Morphological Transformations in Neurographic Art: Algorithmic and Psychological Perspectives

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The intersection of psychological art therapies and computational generative algorithms provides a profound domain for investigating how visual structures can change, morph, and adapt to reflect cognitive states. At the core of this intersection is Neurographic art, or Neurographica, a highly structured, transformative drawing method developed by Russian psychologist and architect Pavel Piskarev in 20141. Designed to bypass conscious resistance and tap directly into the subconscious, the method relies on specific visual morphologies—primarily the continuous, unpredictable bionic line and the systematic rounding of intersections4. While originally conceived as an analog, manual practice involving paper and markers, the underlying principles of Neurographica mirror advanced computational paradigms found in generative art, computer-aided geometric design, and biophysical simulations.

As digital tools have evolved, the static nature of analog Neurographic drawings has been expanded into dynamic, morphing visualizations. By applying algorithms that simulate fluid dynamics, cellular growth, mathematical continuity, and chemical reaction-diffusion, the foundational rules of Neurographica can be translated into continuously morphing digital topologies6. Furthermore, integrating these algorithms with real-time biometric data, such as electroencephalography (EEG), allows the art to morph autonomously in response to the neurological state of the creator9. This analysis exhaustively details the mechanisms through which Neurography Art visualizations change and morph, transitioning from analog psychological frameworks to advanced computational and generative models, exploring the deep psychological rationale for these visual shifts and their mathematical counterparts.

The Foundational Morphology and Philosophy of Neurographica

To understand how Neurographic visualizations can morph computationally, one must first deconstruct the strict algorithmic rules that govern the analog practice. Piskarev formulated Neurographica not merely as an aesthetic exercise, but as a rigid psychological algorithm engineered to rewire neural pathways through the act of drawing—a process leveraging the brain's inherent neuroplasticity, mirror neurons, and multi-sensory integration4. Since its inception, the method has reached over ten million practitioners across thirty-eight countries, highlighting its profound impact on cognitive restructuring12.

A critical distinction must be drawn between true "Neurographica" and the more generalized "NeuroArt." While the latter focuses on the aesthetic pleasure of drawing neuro-inspired lines to transform stress into harmony, Neurographica is an intention-driven, heavily structured psychological discipline13. It requires strict adherence to safety precautions, particularly for beginners, as the rapid access to the subconscious can release overwhelming emotional tension4. The visualization changes and evolves precisely because the creator is subjected to a guided, structural metamorphosis of the image, governed by ten core philosophical principles.

 

The Pyramid of Consciousness and the Eight-Step Metamorphosis

The morphing of the visualization occurs systematically across eight distinct stages, known as the Basic Algorithm. This algorithm is predicated on Piskarev's "Pyramid of Consciousness," a theoretical model comprising four basic levels (each divided into two), tracing a path from the superficial present awareness down through the unconscious, ultimately reaching the "Self" or the "Absolute"4. The gradual immersion prevents psychological shock, allowing hidden problems to emerge visually at a manageable pace16.

The progression through these eight steps forces the visualization to morph from a state of linguistic abstraction to chaotic tension, and finally to bionic harmony.

 

Visual Semantics: The Piskarev Line and Geometric Primitives

The two primary engines of morphological change in a Neurographic drawing are the Neurographic Line (or Piskarev Line) and the act of Conjoining. The Neurographic Line is defined by its inherent bionic unpredictability. Unlike standard doodles, mechanical vectors, or repetitive sine waves, the fundamental rule mandates that the line must not repeat itself in any part of its movement, and the practitioner must intentionally guide the line in a direction where the mind does not expect it to go4. This intentional unpredictability forces the brain out of habitual neural firing patterns, introducing cognitive novelty and neuroplastic adaptation1.

When these unpredictable lines cross, they inevitably create intersections with sharp angles. In the visual and psychological language of Neurographica, sharp angles represent conflict, stress, fear, and cognitive blockages1. The visualization undergoes its most critical morphological shift during the Conjoining phase. The practitioner must meticulously locate every intersection and round off the corners, filling in the sharp crevices to create a web of smooth, flowing curves3. The image transforms from a chaotic, fragmented scribble into a cohesive, organic network resembling neural tissue, cellular structures, or biological fascia.

