsible for different functions.
• Comparing activity between electrodes shows interactions between brain regions.

1.1. Basic Transformations

All application interfaces use a common signal-processing system. Each element of this system can be enabled or disabled separately and has its own settings. The transformations are applied in the following order:

1. Selection of data type (Type): RAW (raw data) or Filtered (pre-filtered in the 3–100 Hz range with a 50 Hz notch filter)
2. Trim: Removal of marked artifact segments
3. CAR (Common Average Reference): Re-referencing to a common reference electrode
4. Denoise (blink artifact suppression): Wavelet processing to remove blinks
5. Normalize: Amplitude normalization
6. IMF (Empirical Mode Decomposition): Decomposition into modes
7. Filter: Band-pass filtering
8. Epoch: Averaging over epochs
9. Power/Amplitude: Conversion to power or amplitude

1.2. Marking and Trim

1.2.1. Automatic Artifact Marking

Purpose: Automatic detection of signal segments with amplitude outliers.

How it works:

• The signal is divided into 3-second windows.
• The maximum and minimum are taken for each window.
• Thresholds are calculated as doubled medians of these extrema:
  quantPl = 2*median(max), quantMn = 2*median(min)
• Values outside the thresholds are clipped to the threshold values, reducing the impact of rare outliers.
• Based on these thresholds, the system automatically marks artifact segments (in the Ignore field).
• Marked segments are shown in yellow on the graph.

Physiological explanation:

The marking procedure is based on the idea of a “typical” amplitude range within 3-second windows: physiologically normal activity usually has a relatively stable amplitude, whereas sharp spikes are more often associated with artifacts. Clipping such spikes reduces their contribution to subsequent calculations while preserving the temporal structure of the signal.

1.2.2. Trim: Removing Marked Segments

Purpose: Removal of marked (yellow) artifact segments from the signal.

How it works:

• Removal is performed when the Trim checkbox is enabled.
• All segments marked as artifacts in the Ignore field are removed from the signal.
• Time stamps for sections and comments are recalculated to account for the removed segments.
• Signal time is recalculated as:
  Time = (1:sum(Ignore==0))/Fs

Use case: Used to exclude motion artifacts, blinks, electromagnetic interference, and other distortions.

Physiological explanation:

Artifacts are signals unrelated to the physiological activity of interest. For example:

• Motion artifacts: When the participant moves their head, the electrodes shift relative to the skin, causing large voltage jumps.
• Blink artifacts: The eyelid muscles generate electrical potentials during blinking, which are recorded by frontal electrodes.
• Electromagnetic interference: Electrical devices (50/60 Hz) create background noise.
• Muscle artifacts: Tension in the neck or facial muscles creates high-frequency oscillations.

Artifact removal is necessary to obtain a “clean” signal that reflects only brain or cardiac activity.

1.3. CAR (Common Average Reference)

Purpose: Removal of common artifacts present in all channels.

Mathematical formula:

avgElectrodes = (F8 + AF4 + AF3 + F7) / 4
for each channel i: signal_i = signal_i - avgElectrodes

How it works:

• The average value of the first four EEG electrodes (F8, AF4, AF3, F7) is calculated.
• This average value is subtracted from each channel.
• Components common to all channels are removed (for example, line noise and eye movements).

Use case: A standard EEG re-referencing method used to reduce the influence of common artifacts.

Physiological explanation:

EEG electrodes record the potential difference between an active electrode and a reference electrode. The problem is that the reference electrode itself (in the typical CleverPoint setup, usually at the center of the forehead or on the earlobe) may contain its own activity or artifacts.

CAR solves this problem:

• Instead of a single reference electrode, the average of all electrodes is used.
• This creates a “virtual” reference that reflects overall activity.
• Components common to all electrodes (line noise, eye movements, general artifacts) are automatically subtracted.
• Only the local activity of each electrode remains.

Analogy: Imagine measuring mountain heights relative to sea level. If sea level itself fluctuates, all measurements become distorted. CAR is like using the average level of all points for more accurate measurements.

1.4. Denoise (Blink Artifact Suppression)

Purpose: Removal of blink artifacts from the signal using wavelet processing.

Method: Wavelet denoising using the Daubechies 4 (db4) wavelet.

Parameters:

• Decomposition level depends on the sampling frequency:
  Fs = 1000 Hz: N = 7 levelsFs = 500 Hz: N = 6 levelsOtherwise: N = 5 levels
• Threshold processing with parameter alpha = 3
• Level-dependent thresholding

How it works:

• Wavelet decomposition of the signal into N levels
• Determination of thresholds for each level based on coefficient statistics
• Thresholding of the coefficients (removing small coefficients corresponding to noise)
• Inverse wavelet transform
• Noise is calculated as the difference:
  noise = original_signal - denoised_signal
• Result:
  signal = original_signal - noise

Use case: Suppression of blink artifacts that distort EEG, especially in frontal channels.

Physiological explanation:

Blinks create short-term, large-amplitude artifacts due to eyelid muscle activity and eye movement. These artifacts have a characteristic shape and temporal structure, which makes it possible to isolate and attenuate them using wavelet transformation while preserving useful neural activity.

Why this matters: Blink artifacts mask weak but important EEG components (for example, the alpha rhythm in the 8–13 Hz range) and can create false changes in the spectrum.

1.5. Normalize

Purpose: Bring the signal amplitude to a standard level.

Formula:
normalized_signal = 5 * signal / mean(abs(signal))

How it works:

• The mean absolute value of the signal is calculated.
• The signal is scaled so that the mean absolute value becomes 5.

Use case: Standardizing amplitudes to compare signals across participants or sessions.

Physiological explanation of normalization:

The amplitude of the EEG signal varies greatly between people and even between sessions for the same person. This depends on:

• Skull and skin thickness
• Quality of electrode contact
• Individual characteristics of the brain
• Level of activation

Why normalization is needed:

• It makes it possible to compare signals between different people.
• It removes the influence of technical factors (contact quality).
• It focuses attention on patterns rather than absolute values.
• It simplifies visualization (all signals are displayed on the same scale).

Important to understand:

• Normalization does not change the shape of the signal, only its scale.
• Relative changes are preserved.
• Absolute values are lost (a normalized signal cannot be directly compared with a non-normalized one).

1.6. IMF (Empirical Mode Decomposition)

Purpose: Decompose the signal into intrinsic mode functions.

Method: EMD (Empirical Mode Decomposition) with a maximum of 5 modes.

How it works:

• The signal is decomposed into intrinsic modes using the EMD algorithm.

• The drop-down list contains 6 values:

• IMF: no decomposition is applied; the signal remains unchanged

• 1: mode 1
• 2: mode 2
• 3: mode 3
• 4: mode 4
• 5: mode 5
• If a specific mode is selected, only that mode is used:
  signal = imf(:, selected_mode)

Use cases:

• Isolating specific frequency components
• Removing baseline drift
• Analyzing nonlinear and non-stationary processes

Physiological explanation of EMD:

Empirical Mode Decomposition is a method for decomposing a signal into “intrinsic modes” without using predefined basis functions (unlike the Fourier transform).

What modes are:

• Modes are oscillatory signal components that have physical meaning.
• Each mode has its own frequency and amplitude.
• Modes adapt to the data (they are not fixed frequencies as in Fourier analysis).

Physiological meaning:

• Mode 1 (high-frequency): the fastest components, possible noise
• Modes 2–3: main rhythms (alpha, beta)
• Mode 4: slower components, signal details
• Mode 5 (low-frequency): slow changes, baseline drift, and possibly artifacts

Why use a specific mode:

• Drift removal: Mode 5 often contains baseline drift (due to sweating or electrode movement).
• Rhythm isolation: A specific mode may contain the rhythm of interest (for example, alpha).
• Artifact removal: Some modes may contain artifacts that can be excluded.

