Envisioning the Ideal Brain Imaging Technique

PSYCH 403A1 - Neuroimaging and Neurostimulation

Step 1: Import our digital toolbox!

Think of this like ge ! ing your cooking utensils before making a meal.

Assignment 1: EEG Data Loading and Basic

Filtering




Setting up your brain data analysis toolkit...
Mobile users: This is the perfect time to grab some water!
Detected Google Colab - installing packages...
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.4/7.4 MB 43.4 MB/s eta 0:00:
Packages installed!
SUCCESS! All tools loaded and ready to go!
MNE-Python version: 1.10.1
NumPy version: 2.0.2
You're all set to analyze some brain waves!
print ( " Setting up your brain data analysis toolkit..." )
print ( " Mobile users: This is the perfect time to grab some water! " )
try :
import google.colab
print ( " Detected Google Colab - installing packages..." )
%pip install -q mne
print ( " Packages installed!" )
except ImportError:
print ( " Running in Binder or local environment - packages should alr
import mne
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
import os
from pathlib import Path
plt.rcParams[ 'figure.figsize' ] = ( 10 , 6 )
plt.rcParams[ 'font.size' ] = 11
plt.rcParams[ 'lines.linewidth' ] = 1.5
mne.set_log_level( 'WARNING' )
print ( " SUCCESS! All tools loaded and ready to go!" )
print ( f " MNE-Python version: {mne. __version__ } " )
print ( f " NumPy version: {np. __version__ } " )
print ( " You're all set to analyze some brain waves! " )
Now for the exciting part - ge ! ing some real brain data!

Step 2: Loading Your Brain Wave "Music File" 

print(" Applying band-pass filter (1-40 Hz)...")
Loading brain wave data (this might take a moment)...
☕ Perfect time to stretch if you're on mobile!
SUCCESS! Brain data loaded!
Data info:
Length: 277.7 seconds of brain activity
Channels: 59 EEG electrodes
⚡ Sampling rate: 601 measurements per second
Ready to explore some brain waves!
print ( " Loading brain wave data (this might take a moment)..." )
print ( " ☕ Perfect time to stretch if you're on mobile!" )
sample_data_folder = mne.datasets.sample.data_path ( )
raw_file = sample_data_folder / 'MEG' / 'sample' / 'sample_audvis_raw.fif'
raw = mne.io.read_raw_fif ( raw_file, preload= True )
raw.pick_types ( meg= False , eeg= True , stim= False , exclude= 'bads' )
print ( " SUCCESS! Brain data loaded!" )
print ( f " Data info:" )
print ( f " Length: { raw.times [ -1 ] :.1f } seconds of brain activity" )
print ( f " Channels: { len ( raw.ch_names ) } EEG electrodes" )
print ( f " ⚡ Sampling rate: { raw.info [ 'sfreq' ] :.0f } measurements per secon
print ( " Ready to explore some brain waves! " )
We’ll apply a band-pass ! lter (1–40 Hz) to clean the EEG, like pu ! ing noise-
canceling headphones on your brain data.

Step 3: Basic Filtering (Noise Cancellation)  Applying band-pass filter (1-40 Hz)... Filtering complete! EEG 001 print ( " Applying band-pass filter (1-40 Hz)..." ) raw_filtered = raw.copy ( ) . filter ( l_freq= 1 ., h_freq= 40 ., fir_design= 'firwin print ( " Filtering complete!" ) # Plot a few channels before and after filtering raw.plot ( n_channels= 10 , duration= 5 , title= 'Raw EEG (before filtering)' ) raw_filtered.plot ( n_channels= 10 , duration= 5 , title= 'Filtered EEG (1-40 Hz)

EEG 001
40.0
EEG 002
EEG 003
EEG 004
EEG 005
EEG 006
EEG 007
EEG 008
EEG 009
EEG 010
2

Help

50 100 150

Time (s) 200 EEG 001 40.0 v EEG 002 EEG 003 E E G 0 0 4 EEG 005

EEG 005
EEG 006
EEG 007 1)
EEG 008 1
EEG 009
EEG 010

Help

50 100 150

Time (s) 200 EEG 001 40.0 v EEG 002 EEG 003 E E G 0 0 4 EEG 005

EEG 006
EEG 007 1)
EEG 008 1
EEG 009
EEG 010

Help

50 100 150

Time (s) 200 This is like a music equalizer — we’ll see which brainwave “notes” are strongest (delta, theta, alpha, beta, gamma).