Furthermore, the introduction of geometric primitives during the Composition or Fixation phases introduces distinct semantic morphing. The shapes introduced are not arbitrary; they act to break rigid thought patterns and introduce new possibilities1.

 

Neurological Architecture and the Expressive Therapies Continuum

The morphological changes occurring on the canvas are intrinsically linked to physiological changes within the brain. By engaging in the slow, intentional process of smoothing angles and weaving continuous lines, the practitioner induces a state of active meditation, bypassing the linguistic centers of the brain and engaging spatial and motor cortices directly5.

The Expressive Therapies Continuum (ETC)

The morphological progression of Neurographica aligns seamlessly with the Expressive Therapies Continuum (ETC), a pantheoretical, outcome-informed framework utilized in art therapy to link artistic expression to specific areas of brain function and information processing21.

The ETC models human interaction with artistic media across three hierarchical levels, mirroring the morphing of a Neurographic drawing from raw energy to complex symbolism:

1. Kinesthetic/Sensory Level: The rapid, chaotic scribbling of the initial "Catharsis" stage engages the basal ganglia and primary motor cortex. The physical act of pressing a marker to paper and moving it erratically processes raw, pre-verbal physiological energy21.

2. Perceptual/Affective Level: The meticulous rounding of corners and the application of color engage the limbic system, the amygdala, and the visual cortex. At this stage, the visualization morphs from a record of physical movement into a structured emotional landscape22.

3. Cognitive/Symbolic Level: The "Affixing" stage, where a specific geometric shape is isolated and thickened as a "solution," engages the prefrontal cortex and executive functioning. The brain transitions from feeling to thinking, assigning symbolic meaning to the morphed geometry and forming an actionable plan23.

In clinical neuroanatomy, drawing non-objective abstract geometric figures stimulates multiple neural pathways, activating the frontal, temporal, occipital, and parietal lobes21. For populations suffering from neurodegenerative conditions—such as Alzheimer's disease, Lewy body dementia, Frontotemporal dementia (FTD), and Benson's syndrome—the morphing structures of Neurographic art serve as a potent tool for cognitive maintenance. For example, Alzheimer's disease heavily affects the fusiform gyrus in the temporal lobe and its interconnections to the frontoparietal association cortex, resulting in the loss of contour and shape recognition21. By forcing the brain to recognize intersections and meticulously morph them into rounded curves, Neurographica stimulates cerebellar "memory genes" and kinesthetic sensory pathways, offering a neuroanatomical basis for slowing clinical decline and preserving emotional responsiveness despite cortical atrophy21.

Hemispheric Activation and EEG Topography

Neurographic drawing dynamically modulates the brain's electrical activity, a phenomenon quantitatively observable through electroencephalography (EEG). In states of high stress or anxiety—corresponding to the initial Catharsis stage of the drawing—the brain typically exhibits high-frequency beta waves4. As the practitioner transitions into the Conjoining phase, the repetitive, rhythmic nature of the fine motor task induces a profound shift in brainwave frequencies. The visualization's morphing is accompanied by a neurological morphing from chaotic beta activity into synchronized alpha waves (associated with relaxed wakefulness and creativity) and theta waves (associated with deep meditation and subconscious access)26.

The colors applied during the Integration and Archetyping phases further dictate the physiological and morphological resonance of the piece. Neuropsycholinguistic studies utilizing multichannel EEG on young adults demonstrate that color perception severely influences brainwave patterns and emotional regulation25.

• Warm Colors (Reds, Purples): Exposure to red and purple stimuli significantly activates beta and gamma waves in the centro-parietal and occipito-parietal regions (particularly in the 85-120 ms and 800-855 ms periods after stimulus onset), inducing states of arousal and intense concentration25.
• Cool Colors (Blues, Greens, Yellows): The application of yellows and greens shifts the EEG topography toward relaxation and comfort, maximizing alpha wave output and mitigating the arousal response25.

The deliberate balancing of these colors during the Archetyping phase allows the visualization to morph energetically, dictating the ultimate psychological tone of the completed Neurographic network. Furthermore, fMRI and MEG studies highlight that visual imagery engages a widely distributed, integrated cross-modal neural network, suggesting that the spatial and visual elements of Neurographic morphing create far more robust mental simulations of risk and reward than linguistic processing alone28.

