Neuro-Flux Band: The Wearable Brain Monitor

Original Code:
// Neural mandala
let angle = 0 ;
function setup() {
colorMode( HSB , 360 , 100 , 100 );
}
function draw() {
background( 0 , 0 , 0 , 0.05 );
translate(width/ 2 , height/ 2 );
let layers = 5 ;
for ( let layer = 0 ; layer < layers; layer++) {
let radius = 50 + layer * 40 ;
let points = 6 + layer * 2 ;
for ( let i = 0 ; i < points; i++) {
let a = angle + ( TWO_PI / points) * i;
let x = cos(a) * radius;
let y = sin(a) * radius;
let hue = (angle * 57.3 + layer * 60 ) % 360 ;
let brightness = 50 + eegData.attention * 40 ;
let size = 5 + eegData.alpha * layer * 2 ;
fill(hue, 80 , brightness, 0.7 );
noStroke();
ellipse(x, y, size);
}
}
angle += eegData.meditation * 0.02 + 0.005 ;
}

New Code
// Neural mandala
// Neural mandala — EEG-reactive visualization by Hannah Cooper
// This animation transforms EEG brainwave data into a colorful, pulsating mandala.
// The piece visualizes focus (attention), relaxation (alpha), and calmness
(meditation)
// through color, brightness, size, and rotational speed.
let angle = 0 ;
function setup() {
// Use HSB color mode for dynamic hue control (easier to shift colors smoothly)
colorMode( HSB , 360 , 100 , 100 );
noStroke(); // Clean, glowing look with no outlines
}
// Simulated EEG data for testing (BrainImation will replace these values in
real-time)
let eegData = {
attention: 0.6 , // Represents mental focus or engagement (0–1)
alpha: 0.5 , // Represents relaxation or calm wakefulness (0–1)
meditation: 0.4 // Represents deep calm and reduced stress (0–1)
};
function draw() {
// Create a faint trail effect for smooth animation persistence
background( 0 , 0 , 0 , 0.05 );
// Center the mandala on the screen
translate(width / 2 , height / 2 );
let layers = 6 ; // Number of concentric circular layers (rings of petals)
// Loop through each layer to build the mandala structure
for ( let layer = 0 ; layer < layers; layer++) {
// Radius grows with each layer and expands with meditation (flower “blooming”)
let radius = 30 + layer * 40 * ( 1 + eegData.meditation);
// Each layer has more points (petals) as it expands outward
let points = 6 + layer * 2 ;
// Draw each point (petal) in the circular layer
for ( let i = 0 ; i < points; i++) {
// Determine angular position around the circle

let a = angle + ( TWO_PI / points) * i;
// Add gentle pulsing motion using sine waves for organic breathing effect
let pulse = sin(angle * 3 + i * 0.5 + layer) * 10 ;
// Convert polar coordinates to (x, y) with pulse offset
let x = cos(a) * (radius + pulse);
let y = sin(a) * (radius + pulse);
// Visual characteristics based on EEG data and motion
let hue = (angle * 40 + layer * 60 + i * 15 ) % 360 ; // Color rotation by angle +
layer depth
let brightness = 40 + eegData.attention * 60 ; // More attention = brighter
colors
let size = 5 + eegData.alpha * layer * 2 ; // More alpha = larger petal
size (relaxed flow)
// Conceptual variables for extended design ideas (petal stretching)
let petalWidth = 10 + eegData.alpha * 20 ;
let petalHeight = 30 + eegData.alpha * 50 + pulse;
// Draw each petal point with its hue, saturation, and brightness
fill(hue, 80 , brightness, 0.9 );
ellipse(x, y, size);
}
}
// Draw the mandala’s center — its size pulses with meditation level (calmness)
fill( 50 , 80 , 90 , 0.9 );
let centerSize = 50 + eegData.meditation * 100 * sin(angle * 2 );
ellipse( 0 , 0 , centerSize);
// Rotation speed adjusts with the center’s size (more calmness = smoother movement)
// constrain() prevents extreme speed changes
let clampedCenter = constrain(centerSize, 30 , 170 );
let speed = map(clampedCenter, 30 , 170 , 0.003 , 0.035 );
angle += speed; // Update global rotation angle
}
// Automatically resize canvas when window changes size
function windowResized() {
resizeCanvas(windowWidth, windowHeight);

Hannah Cooper
1771074
Psych 403 A1: Neuroimaging and Neurostimulation
October 20, 2025
Word count: 1,188
BrainImation : Neural Mandala
My project, Neural Mandala , visualizes the relationship between focus, calmness, and
mental balance through brainwave data. I wanted to show how the mind is always in motion
even in states of meditation and how moments of focus and calm coexist. The animation takes
the form of a mandala, a traditional symbol of harmony and self - awareness, but it reacts
dynamically to EEG signals. The center orb expands and contracts based on meditation levels,
symbolizing rhythmic breathing and inner stillness. The surrounding petals rotate in response to
these changes, speeding up as the center grows and slowing down as it contracts. The result is a
visual balance between stillness and motion, where the speed and flow of the pattern reflect
mental state. Overall, the animation captures the idea that calmness is not the absence of activity,
but a state where everything flows in sync. By linking human cognitive states to visual motion,
this piece explores how data from the mind can become a dynamic form of digital expression.
Ultimately, the visualization bridges neuroscience and art, turning invisible cognitive data into
visible, expressive motion.
I began with the original Neural Mandala template provided in BrainImation but made
substantial modifications to create a more responsive and expressive visualization. I first defined
a simulated EEG data object to represent three core cognitive variables: attention, alpha
(relaxation), and meditation. I then integrated these values into the mandala’s structure so that
attention now controls color brightness, alpha affects the size and pulsing of the petals, and