Advantages of EMD:

• Adaptivity: modes fit the signal
• Nonlinearity: nonlinear processes can be analyzed
• Non-stationarity: works with signals whose properties change over time

1.7. Filtering

Purpose: Isolate the signal in a specific frequency band.

Method: FIR filter (Finite Impulse Response) using the Parks–McClellan algorithm (firpm).

Filter parameters:

• Transition band: ±0.5 Hz from the passband boundaries
• Stopband attenuation: 80 dB
• Filter order: calculated automatically

Filter types:

• Type 0: No filtering
• Type 1: Band-pass filter (single band)
• Type 2: Ratio of two bands (band1 / band2)

Available ranges (Type 1):

• Theta (4–8 Hz)
• Theta1 (4–6 Hz)
• Theta2 (6–8 Hz)
• Alpha (8–13 Hz)
• Alpha1 (8–10 Hz)
• Alpha2 (10–13 Hz)
• Beta (13–30 Hz)
• Beta1 (13–20 Hz)
• Beta2 (20–30 Hz)
• Gamma (30–100 Hz)

Band ratios (Type 2):

• Alpha/Theta (8–13 Hz / 4–8 Hz)
• Alpha/Beta (8–13 Hz / 13–30 Hz)

Use case: Isolation of standard EEG bands, their sub-bands (theta1, theta2, alpha1, alpha2, beta1, beta2), and computation of band ratios.

Physiological explanation of frequency bands:

Delta (0.5–4 Hz):

• Physiology: the slowest rhythms; associated with deep sleep (stages 3–4), coma, and pathological conditions
• Neural mechanisms: synchronization of large cortical regions, reduced metabolism
• When it appears: in adults, only during sleep; in infants, also during wakefulness (normal)
• Clinical significance: excessive delta during wakefulness may indicate pathology

Theta (4–8 Hz):

• Physiology: associated with light sleep, meditation, creative thinking, and learning
• Neural mechanisms: synchronization of the hippocampus and cortex; important for memory consolidation
• When it appears: with eyes closed, in relaxation, during REM sleep
• Clinical significance: elevated theta may indicate drowsiness or pathology

Theta sub-bands:

• Theta1 (4–6 Hz): slower theta, more often associated with drowsiness and transitional states
• Theta2 (6–8 Hz): faster theta, more often associated with attention and cognitive processing

Alpha (8–13 Hz):

• Physiology: the “resting rhythm”; dominant during relaxed wakefulness with eyes closed
• Neural mechanisms: synchronization of occipital and parietal cortex; associated with the absence of visual stimulation
• When it appears: with eyes closed, in a calm state; disappears when the eyes open or during mental effort
• Clinical significance: absence of alpha may indicate pathology; alpha asymmetry between hemispheres may indicate problems

Alpha sub-bands:

• Alpha1 (8–10 Hz): slower alpha, more often associated with relaxation and calmness
• Alpha2 (10–13 Hz): faster alpha, more often reflects readiness for information processing

Beta (13–30 Hz):

• Physiology: active wakefulness, attention, problem solving
• Neural mechanisms: cortical desynchronization, active information processing
• When it appears: with eyes open, during mental activity, movement
• Clinical significance: increased beta may indicate stress, anxiety, and tension

Beta sub-bands:

• Beta1 (13–20 Hz): associated with active thinking and attention
• Beta2 (20–30 Hz): higher beta, more often associated with tension and stress

Gamma (30–100 Hz):

• Physiology: high-level cognitive processing, binding information from different areas
• Neural mechanisms: rapid synchronization of distant cortical regions; “binding” features into a unified perception
• When it appears: during complex cognitive tasks, perception, and attention
• Clinical significance: gamma activity abnormalities are associated with schizophrenia and autism

Band ratios (Type 2):

• Alpha/Theta: an index of relaxation relative to drowsiness/slower processes
• Alpha/Beta: an index of the balance between relaxation and cognitive activity

Why filtering is important:

The EEG signal contains all frequencies simultaneously. Filtering makes it possible to isolate the frequency range of interest and analyze it separately. For example, alpha activity is needed to study relaxation, while beta activity is used to study concentration.

1.8. Power vs Amplitude

Purpose: Convert the signal into power or amplitude.

Modes:

• Amplitude: The original signal without change
• Power: The signal squared:
  power = signal.^2

Physiological explanation of power:

Power equals the square of amplitude. It is a measure of the “energy” of activity.

Why power is important:

• Amplitude may be positive or negative (the signal oscillates around zero).
• Power is always positive and shows the “strength” of activity.
• Power reflects overall activity better than amplitude.

Physiological meaning:

• High alpha power: strong alpha activity = strong relaxation
• High beta power: strong beta activity = strong cognitive activity
• Low power: weak activity or artifacts

Comparison of modes:

• Amplitude: Shows signal shape and the direction of change (positive/negative deflections)
• Power: Shows the intensity of activity and does not depend on sign

When to use:

• Amplitude: Signal visualization, waveform analysis, artifact detection
• Power: Quantitative activity analysis, comparison between conditions, statistics

1.9. Epoch

Purpose: Average the signal using a sliding window.

Method: Sliding average using filtfilt (bidirectional filtering).

Formula:
epoch_length_samples = epoch_length_seconds * Fsb = ones(1, epoch_length_samples) / epoch_length_samplessmoothed_signal = filtfilt(b, 1, signal)

How it works:

• An averaging window of the specified length (in seconds) is created.
• The signal is filtered using a bidirectional moving average.
• The result is a smoothed signal with reduced variability.

Physiological explanation of epochs:

An epoch is averaging over a time window. This makes it possible to see general trends hidden behind rapid fluctuations.

Why averaging is needed:

• EEG signals are very “noisy” and contain many rapid oscillations.
• The processes of interest (for example, changes in alpha activity) often evolve slowly.
• Averaging smooths fast fluctuations while preserving slow trends.

Physiological meaning:

• Fast oscillations (milliseconds): individual neural events, artifacts
• Medium oscillations (seconds): brain rhythms (alpha, beta)
• Slow trends (tens of seconds): changes in state, responses to stimuli

Example:

• Without averaging: all fast oscillations are visible and the general trend is hard to see.
• With 2-second averaging: changes in alpha activity over time become visible.
• With 8-second averaging: only very slow state changes remain visible.

2.1. Purpose

The Summary interface provides overview statistics for all recording sections, including:

• EEG frequency characteristics (theta, alpha, beta, gamma)
• Heart rate parameters (HRV)
• Emotional indicators (valence, arousal, concentration)

2.2. Controls

1. Section split by episodes or fixed time intervals
2. Section list: displays the name, start time, and duration of each section
3. Electrode selection checkboxes
4. Anthropometric data
5. Signal processing settings
6. Data recalculation button
7. Data export button

2.2.1. Signal Processing

1. Trim: Remove artifact segments
2. CAR: Enable common average reference
3. Denoise: Suppress blink artifacts
4. Relative: Use relative values (as a percentage of the total spectrum)

2.2.2. Anthropometric Data

• Age: In years
• Weight: In kilograms
• Height: In centimeters
• Gender: male/female

Important: These data are used to normalize HRV parameters against age- and sex-specific norms.

2.2.3. Sections

1. Episode based: Split by episodes (from metadata)
2. Time based: Split into fixed time intervals (60, 120, 180, 240, 300 seconds)
3. Section list: Displays the name, start time, and duration of each section

2.2.4. Electrode Selection

Checkboxes for displaying data from each EEG/EMG electrode (F8, AF4, AF3, F7, L Zygoma, R Zygoma).