Computational Continuity and Geometric Metamorphosis

While the analog practice of Neurographica relies on the human hand to execute the morphological shift from chaos to harmony, computational algorithms provide a mechanism to simulate, animate, and autonomously morph these visualizations. The translation of Neurographic principles into generative art or computer-aided geometric design (CAGD) requires mathematical frameworks capable of producing bionic unpredictability and topological smoothing29.

Analytical Corner Rounding and Spline Interpolation

The core mandate of Neurographica—the rounding of all intersections—is fundamentally an engineering problem of geometric continuity. In computational geometry and 5-axis CNC machining, the morphing of a sharp linear intersection (a [Figure omitted from source export] block) into a smooth, continuous curve is achieved through filleting and spline interpolation to prevent mechanical jerk and preserve the original toolpath shape32. However, simply bridging two intersecting lines with a basic circular arc is mathematically insufficient to achieve the "bionic" aesthetic demanded by Neurographica.

Geometric continuity is categorized into ascending levels of mathematical smoothness, each dictating how a visualization can morph at a vertex:

 

To computationally morph a rigid, intersecting network of lines into a Neurographic visualization, algorithms utilizing Bezier curves, B-splines, or non-uniform rational B-splines (NURBS) must be employed to enforce [Figure omitted from source export] or [Figure omitted from source export] continuity at every vertex31. Chaikin's algorithm, a pioneering curve subdivision method, offers a high-speed approach to curve smoothing by iteratively cutting off the corners of a polygon to produce a refined, smooth quadratic B-spline curve39.

By dynamically calculating the control points of biquadratic or bicubic Bezier patches, a generative algorithm can autonomously seek out [Figure omitted from source export] intersections (the points of conflict) and insert symmetrical or asymmetrical minimum-curvature splines34. As the algorithm iterates, the visualization morphs in real-time, dissolving angular constraints and yielding a perfectly conjoined, bionic topology that mathematically replicates the manual, psychological labor of human rounding.

Dynamic Morphing via Vector Fields and Force-Directed Graphs

The unpredictable nature of the Piskarev Line can be computationally modeled using vector fields driven by gradient noise, allowing for dynamic, animated morphing in generative frameworks like p5.js, WebGL, or GLSL29. A vector field assigns a directional force to every pixel or coordinate on the canvas, creating invisible currents8.

To simulate the morphing of a Neurographic line, autonomous agents (particles) are introduced into this vector field. Instead of moving in straight lines, the particles trace paths dictated by the underlying noise algorithms, resulting in sinuous, non-repeating curves that leave luminous trails41. The morphology of the visualization can be altered dynamically by manipulating the specific noise parameters:

• Domain Warping: By using nested 3D noise (e.g., computing noise(x, y, noise(x, y))), the vector field gains an uneven, structural complexity44. This creates subtle "unevenness" that simple 2D noise cannot achieve, mirroring the deep psychological complexity drawn out during the Topic 1.0 phase of Neurographica.
• Curl Noise: Taking the cross product of the gradient of a noise field generates a divergence-free vector field. Particles flowing through curl noise will exhibit swirling motion without obvious sinks or sources, creating dense, non-overlapping organic clusters reminiscent of the "Integration" phase30.

Through these algorithms, the visualization morphs continuously. The static lines of a traditional Neurographic drawing are replaced by glowing trails of particles that evolve, intersect, and diverge over time8. The user can intervene interactively, adjusting sliders that control noise scale, density, particle survival, and animation speed, effectively turning the canvas into a digital environment for real-time catharsis and flow-state generation8.

Furthermore, the structural integration of Neurographic shapes can be modeled using force-directed graph algorithms. Originally used to visualize complex network architectures, force-directed algorithms simulate physical forces—treating nodes as charged particles that repel each other, while edges act as springs that pull connected nodes together45. By incorporating localized "rovers" to simulate physical forces locally and utilizing Bezier curve path animations, the graph dynamically settles into an organic, uncluttered layout45. Crucially, for the visualization to adhere to Neurographic principles, the graph drawing algorithm must ensure that all corner adjacencies are mathematically rounded, preventing any sharp bends as the network morphs across data states48.