Step 4: Frequency Analysis (Power Spectral Density) 

Calculating power spectral density (PSD)... PSD plotted! You can now see brain wave frequency strengths. Power (dB/Hz re 1 uV2) 20 10
• 10
• 20
• 30
• 40
• 50
EEG 20 30

Frequency

(Hz)

print ( " Calculating power spectral density (PSD)..." )
raw_filtered.plot_psd(fmax= 60 , average= True , spatial_colors= False , dB= True
print ( " PSD plotted! You can now see brain wave frequency strengths." )

Code: We had troubles saving the code so it is pasted below:
// Brain particle system
let particles = [];
function setup() {
// HSB in 0..1 (you already had this)
colorMode( HSB , 1 , 1 , 1 );
for ( let i = 0 ; i < 100 ; i++) {
particles.push({
x: random(width),
y: random(height),
// base fast velocities (your choice kept)
vx: random( 0 , 50 ),
vy: random(- 2 , 50 ),
life: 1.0 ,
seed: random( 1 ) // NEW: per-particle hue for rainbow
});
}
}
function draw() {
// Use BrainImation sliders: Attention≈beta, Meditation≈alpha
const alpha = (eegData.meditation ?? eegData.alpha ?? 0 );
const beta = (eegData.attention ?? eegData.beta ?? 0 );
// stress logic: true if attention > meditation
const stressed = beta > alpha + 0.02 ; // tiny margin avoids flicker
// BG: stressed = white, calm = dark
if (stressed) background( 0 , 0 , 1 , 0.15 );
else background( 0 , 0 , 0.06 , 0.10 );
// speed scale: calm slow, stressed fast
const speed = stressed ? 1.0 : 0.18 ; // <<< slow when calm
for ( let p of particles) {
// Movement influenced by brain waves (your logic kept)
p.vx += (random(- 1 , 1 ) * (eegData.alpha ?? alpha) * 0.1 );
p.vy += (random(- 1 , 1 ) * (eegData.beta ?? beta) * 0.1 );
// Apply speed scaling (NEW)
p.x += p.vx * speed;
p.y += p.vy * speed;
// Wrap around
if (p.x < 0 ) p.x = width;
if (p.x > width) p.x = 0 ;
if (p.y < 0 ) p.y = height;
if (p.y > height) p.y = 0 ;
// Color: stressed = red, calm = rainbow (NEW)
let hue = stressed ? 0.0 : (p.seed + frameCount * 0.002 ) % 1.0 ;
fill(hue, 0.9 , 0.95 , p.life);
noStroke();
ellipse(p.x, p.y, 5 + (eegData.gamma ?? 0 ) * 10 );
// life (your fade kept)
p.life *= 0.995 ;
if (p.life < 0.1 ) p.life = 1.0 ;
}
}

Demo
Calm Mode:
Meditation ≈ 0.8 ↑ Attention ≈ 0.2 ↓
Dark background, slow/rainbow drift
Stressed mode:
Attention ≈ 0.9 ↑ Meditation ≈ 0.1 ↓
White background, red fast streaks

Zen Blood Particles: A Brain-Controlled Visualization of Stress and Calm
Contributors:
Ayaan Jimale, Sarah Badran, Marinaya Saigh, Hamza Razzo
Concept
Our project began with the Brain Particles example from the BrainImation platform.
We wanted to transform a simple particle system into a bio-responsive visualization of stress and calm
— a sort of “digital mood” that reflects brain activity. When a user becomes stressed (higher beta /
attention activity), the screen erupts into fast-moving red particles against a white background,
resembling blood splatter or heightened physiological arousal. When the user relaxes (higher alpha /
meditation activity), the environment shifts to a dark background with slow, softly colored rainbow
particles drifting gently. The contrasting aesthetics represent two opposing brain states: tension versus
calm.
Technical Implementation
We used the built-in BrainImation variables eegData.attention (as a proxy for beta ) and
eegData.meditation (as a proxy for alpha ).
The program continuously compares these two values to determine the current state:
if attention > meditation → stressed mode
else → calm mode
Stressed Mode
● White background
● Red particles
● High velocity (up to 50 px per frame)
● Slight downward/right bias to suggest urgency and gravity
Calm Mode
● Dark background
● Rainbow-cycling particle hues (based on each particle’s seed and frame count)
● Reduced velocity for smoother, slower drift
To avoid flicker during rapid EEG fluctuations, the code includes a small threshold (attention >
meditation + 0.02) before switching states.
This keeps transitions visually stable while remaining responsive in real time.
The system also uses the “Simulate Data” mode for testing, with the Attention and Meditation
sliders controlling stress and calm levels manually.
When connected to a Muse headset, these values update automatically from the user’s EEG signals.
Challenges
Early versions of the code produced constant flickering because simulated EEG data changed
frame-by-frame. We fixed this by slightly smoothing the inputs and introducing a small difference

threshold before toggling modes. Another challenge was balancing aesthetics and performance: large
particle velocities looked dramatic but caused visual clutter, so we tuned the speed scale for a more
natural flow. We also adjusted HSB color ranges so that red remained vivid on a white background
while rainbow hues remained visible on a dark one.
What We Learned
Working with BrainImation showed how small numerical differences in brain-wave bands can create
striking visual differences. We learned how to translate abstract EEG measures (alpha vs beta) into an
intuitive sensory experience of stress versus relaxation. The project also reinforced principles of
real-time visualization, such as smoothing noisy data, scaling motion by signal intensity, and
designing visual metaphors that match psychological states.
Future Improvements
● Add gradual color blending instead of hard switching between modes.
● Include subtle particle size or opacity changes linked to overall calmness level.
● Experiment with live Muse input to explore individualized thresholds.
● Optionally add sound modulation for a multimodal neurofeedback experience.
Summary
Zen Blood Particles transforms EEG data into a visceral display of the brain’s changing internal state.
By mapping stress to speed and color intensity, and calmness to slow, colorful drift, the animation
becomes a live mirror of the user’s mental tension and relaxation — a minimal yet powerful
demonstration of brain-computer interaction through art.