2.3. Graphs

2.3.1. Frequency Bands (THETA, THETA1, THETA2, ALPHA, ALPHA1, ALPHA2, BETA, BETA1, BETA2, GAMMA)

Calculation method:

• A band-pass FIR filter is applied to isolate the frequency range.
• Power is calculated:
  power = filtered_signal.^2
• The mean power value is calculated for each section.

Formula:
filtered = filtfilt(fir_filter, 1, signal)power = filtered.^2section_power = mean(power(section_start:section_end))

Interpretation: Average signal power in the selected frequency range for each section.

Physiological explanation of power:

Power is the square of signal amplitude. It is a measure of the “energy” of activity in a given frequency range.

What power means:

• High alpha power: strong relaxation, calm state
• High beta power: active thinking, concentration, possibly stress
• High theta power: light sleep, meditation, creative state
• Low power in all bands: possibly artifacts, poor electrode contact, or a pathological condition

Comparison between sections:

• If alpha power is higher in a “Relaxation” section than in a “Stress test” section, this is normal and expected.
• If beta power is higher in a “Problem solving” section, this indicates active cognitive work.
• Changes in power reflect changes in the functional state of the brain.

2.3.2. Pulse (PULSE)

Calculation method:

• RR intervals are extracted from the ECG signal.
• The mean pulse is computed for each section:
  HR = 60 / mean(RR_intervals)

Interpretation: Average heart rate in beats per minute for each section.

Physiological explanation of pulse:

Pulse (heart rate, HR) is the number of heartbeats per minute. It is one of the simplest and most important indicators of the body’s state.

Normal values:

• At rest: 60–100 bpm (in adults)
• In athletes: 40–60 bpm (a trained heart)
• During exercise: can reach 140–180 bpm (depending on age)
• During sleep: 40–60 bpm

What affects pulse:

• Sympathetic activity: increases pulse (stress, physical exertion, emotions)
• Parasympathetic activity: decreases pulse (rest, relaxation)
• Physical exertion: increases pulse to supply muscles with oxygen
• Emotions: stress and anxiety increase it; calmness decreases it
• Age: maximum pulse declines with age
• Fitness level: trained people have a lower resting pulse

Interpretation of changes:

• Increased pulse: activation, stress, exertion, emotions
• Decreased pulse: relaxation, rest, recovery
• Sharp changes: response to a stimulus, transition between states
• Persistently high pulse: possible chronic stress or health problems

2.3.3. Stress (STRESS)

Calculation method:

• Proprietary algorithm
• The value is limited to the range [0, 100]

Physiological explanation of stress:

Stress activates the sympathetic nervous system, which leads to:

• Increased heart rate
• Reduced heart rate variability (the heart beats more regularly, like a metronome)
• Changes in the shape of the Poincaré plot (the cloud of points becomes more elongated and compact)

Interpretation:

• Stress index from 0 (minimum stress) to 100 (maximum stress)
• 0–30: low stress, good recovery
• 70–100: high stress, possible overstrain

2.3.4. RMSSD (Root Mean Square of Successive Differences)

Calculation method:
RMSSD = sqrt(mean(diff(RR).^2))

Interpretation: A heart rate variability metric that reflects parasympathetic activity. Higher values = greater variability = more relaxation.

Physiological explanation of RMSSD:

RMSSD measures short-term changes in RR intervals (beat to beat). It is one of the most sensitive indicators of parasympathetic (vagal) activity.

How the parasympathetic system works:

• The vagus nerve sends signals to the heart.
• These signals modulate heart rate depending on the phase of breathing.
• During inhalation: pulse speeds up slightly.
• During exhalation: pulse slows down slightly.
• This creates the variability measured by RMSSD.

What the values mean:

• High RMSSD (>50 ms in younger people): good parasympathetic activity, relaxation, recovery
• Low RMSSD (<20 ms): reduced parasympathetic activity, stress, fatigue, possible health problems
• Normal values depend on age (higher in younger people, lower in older people)

Why this matters:

RMSSD is a marker of the body’s ability to recover. Athletes with high RMSSD recover faster after exercise. Low RMSSD may indicate overtraining or chronic stress.

2.3.5. SDNN (Standard Deviation of NN Intervals)

Calculation method:
SDNN = std(RR)

Interpretation: Overall heart rate variability. Reflects both sympathetic and parasympathetic activity.

Physiological explanation of SDNN:

SDNN is the standard deviation of all RR intervals. It is an integral measure of overall variability that reflects the functioning of the entire autonomic nervous system.

What affects SDNN:

• Parasympathetic activity: increases variability (respiratory arrhythmia)
• Sympathetic activity: may either increase variability (with moderate activation) or decrease it (under strong stress)
• Circadian rhythms: variability changes throughout the day
• Age: variability decreases with age

Clinical significance:

• High SDNN (>100 ms in younger people): good autonomic regulation, good health
• Low SDNN (<50 ms): reduced autonomic regulation, possible problems
• SDNN <20 ms: very low variability, high risk of cardiovascular events

Comparison with RMSSD:

• RMSSD is more sensitive to parasympathetic activity (short-term changes)
• SDNN reflects total autonomic regulation (all changes)
• Both parameters are important for a complete picture of ANS function

2.3.6. Functional Group (FG)

Calculation method:

SD1 and SD2 are calculated from the Poincaré plot.

Mean pulse is calculated.

The findFunctionalGroup_Z function is used, which:

• normalizes SD1, SD2, and HR relative to age- and sex-specific norms (z-scores)
• determines the functional group (0–13) based on the combination of z-scores
• considers whether the section is a relaxation section (Calibration, Relax)

Physiological explanation of the functional group:

The functional group is a comprehensive assessment of the state of the autonomic nervous system based on normalization of HRV parameters relative to age- and sex-specific norms.

Interpretation: See the guide *Classification of Functional States*.

2.3.7. Concentration (CONCENT)

Calculation method:

The signal from electrode AF4 is filtered into three ranges:

• Alpha (10–13 Hz)
• Beta (20–30 Hz)
• Gamma (30–90 Hz)

Envelopes are calculated using a 4-second window:
envelope_alpha = filtfilt(moving_average, 1, abs(alpha_signal))envelope_beta = filtfilt(moving_average, 1, abs(beta_signal))envelope_gamma = filtfilt(moving_average, 1, abs(gamma_signal))

Concentration formula:
Concentration = 20 * (envelope_beta + envelope_gamma) / envelope_alpha

Physiological explanation of concentration:

Concentration of attention is associated with activation of the prefrontal cortex and suppression of distracting signals. This is reflected in changes in the ratio of EEG frequency bands.

Neurophysiology of concentration:

• Alpha activity (8–13 Hz): associated with relaxation and the absence of active processing
• Beta activity (13–30 Hz): associated with active thinking and information processing
• Gamma activity (30–90 Hz): associated with information binding and attentional focus

What this means:

• During concentration: beta and gamma increase (active processing), alpha decreases (less relaxation)
• During distraction: alpha may be high (relaxation), while beta and gamma are low (no active processing)

Interpretation of values:

• Low values (<10): scattered attention, relaxation, possible drowsiness
• Medium values (10–30): normal concentration
• High values (>30): high concentration, active mental work, possibly tension

Why AF4 is used:

AF4 (right hemisphere, anterior frontal area) is used because this area is important for:

• Attention control
• Suppression of distracting stimuli
• Working memory
• Executive functions

2.3.8. Arousal (AROUSAL)

Calculation method:

• Signals from AF3 and AF4 are filtered into alpha (8–13 Hz) and beta (13–30 Hz) ranges.
• Power is calculated in an 8-second window:
  power_alpha = 1 + log10(sum(alpha_signal.^2) / window_length)power_beta = 1 + log10(sum(beta_signal.^2) / window_length)

Arousal formula:
Arousal = (AF3_beta + AF4_beta) / (AF3_alpha + AF4_alpha)

• Values are clipped at the 95th percentile to remove outliers.