Self-Organizing Topologies: Reaction-Diffusion Systems

Beyond basic curve smoothing and particle flow, advanced computational biology models can be utilized to grow and morph Neurographic visualizations as if they were living organisms. The aesthetic of Neurographica heavily resembles biological tissues and cellular networks, making Reaction-Diffusion systems an ideal mechanism for generative rendering3.

Reaction-diffusion models describe how the concentration of substances changes in space over time due to local chemical reactions and physical diffusion51. The most prominent algorithm for generating organic textures is the Gray-Scott model, which describes the continuous morphing of two interacting chemical species, [Figure omitted from source export] (which is fed into the system) and [Figure omitted from source export] (which eats [Figure omitted from source export] and reproduces, but is simultaneously killed off)53.

The morphological changes in the visualization are governed by two coupled partial differential equations:

[Figure omitted from source export]

[Figure omitted from source export]

In these equations, [Figure omitted from source export] and [Figure omitted from source export] are the diffusion rates of the species, [Figure omitted from source export] is the spatial Laplacian operator, [Figure omitted from source export] is the feed rate, and [Figure omitted from source export] is the kill rate7. By varying the dimensionless parameters [Figure omitted from source export] and [Figure omitted from source export], the Gray-Scott algorithm generates a vast topology of morphing Turing patterns, mapping remarkably well onto Neurographic aesthetics.

 

By implementing a spatially-varying parameter mode—often visualized as a Pearson plot—where [Figure omitted from source export] and [Figure omitted from source export] form linear gradients across the [Figure omitted from source export] and [Figure omitted from source export] axes, the generative artwork can morph seamlessly across states7. A single digital canvas might feature chaotic spot patterns on the left edge ([Figure omitted from source export]), morphing into labyrinths in the center, and ultimately resolving into flowing, integrated stripes on the right edge ([Figure omitted from source export])57. This continuous spatial morphing serves as a powerful computational metaphor for the psychological journey of the 8-step Basic Algorithm, automating the transition from neurosis to clarity within a self-organizing system56. Furthermore, the integration of Generative Physics-Informed Neural Networks (PINNs) allows for the traversal of the latent space of these reaction-diffusion models, producing real-time morphing Turing patterns driven by neural network inference rather than strict differential calculation58.

Neural Cellular Automata and Goal-Guided Growth

While Reaction-Diffusion models chemical interactions, Neural Cellular Automata (NCA) leverage deep learning to simulate the localized, discrete rules of cellular growth and morphogenesis6. Traditional cellular automata, like Conway's Game of Life, use binary states and fixed rules29. NCAs, conversely, conceptualize an organism or a visual pattern as a grid of continuous cells, where each cell contains an [Figure omitted from source export]\-dimensional state vector representing hidden channels (memory) and a specialized "living" channel (where an alpha value [Figure omitted from source export] dictates cellular life)49.

At each discrete, stochastic time step, cells communicate with their immediate neighbors via fixed convolution filters. Traditionally, these are classical Sobel filters that estimate the partial derivatives of cell state channels in the [Figure omitted from source export] and [Figure omitted from source export] directions, mimicking chemosensory receptors detecting chemical gradients60. The cell concatenates these gradients with its own state and passes the resulting perception vector through a compact neural network to determine its next update60.

To apply NCA to Neurographic art, a network can be trained to "grow" specific shapes—such as perfect circles, triangles, or complex neurographic archetypes—from a single seed pixel49. The morphing process here is profoundly robust. If a section of the visualization is erased or perturbed by the user, the local cells sense the damage via their perception filters and autonomously regenerate the missing structure, perfectly mimicking biological healing and psychological resilience60.

Recent advancements have introduced profound ways to dictate how these visualizations change and morph:

• Isotropic NCA: The original NCA models were anisotropic, meaning they could only grow patterns in a specific orientation dictated by the Sobel [Figure omitted from source export] and [Figure omitted from source export] filters6. By replacing the Sobel filters with a single, rotation-invariant Laplacian filter, Isotropic NCA models prevent the system from relying on global coordinate axes. This allows the generated neurographic patterns to grow organically and symmetrically in any orientation, preserving the boundless, bionic philosophy of Piskarev's original principles6.
• Goal-Guided Neural Cellular Automata (GoalNCA): Standard NCAs are inherently uncontrollable after the initial seed is planted. GoalNCA infuses target encodings into the hidden states of living cells at every step of the growth cycle49. By training a goal encoder concurrently with the NCA, the visualization can dynamically change its behavior during the growth process. If we consider a Neurographic session where the practitioner's intention (Topic 1.0) shifts midway through the exercise, a GoalNCA algorithm can interpret a new parameter input and seamlessly command the cellular matrix to morph from one target pattern into a radically different one without causing systemic collapse49.
• Adversarial Perturbations: By multiplying trainable symmetric perturbation matrices with NCA cell states, the system's behavior can be guided toward new targets (such as morphing the color palette from warm stress to cool harmony) while re-using the existing trained NCA weights, allowing for highly efficient, real-time stylistic morphing49.

Cybernetic Biofeedback: The Ultimate Metamorphosis

The ultimate synthesis of Neurographic art and algorithmic morphing occurs when the two domains are linked in a closed-loop system using real-time biometric sensors. Because the foundational purpose of Neurographica is to alter the creator's emotional and neurological state, the resulting physiological changes can be quantified and fed back into generative algorithms, allowing the visualization to morph synchronously with the user's brainwaves.

EEG-Driven Generative Rendering

Electroencephalography (EEG) headsets—ranging from consumer-grade devices utilizing the NeuroSky API to higher-fidelity clinical systems—capture electrical neural activity and categorize it into discrete frequency bands alongside proprietary metrics for "attention" and "meditation"9. These biometric streams are translated into control signals that dictate the morphing parameters of the digital artwork62.

In applications such as "NeuroPaint," the artist's cognitive state directly influences the generative rendering engine (e.g., built in Processing or p5.js)9.

• Color and Stroke Morphing: The hue of the digital stroke is tethered to meditation levels. High stress or cognitive load (low meditation) triggers warm, chaotic colors, while elevated calmness morphs the palette into cool, harmonious tones9. Simultaneously, the thickness of the generative brush responds to the user's level of mental focus, becoming finer and more precise as concentration peaks9.
• Vector Field Manipulation: The intensity of a generative flow field can be linked to the user's attention metric. A lack of focus may increase the amplitude and unevenness of the underlying domain-warped Perlin noise, causing the digital Neurographic lines to kink, cross, and wander erratically, generating [Figure omitted from source export] conflicts8. As the user achieves deep concentration, the noise amplitude dampens, morphing the chaotic tangle into streamlined, parallel Field Lines exhibiting [Figure omitted from source export] acceleration continuity8.
• Particle Systems: Biofeedback can dictate the kinetic behavior of autonomous agents. An elevated heart rate or a burst of beta-wave activity might cause particle systems to exhibit repulsion forces, scattering aggressively across the canvas10. As the user transitions into a state of emotional catharsis and subsequent relaxation, the algorithmic parameters shift. The particles morph into attractive behaviors, coalescing into the unified geometric archetypes (circles for harmony, squares for stability) mandated by the Integration and Fixation phases of Neurographica1.

Pioneering installations, such as cognitive neuroscientist Kim Awa's "Music of the Mind" project inside James Turrell's Skyspace, demonstrate the profound potential of this integration. By recording the neural electrical activity of individuals as they experience shifting light and atmosphere, the unique rhythms of focus, calm, and emotion are translated computationally into immersive soundscapes and generative visual art10.

Such interactive integrations elevate Neurographic art from a static, retrospective reflection of the subconscious into an active, real-time mirror. The participant witnesses their internal psychological morphing projected externally on a digital canvas. This creates an immersive cybernetic feedback loop: the sight of the harmonizing, smoothing algorithm further deepens the practitioner's meditative state, which in turn feeds calmer biometric data back into the system, driving the visualization toward ultimate geometric and psychological resolution64.

Synthesis and Implications

The morphology of Neurography Art visualizations extends far beyond ink on a page. Conceived as a highly structured psychological tool by Pavel Piskarev, the fundamental rules of the practice—the unpredictable bionic line, the imperative of geometric rounding, and the structural integration of chaotic elements—serve as a definitive blueprint for visualizing neuroplasticity and emotional processing. The transformation of a drawing from a fractured, angular scribble to a cohesive, flowing network mirrors the transformation of the human brain from a state of acute stress to one of holistic integration, measurable through shifts in EEG patterns, hemispheric activation, and cognitive organization within the Expressive Therapies Continuum.