Name: Marinaya Saigh
CCID: saigh@ualberta.ca
PSYCH 403: Neuroimaging and Neurostimulation
Part 1: Calm Bloom Neurofeedback Visualization:
In this assignment, which is an extension of my assignment #2. Calm Bloom starts with the Brain
Particles layout and turns it into a picture that reacts to feelings, plus it shows whether the mind is quiet or
tense. If the user relaxes and the headset records strong meditation values, the dots drift more slowly,
glide in gentle circles across a black screen, and fade through every colour of the rainbow—the colours
stand for a steady, flowing mind. If stress or sharp focus takes over and the headset detects high attention
levels, the screen flips to white, the dots blink red, and they dart around in a fast, jumbled pattern. A slim
bar at the bottom fills or empties to show how close the user is to calm, and a faint line above it traces the
same measure second by second—the picture keeps moving, or the user sees steady change instead of a
plain yes-or-no signal.
The program processes EEG readings from eegData.meditation and eegData.attention in real time. The
values are compared to classify the user's brain state. When the value of attention surpasses the value of
meditation, the user is marked as stressed. When the value of meditation exceeds that of attention, the
user is deemed calm. Both states affect the visual output – colour, brightness and velocity of the objects –
and change the proper display of the “calmness status history” graph. Staying calm for an extended period
of time increases the on-screen progress bar, rewarding stability rather than mere blips of relaxation. The
overall effect is a biological feedback loop in which the user can perceive both their focus and calmness
evolving in real time as colours and rhythms.
Rather than static BCI art that merely reacts to the brain, Calm Bloom uses layered feedback. Calmness is
an attribute that is varied, built and decayed on a time scale, similar to training a muscle. The design is a
fusion of aesthetics and function. Calm Bloom does not merely display the state of the user’s brain, but is
aimed at teaching the user to sustain relative inner stability. The combination of the live graph of
calmness, the incremental progress and the colour emblems contributes to the work, making it both
aesthetic and functional.
Through this assignment, I learned how small differences in brainwave ratios can easily bring life into
the data , stimulating visual feedback. The relationship between EEG signals and motion, brightness, and
colour deepened my understanding of how biological feedback systems furnish self-regulation. How

powerful are the simplest kinds of cues that can lead to behavior like progress bars or even smooth
transitions. Ultimately, Calm Bloom took a simple representation of the data and turned it into an
interactive, meditative experience for the participant, bringing mind, data, and art together.EEG Data
Used: eegData.meditation, eegData.attention
Part 2: ERP experiment:
In this experiment, I measure the N170 event-related potential (ERP) component. The N170 is a
well-known negative deflection that peaks around 170ms after stimulus onset and is typically seen at
occipitotemporal electrode sites like TP9 and TP10. It is most strongly elicited by faces and is thought to
reflect the early structural encoding of facial features. The N170 provides a reliable neural marker for
distinguishing face-specific visual processing versus general object recognition. In the stimulus, there are
two visual categories: faces and objects. In each trial, either a stylized cartoon face or a simple geometric
object is presented. The two conditions are presented in an alternating sequence to ensure balanced
exposure and to minimize order effects. This allows us to directly compare neural responses to socially
relevant stimuli (faces) vs non-social stimuli (objects), a classic approach in N170 paradigms.
The averaging system captures and processes EEG data from 4 channels: TP9, AF7, AF8, and TP10.
Where each trial produces an epoch of 200ms before to 800ms after stimulus onset, sampled at 256Hz. To
obtain more accurate data, a baseline correction is applied. The baseline correction is subtracting the
average amplitude from the time before the stimulus was shown. The baseline correction helps to
eliminate slow drifts and account for individual differences in the baseline voltage. For each condition
presented, up to 20 epochs per channel are retained, which enables for a thorough analysis of the results.
The system then computes a point-by-point mean calculation across all existing epochs and then produces
an averaged ERP waveform that represents the characteristic neural response for each stimulus type. If
tested with real EEG data, the expected result would be a larger, more negative N170 amplitude for faces
than for objects, especially at TP9 and TP10, with the strongest response often over the right hemisphere.
This would match decades of ERP research showing the N170’s sensitivity to facial structure and its role
in early visual categorization.