Physiological explanation of arousal:

Arousal is the general level of activation of the nervous system, ranging from deep sleep to severe stress.

Neurophysiology of arousal:

Low arousal: alpha rhythm dominates (8–13 Hz)

• relaxed wakefulness
• eyes closed
• meditation
• calm state

Medium arousal: mixed activity

• eyes open
• normal activity
• balance of alpha and beta

High arousal: beta rhythm dominates (13–30 Hz)

• active thinking
• problem solving
• stress
• anxiety
• physical activity

Interpretation:

• Low values (<0.5): low arousal, relaxation, possible drowsiness
• Medium values (0.5–1.5): normal arousal, wakefulness
• High values (>1.5): high arousal, stress, active mental work

Practical significance:

• Persistently high arousal may indicate chronic stress or anxiety.
• Very low arousal may indicate depression, fatigue, or drowsiness.
• Optimal arousal depends on the task (low for rest, medium to high for work).

2.3.9. Valence (VALENCE)

Calculation method:

• The same alpha and beta power values used for arousal are used.

Valence formula:
Valence = (AF4_alpha / AF4_beta) - (AF3_alpha / AF3_beta)

• Values are clipped at the 95th percentile.

Physiological explanation of valence:

Valence is the evaluation of an emotion as pleasant or unpleasant. Research shows that the left and right hemispheres process emotions differently.

Theory of hemispheric asymmetry (Davidson, 1992):

Left hemisphere (AF3):

• processing of positive emotions
• approach behavior
• activity and enthusiasm
• high alpha = low activation = relaxed but positively oriented state

Right hemisphere (AF4):

• processing of negative emotions
• withdrawal behavior
• caution and anxiety
• high alpha = low activation = relaxation, but with possible negative valence

Interpretation:

• Positive values:

• the right hemisphere is more relaxed (high alpha/beta)

• the left hemisphere is more active (low alpha/beta)
• positive emotions, pleasure, joy

• Negative values:

• the left hemisphere is more relaxed

• the right hemisphere is more active
• negative emotions, sadness, anxiety, discomfort
• Close to zero: neutral emotions, balance

Important to understand:

• Alpha activity is inversely related to cortical activation.
• High alpha/beta = low cortical activation in that hemisphere.
• Low alpha/beta = high cortical activation in that hemisphere.
• The signal is influenced by tension in the forehead muscles.

Practical significance:

• Persistently negative valence may indicate depression or anxiety.
• Positive valence during relaxation is a good sign.
• Changes in valence reflect emotional reactions to stimuli.

2.4. Data Export

The Export to CSV button saves all calculated parameters to a CSV file with delimiters, including:

• Anthropometric data
• Section time stamps
• Section durations
• EEG and ECG data validity (percent)
• All frequency parameters by electrode
• Brain activity parameters
• Heart rhythm parameters

3.1. Purpose

This interface is used for visualizing signals in the time domain, with the ability to overlay two different signal-processing pipelines.

3.2. Controls

3.2.1. General Settings

1. Trim: Remove artifact segments
2. Norm (Normalize): Normalize amplitude

3.2.2. Signal Processing 1 (upper graph, green)

3. Type: RAW / Filtered (if no value is selected for signal 2, signal 2 is not shown)
4. CAR: Common average reference
5. Denoise: Blink artifact suppression
6. IMF: EMD mode selection
7. Filter: Frequency filter selection
8. Signal 2 controls: if no value is selected in Type for signal 2 (RAW / Filtered), signal 2 is not shown

3.2.3. Signal Processing 2 (lower graph, red)

The same parameters as for signal 1.

3.2.4. Additional Parameters

1. Amplitude/Power: Display amplitude or power
2. Relative: Relative or absolute values for signal power
3. Epoch length: Epoch length for averaging (in seconds)
4. Recalculation buttons (left button returns graph scaling to 100%; right button keeps the same signal fragment)

3.2.5. Electrode Selection

Checkboxes for showing or hiding the following channels:

1. F8, AF4, AF3, F7 (EEG)
2. L Zygoma, R Zygoma (EMG)
3. Cardio (ECG)

3.2.6. EEG Scale

Drop-down list for setting a fixed Y-axis scale for EEG channels (auto, 20, 50, 100 µV).

3.3. Signal Display

3.3.1. Graph Structure

• Each channel is displayed on a separate axis.
• All axes are synchronized in time (X-axis).
• EEG channels are synchronized in amplitude (Y-axis).

3.3.2. Event Markers

• Vertical colored lines indicate events and sections.
• Different colors correspond to different sections.
• Section names are displayed above the graphs.
• Yellow dots indicate segments marked to be ignored.

3.3.3. Working with Sections

• Edit episodes: Edit sections
• Add Sector (-): Add a segment to ignore (select with the cursor)
• Remove Sector (+): Remove a segment from the ignore list
• Editing changes are not saved back into the source file, but they affect section markers in the exported data.

1. Edit episodes includes:
2. Section (episode) selection
3. Section (episode) start time for calculations
4. Section (episode) end time for calculations

3.4. Signal Transformations

All transformations are applied in the order described in Section 1. For each graph (1 and 2), an independent transformation chain can be specified, which allows different processing methods to be compared.

Example use case:

• Graph 1: Filtered + CAR + Denoise (blink artifacts) + Alpha filter (8–13 Hz) + Power + Epoch length 4 s
• Graph 2: Filtered + CAR + Denoise (blink artifacts) + Beta filter (13–30 Hz) + Power + Epoch length 4 s

This makes it possible to visually compare alpha and beta activity on one screen.

4.1. Purpose

This interface is used for analyzing signals in the frequency domain by means of spectrograms (Short-Time Fourier Transform).

4.2. Interface Structure

The interface is divided into two panels:

• Spectrogram 1 (left panel)
• Spectrogram 2 (right panel)

Each panel contains:

• A spectrogram (upper graph)
• A signal time series (lower graph)

4.3. Controls

4.3.1. Signal Processing

1. Trim: Remove artifact segments
2. Type: Filtered / RAW
3. Channel: Select the channel (F8, AF4, AF3, F7, L Zygoma, R Zygoma, RR)
4. CAR: Common average reference
5. Denoise: Blink artifact suppression
6. IMF: EMD mode selection
7. Fourier/Wavelet/Hilbert: Spectral analysis method
8. Window, s: STFT window length (in seconds)
9. Normalize: Normalize the spectrogram
10. Correlation of the channels: Correlation between the channels in the two panels

4.4. Spectral Analysis Methods

4.4.1. Fourier (STFT — Short-Time Fourier Transform)

Method: Spectrogram using windowed FFT.

Parameters:

• Window: Hamming
• Window length:
  window = Fs * window_length_seconds
• Overlap:
  nooverlap = window - 25(to ensure smoothness)

Formula:
[S, F, T, P] = spectrogram(signal, hamming(window), nooverlap, window, Fs, 'power', 'yaxis')

Display:

• X-axis: time
• Y-axis: frequency (0–50 Hz)
• Color: logarithmic power (10*log10(P))
• Color range: -15 to 25 dB

Interpretation: Shows how signal power is distributed across frequencies over time. Brighter colors = higher power.