As computational capabilities have expanded, these analog transformations have found profound parallels in generative algorithms that allow the art to change, morph, and evolve autonomously. Through the application of [Figure omitted from source export] and [Figure omitted from source export] continuity mathematics, algorithms can precisely replicate the topological smoothing of internal conflicts. Fluid dynamics and noise-driven vector fields simulate the unconstrained, organic flow of psychological energy, escaping rigid Cartesian predictability. Simultaneously, self-organizing biophysical models—such as the Gray-Scott Reaction-Diffusion system and Goal-Guided Neural Cellular Automata—allow digital canvases to grow and morph with the complexity, resilience, and adaptability of living tissue.

Ultimately, by coupling these generative systems with real-time biofeedback, the boundary between the psychological and the digital dissolves entirely. The morphing Neurographic visualization ceases to be a mere representation of thought; it becomes a living, breathing cybernetic entity, acting as a real-time reflection of human consciousness in a constant, beautiful state of therapeutic flux.

Works cited

1. How Does Neurographica Work \- Step-By-Step Tutorial, https://expansionink.com/how-does-neurographica-work/

2. An Introduction to Neurographic Art \- Art by Ro, https://artbyro.com/an-introduction-to-neurographic-art/

3. Step-by-Step Neurographic Art Guide | PDF \- Scribd, https://www.scribd.com/presentation/928555494/Neurographic-Art

4. Neurographica Basic Algorithm: Rewire Your Subconscious Mind, https://expansionink.com/neurographica-basic-algorithm/

5. Introduction to Neurographica Method | PDF | Psychological Concepts, https://www.scribd.com/document/898550093/NeurographicaGuide-byAntjeHoward2025

6. Growing Isotropic Neural Cellular Automata \- MIT Press Direct, https://direct.mit.edu/isal/proceedings-pdf/isal2022/34/65/2035305/isal\_a\_00552.pdf

7. Gray-Scott Reaction-Diffusion \- LimbNET, https://limbnet.embl.es/tutorials/gray-scott/

8. amoyroud/flowfield-studio: Interactive vector-field art generator using, https://github.com/amoyroud/flowfield-studio

9. Neuropaint: An Expressive Painting Application using EEG, https://www.joanneleong.com/neuropaint

10. EEG \+ Art: How a Cognitive Neuroscientist Turns Brainwaves into, https://www.youtube.com/watch?v=JrDCAzq0fFo

11. NeuroGraphica FAQ: 12 Common Questions Before Starting, https://zulfiyageorgiou.com/neurographica-faq/

12. The NeuroGraphica® Method: History, Science & Pavel Piskarev, D, https://zulfiyageorgiou.com/neurographica-method

13. NEUROGRAPHS and COLOR SCHEMES, http://dearingcampbell.blogspot.com/2023/04/neurographs-and-color-schemes.html

14. What's the Difference Between Neurographica and Neurographic Art, https://expansionink.com/neurographica-vs-neurographic-art/

15. Neuroart vs Neurographica: What's the difference?, https://www.krystalmillscoaching.com/blog/neuroart-vs-neurographica-whats-the-difference

16. Safety precautions to follow when working with Neurographica, https://english.neurographica.com/post/safety-precautions-to-follow-when-working-with-neurographica-part-1

17. Glossary \- Neurographica, https://english.neurographica.com/glossary

18. Neurographica-Rounding the Corners \- YouTube, https://www.youtube.com/watch?v=zJIu2un7moA

19. The New and Revised 8 Steps of the Algorithm \- Neurographica, https://www.youtube.com/watch?v=oDkNsewwWE4

20. Seven Steps of a Neurographica Algorithm (DOWNLOAD a PDF doc), https://neurographic.art/2024/08/28/seven-steps-of-a-neurographica-algorithm-download-a-pdf-doc/

21. (PDF) Neuroanatomical Basis of Art Therapy and Dementia, https://www.researchgate.net/publication/394771902\_Neuroanatomical\_Basis\_of\_Art\_Therapy\_and\_Dementia\_A\_Correlative\_Review\_and\_Implications