meditation influences the rotational speed and overall expansion of the form. I also introduced
sinusoidal motion to create a breathing - like effect, making the mandala feel alive and organic.
Additionally, I added a dynamic center pulse that grows and contracts with meditation levels to
visually emphasize changes in pace. The rotation speed was remapped to this pulse rather than
being linearly tied to EEG values, allowing for smoother, more natural transitions. Finally, I
improved usability by adding automatic canvas resizing and moving noStroke() to setup() for
efficiency and cleaner visuals. I also included detailed comments throughout the code to explain
what each section does and how it connects to EEG data.
The biggest challenge was balancing the visual motion to look smooth and meaningful
rather than chaotic. At first, the petals all rotated at a constant speed, which made the animation
feel mechanical and disconnected from the intended meditative tone. Linking the rotation tempo
to the meditation - driven center size solved this, creating a natural breathing rhythm. I also had to
experiment with the range mapping for speed and brightness so the visual changes were neither
too subtle nor too extreme. Finding the right map() values for angle and color transitions required
several rounds of testing to achieve a visually coherent result.
This project helped me understand how brain - computer interfaces can be used creatively
to visualize mental states. I discovered that attention, alpha, and meditation each represent
measurable aspects of cognition and emotion that can be artistically expressed through
parameters like color, size, and speed. I developed stronger technical skills in P5.js animation,
particularly in using mapping functions, sinusoidal motion, and responsive rendering.
Conceptually, I learned how art and technology can work toge ther to communicate inner
experiences that are often invisible. Through this project, I realized that neuroart is not just about

displaying data; it is about translating brain activity into something emotionally resonant and
deeply human.

Hannah Cooper
1771074
Part 1: EG Snake: Target and Adaptive Neurofeedback Game
My project, EG Snake , is a closed-loop neurofeedback game that merges two advanced
BCI principles: target brain-state training and adaptive difficulty control. The system begins with
a brief calibration phase that establishes my personal alpha baseline and defines a target
threshold ( targetAlpha ). During gameplay, the animation continuously measures how closely my
live alpha activity matches this target. As my brain approaches the ideal range, the visuals
become more harmonious. The background glow intensifies, and a status badge progresses
from NEAR to CLOSE to IN ZONE. When the “IN ZONE” state is maintained, the score
multiplier accelerates and a streak counter builds, rewarding sustained self-regulation and
reinforcing calm, stable alpha production.
Simultaneously, beta activity (associated with attention and arousal) drives an adaptive
difficulty loop. Higher beta reduces challenge by slowing the snake’s speed, enlarging the food,
and smoothing turning, while lower beta increases the difficulty to keep engagement high. This
dual-feedback system ensures the player’s brain not only influences the environment but also
learns from it, creating a dynamic balance between challenge and relaxation rather than a
one-way visual response.
Developing this as a non-coder was challenging but highly educational. I learned to
debug real-time loops, tune thresholds, and manage smooth visual feedback without disrupting
logic flow. Implementing the calibration and adaptive loops revealed several key insights: EEG
signals drift and require recalibration; tiered, gradual feedback is more effective than simple
binary cues; and integrating two adaptive systems demands careful balancing of timing and
reward. Even when I did not reach the exact target state, watching the system respond
dynamically felt rewarding and motivating, illustrating the power of neurofeedback to sustain
engagement and promote self-awareness.

Hannah Cooper
1771074
Part 2: ERP Experiment (P300 Oddball with Circle→Annulus)
This experiment measures the P300 (P3) component, a positive ERP deflection
occurring approximately 300–600 ms after an unexpected or task-relevant event. According to
classic oddball literature (e.g., Polich, 2007), rare or significant stimuli evoke larger P300
amplitudes than frequent ones, reflecting attention allocation and working-memory updating.
Each 1000 ms trial begins from baseline. At 0 ms, a filled black circle (~100 px diameter)
appears for 30 ms, followed by a 60 ms blank gap, then a black annulus for 30 ms (outer radius
≈ 2× inner). The entire sequence lasts 120 ms, maintaining a 1 s inter-trial interval. Trials are
pseudorandomly assigned as STANDARD (80%) or RARE (20%). The physical stimuli are
identical, only the condition label differs, ensuring that ERP differences reflect cognitive rather
than visual factors.
For each trial, an epoch from −200 to +800 ms around circle onset is extracted across
channels TP9, AF7, AF8, and TP10 (256 Hz sampling). Baseline correction uses the
pre-stimulus −200 to 0 ms mean. Separate online averages are maintained for STANDARD and
RARE trials using incremental updating capped at roughly 20 epochs per condition to keep the
display responsive. The visualization shows two colored traces aligned to stimulus onset with
100 ms tick marks, vertical zero-line, condition counters, and a live stimulus monitor that
indicates phase and trial number.
If tested with real EEG, the RARE condition should display a larger parietal positivity
between 300 and 600 ms compared with the STANDARD trace, most pronounced at TP9 and
TP10. Both conditions would baseline near 0 μV before stimulus onset, and their divergence
would represent the classic P300 effect. This design fulfills all ERP-analysis criteria: clearly

Hannah Cooper
1771074
defined component, controlled stimulus manipulation, proper epoching and averaging, labeled
dual-trace visualization, and expected differential amplitude consistent with empirical literature.