How to read a spectrogram:

• Horizontal axis (X): recording time
• Vertical axis (Y): frequency (0–50 Hz)
• Color: power (brighter = more power)

Typical patterns:

• Horizontal bands: stable rhythms
  • a band at 10 Hz = alpha rhythm (relaxation)
  • a band at 20 Hz = beta rhythm (activity)
• Vertical bands: short events
  • blink artifacts (low frequencies, short duration)
  • muscle artifacts (high frequencies, short duration)
• Changes over time: state transitions
  • alpha to beta when opening the eyes
  • beta to alpha when closing the eyes

Physiological interpretation:

A spectrogram is a “map” of brain activity over time and frequency. It shows which frequency components are present at each moment.

• Bright band in the alpha range (8–13 Hz): relaxed state, eyes closed
• Bright band in the beta range (13–30 Hz): active thinking, concentration
• Low power across all ranges: possible artifacts or pathology
• Sharp changes: state transitions, responses to stimuli

4.4.2. Wavelet

Method: Wavelet Packet Decomposition.

Parameters:

• Decomposition level: 8
• Wavelet type: selected from the list (for example, db4, coif2, sym4)

• 1. Wavelet type: select from the drop-down list
• 2. Data recalculation buttons

Formula:
wpt = wpdec(signal, 8, wavelet_type)[P, T, F] = wpspectrum(wpt, Fs)P = flipud(P)

Interpretation: Similar to STFT, but with better time resolution at high frequencies and better frequency resolution at low frequencies.

Advantages for EEG:

• High frequencies (beta, gamma): short windows make it possible to determine the exact event time
• Low frequencies (alpha, theta): long windows provide better frequency resolution

Physiological meaning:

Wavelet transformation uses windows of variable length—short windows for high frequencies and long windows for low frequencies. This corresponds well to the way the brain processes information.

• Fast processes (for example, a response to a stimulus) require precise time resolution.
• Slow rhythms (for example, alpha) require precise frequency resolution.
• Wavelet transformation provides an optimal balance.

When to use:

• For analyzing rapid events (responses to stimuli)
• For analyzing slow rhythms with high precision
• When both time and frequency resolution are needed simultaneously

4.4.3. Hilbert (Hilbert–Huang Transform)

Method: Hilbert–Huang Transform using EMD.

Parameters:

• Frequency resolution: 1 Hz

Formula:
[P, F, T] = hht(signal, Fs, 'FrequencyResolution', 1)

Interpretation: Adaptive decomposition into intrinsic modes followed by analysis of instantaneous frequency. Suitable for nonlinear and non-stationary signals.

Physiological explanation of HHT:

The Hilbert–Huang Transform combines EMD (decomposition into modes) with instantaneous frequency analysis. This makes it possible to track how the frequency of a rhythm changes over time.

Physiological meaning:

• The alpha rhythm frequency is not constant; it may “drift” from 8 to 13 Hz.
• HHT shows these changes in instantaneous frequency.
• This is important for understanding rhythm dynamics.

Advantages:

• Adaptivity: adjusts to the signal
• Nonlinearity: can process nonlinear phenomena
• Instantaneous frequency: shows frequency changes over time

When to use:

• For analyzing non-stationary signals (whose properties change over time)
• For tracking rhythm frequency drift
• For analyzing complex nonlinear processes

4.5. Channel Correlation

When the Correlation of the channels option is enabled:

• A spectrogram is computed for the current channel in panel 1.
• A spectrogram is computed for the selected channel in panel 2.
• The cross-spectrum is computed:
  P_cross = (P1 .* conj(P2)).^0.7

Interpretation: Shows the synchrony of frequency components between two channels.

Physiological meaning:

Correlation between channels shows how synchronously two brain areas operate at specific frequencies. If two areas are synchronized at a certain frequency, they are “communicating” with each other. This may indicate a functional connection between the regions.

Different frequencies may have different degrees of synchrony:

• Synchronization in alpha: the regions are relaxed together
• Synchronization in beta: the regions are actively working together (for example, during problem solving)
• Synchronization in gamma: rapid communication between regions; information binding

Practical significance:

• Synchronization abnormalities may indicate connectivity problems.
• Very high synchronization (>0.6) may indicate not only true connectivity, but also a common source contaminating the signal (noise or artifacts).

4.6. Time Series

The lower graph shows the original signal in the time domain with the same transformations (Trim, CAR, Denoise, IMF) as the spectrogram, but without frequency filtering. Denoise is used to suppress blink artifacts.

5.1. Purpose

This interface is used to analyze signal power in frequency bands across recording sections. It has two modes:

• Power by sections: a table of power values by section
• Between-channel interactions: a graph of interactions between channels

Common controls:

1. Trim: Remove artifact segments
2. CAR: Common average reference
3. Denoise: Blink artifact suppression
4. Log: Logarithmic scale
5. IMF: EMD mode selection
6. Recalculation button

5.2. “Power by sections” Mode

5.2.1. Controls

1. Trim: Remove artifact segments
2. CAR: Common average reference
3. Denoise: Blink artifact suppression
4. Filter: Frequency filter selection
5. Function: Aggregation function (sum, mean, median, std)
6. Relative: Relative values (percentage of the total spectrum)
7. Epoch length: Epoch length for averaging
8. Recalculation button
9. Mode selection: Power by sections / Between-channel interactions

5.2.2. Results Table

The table contains:

1. Section/filter: Section name or filter name
2. F8, AF4, AF3, F7, L Zygoma: Power for each electrode
3. Epoch length: Epoch length
4. Power: Checkbox for including or excluding the section

5.2.3. Power Calculation Method

• Transformations are applied: Trim, CAR, Denoise (blink artifacts)
• The frequency filter is applied (if selected)
• The signal is converted to power:
  power = signal.^2
• Epoch averaging is applied:
  power_smoothed = filtfilt(moving_average, 1, power)
• An aggregate statistic is calculated for each section:
  sum: sum(power_section) / Fs (power integral)mean: mean(power_section) (mean power)median: median(power_section) (median power)std: std(power_section) (standard deviation)

5.2.4. Relative Values

When Relative is enabled:

• Total power (without filtering) is calculated for each channel.
• Power in the selected band is expressed as a percentage:
  relative_power = 100 * filtered_power / total_power

Interpretation: Percentage of power in the selected frequency band relative to the total signal power.

Physiological explanation of relative power:

Absolute power depends on many factors (contact quality, individual characteristics). Relative power shows what share of total brain activity belongs to a given frequency band.

• Relative power is more stable and makes it easier to compare people.
• It shows the “balance” between different rhythms.

Examples:

• Alpha 50%: half of the brain’s activity is alpha rhythm (strong relaxation)
• Beta 30%: one third of the activity is beta rhythm (active thinking)
• Theta 10%: one tenth of the activity is theta rhythm (light sleep or meditation)

Interpretation of changes:

• Increased relative alpha: transition toward relaxation
• Increased relative beta: transition toward active work
• A change in balance reflects a change in functional state

5.3. “Between-channel interactions” Mode

5.3.1. Purpose

Visualization of the relationship between two channels within a selected frequency band.

5.3.2. Controls

1. Trim: Remove artifact segments
2. CAR: Common average reference
3. Denoise: Blink artifact suppression
4. Filter: Frequency filter selection
5. Amplitude/Power: Amplitude or power
6. Epoch length: Epoch length
7. Mode selection: Power by sections / Between-channel interactions
8. Recalculation button
9. Electrode Y: Channel for the Y-axis
10. Electrode X: Channel for the X-axis

5.3.3. Calculation Method

• Transformations and filtering are applied to each channel.
• The signal is converted to amplitude or power.
• Epoch averaging is applied.
• For each section, a scatter plot is built:
  plot(X_channel, Y_channel)

5.3.4. Interpretation

• Linear relationship: high correlation between channels
• Clusters: different sections may form separate clusters
• Cloud shape: reflects the type of interaction (synchronous, asynchronous, delayed)

5.4. Frequency-Band Graphs

On the left side of the interface, power graphs for the four EEG channels (F7, AF3, AF4, F8) are displayed for all sections.

Method:

• FFT is computed for each section.
• A power spectrum is constructed:
  P = abs(FFT(signal)) / length(signal)
• Smoothing is applied.
• The spectrum from 0 to 100 Hz is displayed.

Colors: each section has its own color for visual distinction.

6.1. Purpose

This interface is used to analyze coherence between pairs of EEG electrodes. Coherence shows the degree of synchrony of frequency components between two channels.

6.2. Controls

1. Trim: Remove artifact segments
2. Type: RAW / Filtered
3. CAR: Common average reference
4. Denoise: Blink artifact suppression
5. IMF: EMD mode selection
6. df: Frequency resolution (0.0, 0.2, 0.4, 0.6, 0.8, 1.0 Hz)
7. Norm: Normalize coherence
8. Recalculation button

6.3. Coherence Calculation Method

Formula: the mscohere (Magnitude Squared Coherence) function is used:
[Cxy, F] = mscohere(signal1, signal2, [], [], frequencies, Fs)

Parameters:

• signal1, signal2: signals from two electrodes after processing
• frequencies: frequency vector for calculation (from df to 50 Hz with step df)
• Fs: sampling frequency

Mathematical definition:
Cxy(f) = |Pxy(f)|^2 / (Pxx(f) * Pyy(f))

where:

• Pxy(f): cross-spectral power density
• Pxx(f), Pyy(f): auto-spectral power densities

Value range:

• 0 ≤ Cxy ≤ 1
• 0: complete absence of coherence
• 1: full coherence

6.4. Display

The interface contains a 16-plot grid (4 × 4), where each cell corresponds to a pair of electrodes:

• F8–AF4, F8–AF3, F8–F7
• AF4–AF3, AF4–F7
• AF3–F7

For each pair, coherence curves are shown for all sections, and each section has its own color.

1. Controls:
2. Power: Checkbox for including or excluding sections

6.5. Interpretation

Physiological explanation of coherence:

Coherence measures how synchronously two brain areas operate at a specific frequency.

High coherence (close to 1):

• Synchronous activity between channels
• Functional connection between brain regions
• Possible synchronization of neural ensembles
• Possible common noise source (interference, muscle artifacts)

Low coherence (close to 0):

• Independent channel activity
• No functional connection

Frequency specificity:

• Coherence can be high in some frequency ranges and low in others.
• This reflects frequency-specific functional connections.

Frequency-specific relationships:

• Alpha coherence (8–13 Hz): associated with relaxation and resting synchrony
• Beta coherence (13–30 Hz): associated with active information processing and cognitive tasks
• Gamma coherence (30–100 Hz): associated with information binding and perception

Practical significance:

• Changes in coherence reflect changes in functional brain networks.
• High coherence in appropriate ranges is a sign of efficient brain functioning.

6.6. Use in Research

• Analysis of functional brain connectivity
• Study of synchronization between regions
• Diagnosis of connectivity abnormalities
• Monitoring of connectivity changes over time

7.1. Purpose

This interface is used to analyze emotional state based on EEG signals. It uses the Valence–Arousal model.

7.2. Interface Structure

The interface is divided into two panels:

• Local EMOTION: time series of valence and arousal
• Integral EMOTION: integral emotion map (scatter plot)

7.3. Controls

7.3.1. Signal Processing

1. Trim: Remove artifact segments
2. CAR: Common reference processing
3. Denoise: Blink artifact suppression
4. Electrode 1: First electrode (default: AF3)
5. Filter 1: Frequency filter for the first electrode (default: Alpha 8–13 Hz)
6. Electrode 2: Second electrode (default: AF4)
7. Filter 2: Frequency filter for the second electrode (default: Beta 13–30 Hz)
8. Window, s: Window length for power calculation (default: 8 seconds)
9. Recalculation button

7.4. Emotion Calculation Method

7.4.1. Power Computation

For each electrode and frequency band:

• Filtering in the selected band is applied.
• Power is computed in a sliding window:
  window_samples = window_seconds * Fsfor i = 1:(length(signal) - window_samples) power(i) = 1 + log10(sum(signal(i:i+window_samples-1).^2) / window_samples)end

Power formula:
power = 1 + log10(mean(signal_window.^2))

7.4.2. Arousal

Formula:
Arousal = (Electrode1_beta + Electrode2_beta) / (Electrode1_alpha + Electrode2_alpha)

Theoretical rationale: The beta/alpha ratio reflects the activation level of the cerebral cortex. Beta activity is associated with active thinking and stress, while alpha activity is associated with relaxation.

Physiological explanation of arousal:

Arousal is the general activation level of the nervous system, ranging from deep sleep to severe stress.

Neurophysiology of arousal:

Low arousal: alpha rhythm dominates (8–13 Hz)

• relaxed wakefulness
• eyes closed
• meditation
• calm state

Medium arousal: mixed activity

• eyes open
• normal activity
• alpha/beta balance

High arousal: beta rhythm dominates (13–30 Hz)

• active thinking
• problem solving
• stress
• anxiety
• physical activity

Interpretation:

• Low values (<0.5): low arousal, relaxation, possible drowsiness
• Medium values (0.5–1.5): normal arousal, wakefulness
• High values (>1.5): high arousal, stress, active mental work

Clinical significance:

• Persistently high arousal may indicate chronic stress or anxiety.
• Very low arousal may indicate depression, fatigue, or drowsiness.
• Optimal arousal depends on the task (low for rest, medium to high for work).

7.4.3. Valence

Formula:
Valence = (Electrode2_alpha / Electrode2_beta) - (Electrode1_alpha / Electrode1_beta)

Theoretical rationale: Alpha/beta asymmetry between the left and right hemispheres reflects emotional valence. The left hemisphere (AF3) is associated with positive emotions; the right hemisphere (AF4) with negative emotions.

Physiological explanation of valence:

Valence is the evaluation of an emotion as pleasant or unpleasant. Research shows that the left and right hemispheres process emotions differently.

Theory of hemispheric asymmetry (Davidson, 1992):

Left hemisphere (AF3):

• processing of positive emotions
• approach behavior
• activity, enthusiasm
• high alpha = low activation = relaxed state with a positive emotional orientation

Right hemisphere (AF4):

• processing of negative emotions
• withdrawal behavior
• caution, anxiety
• high alpha = low activation = relaxation, but with possible negative valence

Interpretation:

Positive values:

• the right hemisphere is more relaxed (high alpha/beta)
• the left hemisphere is more active (low alpha/beta)
• positive emotions, pleasure, joy

Negative values:

• the left hemisphere is more relaxed
• the right hemisphere is more active
• negative emotions, sadness, anxiety, discomfort

Close to zero: neutral emotions, balance

Important to understand:

• Alpha activity is inversely proportional to cortical activation.
• High alpha/beta = low cortical activation in that hemisphere.
• Low alpha/beta = high cortical activation in that hemisphere.

Practical significance:

• Positive valence during relaxation is a good sign.
• Changes in valence reflect emotional responses to stimuli.

7.4.4. Outlier Handling

Values are clipped at the 95th percentile to remove artifact outliers:
quantile_arousal = quantile(abs(arousal), 0.95)arousal(abs(arousal) > quantile_arousal) = sign(arousal) * quantile_arousal

The same is done for valence.

7.5. Display

7.5.1. Local EMOTION

Two graphs:

• Arousal: arousal time series (green line)
• Valence: valence time series (green line)

Both graphs are synchronized in time and contain section markers.

7.5.2. Integral EMOTION

Scatter plot:

• X-axis: Valence
• Y-axis: Arousal
• Points: each point represents one moment in time
• Colors: different sections have different colors
• Large points: median values for each section

Emotion quadrants:

• Upper right (high valence, high arousal): joy, enthusiasm
• Upper left (low valence, high arousal): anxiety, anger
• Lower left (low valence, low arousal): sadness, depression
• Lower right (high valence, low arousal): calmness, satisfaction

Physiological explanation of the emotion quadrants:

Upper right quadrant (High valence + High arousal):

• Emotions: joy, enthusiasm, excitement, delight
• Physiology:
  • activation of the left hemisphere (positive emotions)
  • high beta activity (active thinking)
  • increased sympathetic activity, but in a positive context
  • dopamine and endorphin release
• Behavior: activity, goal-directed behavior, positive motivation

Upper left quadrant (Low valence + High arousal):

• Emotions: anxiety, anger, fear, panic
• Physiology:
  • activation of the right hemisphere (negative emotions)
  • high beta activity (stress, tension)
  • high sympathetic activity (“fight or flight” response)
  • cortisol and adrenaline release
• Behavior: avoidance, defense, aggression

Lower left quadrant (Low valence + Low arousal):

• Emotions: sadness, depression, apathy, gloom
• Physiology:
  • activation of the right hemisphere (negative emotions)
  • low beta activity (reduced cognitive activation)
  • high alpha activity, but in a negative context
  • reduced sympathetic activity
  • possible neurotransmitter imbalances (serotonin, dopamine)
• Behavior: passivity, avoidance, reduced motivation

Lower right quadrant (High valence + Low arousal):

• Emotions: calmness, satisfaction, relaxation, peacefulness
• Physiology:
  • activation of the left hemisphere (positive emotions)
  • high alpha activity (relaxation)
  • low beta activity (absence of stress)
  • high parasympathetic activity
  • balanced autonomic nervous system
• Behavior: rest, recovery, positive relaxation

Important:

The quadrants are conditional and show not absolute emotions, but changes in emotion:

• left to right: from less positive to more positive (or less negative)
• bottom to top: from weaker to stronger

Emotion dynamics:

• Emotions are not static; points may move between quadrants.
• Transitions reflect changes in emotional state.
• A healthy person may appear in different quadrants depending on the situation.
• A problem may exist if a person remains in one quadrant continuously, especially a negative one.

7.6. Section Selection

Checkboxes to the right of the scatter plot make it possible to include or exclude sections from the display. This is useful for comparing emotional states across conditions.

8.1. Purpose

This interface is used for comprehensive analysis of heart rate variability (HRV) based on the ECG signal.

8.2. Interface Structure

The interface contains:

1. Top panel: visualization of pulses and RR intervals
2. Middle left panel: HRV parameter table
3. Middle center panel: RR tachogram and spectrum
4. Middle right panel: Poincaré plot (Local Return Map)
5. Bottom panel: segmental HRV analysis

8.3. Controls

8.3.1. General Settings

1. Sector: Select the section for analysis (All signal or a specific section)
2. Calculate HRV measures: Button for recalculating HRV metrics on the ECG segment selected in Sector
3. Save RR: Save RR intervals to CSV
4. disp cardio signal: Display the original ECG signal
5. Age, Weight, Height, Gender: Anthropometric data (used for normalization)

6. Sector: Select the section for analysis (AllRecord or a specific section)
7. Save HRV: Save HRV parameters by section to CSV
8. FFT type: FFT or Angles (angles in the Poincaré plot)

Shift RR: Shift for the Poincaré plot (1–15 intervals)

Average Window: Averaging window for segmental analysis (10–100 intervals)

8.4. Extraction of RR Intervals

8.4.1. R-Peak Detection Method

A function is used that:

• Applies preliminary filtering of the ECG signal
• Uses an adaptive threshold for peak detection
• Computes RR intervals:
  RR(i) = time(R_peak(i+1)) – time(R_peak(i))
• Filters artifacts (extrasystoles, missed beats)

8.4.2. Pulse Visualization

The upper graph shows:

• Gray points: all detected pulses
• Blue points: valid RR intervals
• Red points: extrasystoles (marked for exclusion)

8.5. HRV Parameters

8.5.1. Time-Domain Parameters

HR (Heart Rate):
HR = 60 / mean(RR) [bpm]

SDNN (Standard Deviation of NN Intervals):
SDNN = std(RR) [s]

Interpretation: overall heart rate variability.

RMSSD (Root Mean Square of Successive Differences):
RMSSD = sqrt(mean(diff(RR).^2)) [s]

Interpretation: parasympathetic activity (vagal influence).

pNN50:
pNN50 = 100 * sum(abs(diff(RR)) > 0.05) / (length(RR) - 1) [%]

Interpretation: percentage of interval differences greater than 50 ms. High values = high variability.

8.5.2. Poincaré Plot Parameters

SD1 and SD2:

• A Poincaré plot is constructed:
  plot(RR(1:end-1), RR(2:end))
• Correlation and rotation angle are calculated.
• Coordinates are rotated to align with the principal axes.
• SD1: standard deviation along the minor axis (short-term variability)
• SD2: standard deviation along the major axis (long-term variability)

Formulas:
SD1 = std(perpendicular_axis)SD2 = std(major_axis)SD1/SD2 ratio = SD1 / SD2

Interpretation:

• SD1: parasympathetic activity
• SD2: sympathetic and parasympathetic activity
• SD1/SD2: balance of the autonomic nervous system

8.5.3. Frequency-Domain Parameters

Method: FFT spectral analysis of the RR tachogram.

Procedure:

• RR intervals are interpolated onto an even grid (spline interpolation).
• FFT is computed:
  Y = fft(zscore(RR_interpolated))
• Spectral power density is calculated.

• Power is integrated over the following ranges:

• VLF (Very Low Frequency): 0–0.04 Hz

• LF (Low Frequency): 0.04–0.15 Hz
• HF (High Frequency): 0.15–0.4 Hz

Parameters:
LF_nu = LF / (LF + HF) * 100 [%]HF_nu = HF / (LF + HF) * 100 [%]LF/HF ratio = LF / HF

Interpretation:

• LF: sympathetic and parasympathetic activity (baroreflex)
• HF: parasympathetic activity (respiratory arrhythmia)
• LF/HF: balance of the sympathetic and parasympathetic systems

Physiological explanation of HRV frequency components:

VLF (Very Low Frequency, 0–0.04 Hz):

• Physiology: very slow oscillations associated with thermoregulation and hormonal cycles
• Control: mainly humoral factors; less directly related to the autonomic nervous system
• Clinical significance: less well studied; often excluded from analysis

LF (Low Frequency, 0.04–0.15 Hz):

Physiology: associated with the baroreflex, the blood pressure regulation mechanism

Control: mixed—both sympathetic and parasympathetic systems

Mechanism:

• When blood pressure changes, baroreceptors send signals.
• The autonomic system adjusts heart rate.
• This creates slow oscillations (around 0.1 Hz, period about 10 seconds).

Interpretation:

• High LF may indicate activation (sympathetic + parasympathetic)
• Low LF may indicate reduced regulation

HF (High Frequency, 0.15–0.4 Hz):

Physiology: associated with respiratory arrhythmia

Control: predominantly parasympathetic (vagus nerve)

Mechanism:

• During inhalation: parasympathetic activity decreases → pulse increases
• During exhalation: parasympathetic activity increases → pulse decreases
• This creates fast oscillations synchronized with breathing

Interpretation:

• High HF = good parasympathetic activity = relaxation, recovery
• Low HF = reduced parasympathetic activity = stress, fatigue

LF/HF ratio:

Physiology: balance between sympathetic and parasympathetic systems

Interpretation:

• High LF/HF (>2): predominance of sympathetic activity, stress, activation
• Low LF/HF (<1): predominance of parasympathetic activity, relaxation, rest
• Normal LF/HF (1–2): balanced state

Normalized units (nu):

• LF_nu and HF_nu express the percentage of total power excluding VLF.
• This allows comparisons between people by removing the influence of total power.
• LF_nu + HF_nu = 100%

Clinical significance:

• Reduced HF and increased LF/HF are markers of stress and fatigue.
• Athletes at rest often show high HF and low LF/HF.
• Under chronic stress, LF/HF is persistently elevated.
• Recovery after exercise is accompanied by an increase in HF.

8.5.4. Functional Group

Interpretation: Functional state of the autonomic nervous system.

Physiological explanation of the functional group:

The functional group is a comprehensive evaluation of autonomic nervous system state based on normalization of HRV parameters relative to age- and sex-specific norms.

How it works:

• Each person has “normal” SD1, SD2, and HR values based on age and sex.
• Actual values are compared with these norms (z-scores).
• The combination of z-scores determines the functional group.

See the document *Classification of Functional Groups*.

8.5.5. Additional Parameters

Relaxation:
Relaxation = 1e6 * SD1 * SD2 / HR [ms²/beat]

Interpretation: an integral relaxation indicator.

rrHRV median:
Median distance to the central point on the map of relative RR intervals.

Extra point:
Additional score in the functional group (0–100).

8.6. Visualization

8.6.1. RR Tachogram

Graph of RR intervals over time:

• Blue line: RR intervals
• Gray points: local minima (respiratory cycles)
• Green line: signal power (scaled)
• Gray vertical lines: section markers

8.6.2. RR Tachogram Spectrum

FFT spectrum of the interpolated RR signal:

• X-axis: frequency (0–0.5 Hz)
• Y-axis: spectral amplitude
• LF (0.04–0.15 Hz) and HF (0.15–0.4 Hz) ranges are marked
• LF%, HF%, and LF/HF ratio are displayed

8.6.3. Poincaré Plot (Local Return Map)

Scatter plot of RR(i) versus RR(i-1) or RR(i-shift):

• Each point: a pair of consecutive RR intervals
• Colors: different sections
• Ellipses: 2SD1 and 2SD2 for each section

1. Shift RR: shift for the Poincaré plot (1–15 intervals)
2. Checkboxes for including or excluding sections

Physiological explanation of the Poincaré plot:

The Poincaré plot (return map) is a graphical way to visualize heart rate variability by showing the patterns of RR interval changes.

How to read the plot:

• Each point = a pair of consecutive RR intervals
• X-axis: RR(i-1), the previous interval
• Y-axis: RR(i), the current interval
• If the heart beat like a metronome, all points would be in one place.

Typical shapes:

Round/oval shape (healthy heart):

• high variability
• good adaptability
• healthy ANS state
• SD1 and SD2 are roughly equal

Elongated shape along the diagonal:

• low short-term variability (small SD1)
• long-term trends remain (larger SD2)
• possible stress or fatigue
• low SD1/SD2

Compact cloud:

• low variability overall
• very regular heartbeat
• possible health problems
• both SD1 and SD2 are small

Shifted center:

• change in mean RR
• transition between states (rest → load)
• response to a stimulus

Physiological meaning of SD1 and SD2:

• SD1 (minor axis):
  • short-term variability
  • beat-to-beat changes
  • predominantly parasympathetic activity
  • associated with respiratory arrhythmia
• SD2 (major axis):
  • long-term variability
  • trends and slow changes
  • sympathetic + parasympathetic activity
  • associated with the baroreflex and other slow regulatory processes

Clinical significance:

• Healthy people: round or oval cloud, SD1/SD2 ≈ 0.3–0.5
• Stress: the cloud becomes elongated, SD1 decreases
• Aging: the cloud becomes more compact, both SDs decrease
• Pathology: a very compact cloud or an unusual shape

8.6.4. Segmental HRV Analysis

The graph shows the dynamics of HRV parameters over time:

• HR: heart rate
• FuncGroup: functional group (×10)
• ExtraPoint: additional score (×100)
• z(HR): pulse z-score
• z(SD1): SD1 z-score
• z(SD2): SD2 z-score
• RMSSD: RMSSD
• Relaxation: Relaxation
• Concentration: Concentration (from EEG)
• Breath: breathing rate

All parameters are calculated in a sliding window with 75% overlap.

1. Checkboxes for including or excluding HRV parameters
2. Average Window: Averaging window for segmental analysis (10–100 intervals)
3. Save continuously HRV: Save continuous HRV parameters over time

8.7. Data Export

• Save HRV: Save HRV parameters by section to CSV
• Save RR: Save RR intervals to CSV
• Save continuously HRV: Save continuous HRV parameters over time

Conclusion

CleverPoint View provides a comprehensive set of tools for physiological signal analysis with detailed control over every processing stage. Understanding how each transformation works helps researchers to:

• Interpret results correctly: Knowledge of the mathematical foundations of the methods helps avoid incorrect conclusions.
• Choose optimal parameters: Understanding the effect of each parameter on the result makes it possible to adjust the analysis to a specific task.
• Validate results: Knowledge of the limitations of methods helps assess result reliability.
• Publish results: A detailed description of processing methods is required for scientific publications.

9.1. Recommendations for Use

• Always document processing parameters: record all settings used (Trim, CAR, Denoise, filters, etc.), including a note about blink artifacts.
• Check data quality: use the Time domain interface to visually inspect signals before analysis.
• Compare different methods: use the option to overlay two processing pipelines.
• Account for anthropometric data: for HRV analysis, always specify age, sex, height, and weight.
• Validate results: compare results with literature data and known norms.

9.2. Additional Resources

• Official website: cleverpoint.pro
• Application version: 2.15
• Supported formats: CSV with section metadata

Note: This guide is based on CleverPoint View Data version 2.15. All described methods and formulas correspond to the implementation in the application code.

9.3. Beginner’s Guide

If you are new to electrophysiology, we recommend the following order of study:

1. Start with the basics (Section 0):

• Learn what EEG, ECG, and EMG are.
• Understand the meaning of frequency ranges (alpha, beta, theta, gamma).
• Learn about the autonomic nervous system and HRV.

2. Study signal processing (Section 1):

• Understand why each operation is needed (Trim, CAR, Denoise, etc.), especially Denoise for blinks.
• Start with simple operations (Trim, Filter).
• Gradually add more complex ones (CAR, Denoise, EMD), where Denoise is responsible for blink artifacts.

3. Practice with the interfaces:

• Time domain: start here—signal visualization will help you understand what is happening.
• Summary: use it for an overview—you will see all parameters at once.
• Frequency domain: study spectrograms—you will understand frequency patterns.
• Cardio domain: study HRV—an important indicator of health.
• Emotional state: study emotions—an interesting application of EEG.

4. Interpret results:

• Always consider the physiological meaning of parameters.
• Compare with normal values.
• Take context into account (rest vs. load).
• Do not rush to conclusions—one parameter does not provide the full picture.

5. Study the literature:

• Read scientific papers on psychophysiology.
• Learn the normal values of parameters.
• Understand the limitations of the methods.

Important to remember:

• Electrophysiology is a complex science that requires time to learn.
• Not all parameters can always be interpreted unambiguously.
• Context is very important—the same values may mean different things in different situations.
• Always critically evaluate results and compare them with the literature.