22. New Frontiers in Art Therapy: Art Materials and Techniques, https://www.artstherapies.org/art-materials-techniques

23. Neuroanatomical Basis of Art Therapy and Dementia \- SciRP.org, https://www.scirp.org/pdf/wjns\_1390836.pdf

24. How Does An Art Therapist Build Rapport With Socially Isolated, https://digitalcommons.lesley.edu/cgi/viewcontent.cgi?article=1023\&context=expressive\_therapies\_theses

25. What's in a Color? A neuropsycholinguistic study on the effect of, https://www.researchgate.net/publication/360477766\_What's\_in\_a\_Color\_A\_neuropsycholinguistic\_study\_on\_the\_effect\_of\_colors\_on\_EEG\_brainwaves\_immediate\_emotional\_responses\_and\_English\_language\_vocabulary\_retention\_among\_Iranian\_young\_adults

26. Neuroscience of Personality: Brain Savvy Insights for All Types of, https://dokumen.pub/neuroscience-of-personality-brain-savvy-insights-for-all-types-of-people-10nbsped-0979868475-9780979868474.html

27. The Intention Experiment \- MindMeister, https://www.mindmeister.com/generic\_files/get\_file/18062041?filetype=attachment\_file

28. An asymmetrical relationship between verbal and visual thinking, https://www.researchgate.net/publication/315445999\_An\_asymmetrical\_relationship\_between\_verbal\_and\_visual\_thinking\_Converging\_evidence\_from\_behavior\_and\_fMRI

29. aljazfrancic/generative-art-gallery \- GitHub, https://github.com/aljazfrancic/generative-art-gallery

30. Pen Plotter Algorithms, Explained & Animated \- DrawingBots.com, https://drawingbots.com/algorithms/

31. G0-G3 corners, visualised: learn what "Apple corners" are, https://news.ycombinator.com/item?id=46025911

32. Create Fillets \- Vectric Docs, https://docs.vectric.com/docs/V12.5/VCarvePro/ENU/Help/form/Create%20Fillets/index.html

33. Geometry of Ships \- Letcher | PDF | Ships | Hull (Watercraft) \- Scribd, https://www.scribd.com/document/255663584/Geometry-of-Ships-Letcher

34. An Analytical Corner Rounding Algorithm With G01 Shape, https://www.researchgate.net/publication/358859431\_An\_Analytical\_Corner\_Rounding\_Algorithm\_With\_G01\_Shape\_Preserving\_for\_Five-Axis\_Computer\_Numerical\_Controlled\_Machining

35. Standard Library Documentation \- https : / / cad . onshape . com, https://cad.onshape.com/FsDoc/library.html

36. How to model aesthetic round corners like apple in solidworks or, https://www.reddit.com/r/SolidWorks/comments/1128lxo/how\_to\_model\_aesthetic\_round\_corners\_like\_apple/

37. How to model aesthetic round corners like apple in fusion or ... \- Reddit, https://www.reddit.com/r/Fusion360/comments/1128mbo/how\_to\_model\_aesthetic\_round\_corners\_like\_apple/

38. Alias Help: Multi-knots and CV multiplicity, https://download.autodesk.com/us/alias/2011help/files/WS1a9193826455f5ff4e421d7d11bf108001d-68c0.htm

39. Computer Graphics and Image Processing \- Index of files in /, ftp://ftp.math.utah.edu/pub/tex/bib/toc/cvgip.html

40. Geometric Modeling Using High-order Derivatives, https://cs.nyu.edu/media/publications/tosun\_elif.pdf

41. michaelruppe/art: Code-art projects built with p5.js and Processing, https://github.com/michaelruppe/art

42. Visual Studio \- Neorealiti, https://www.neorealiti.com/visuals

43. Creative Coding World — Where Algorithms Become Art \- The, https://wavgen.ca/art/creative-coding/

44. Generative Image Processing: Edge-Based Vector Fields, https://www.deconbatch.com/2020/11/picture-this-vector-field-method-to.html

45. Rover Takes Over Graph Coordinates Printable PDF, https://www.muthumuniayurvedariver.com/digest/uIPN6HUMZYU4/rover-takes-over-graph-coordinates

46. Articles & Tutorials | mysimulator.uk, https://www.mysimulator.uk/articles/

47. Impeccable frontend-design skill v4.0.4 — single-file ... \- GitHub Gist, https://gist.github.com/cjwaavy/76d695af109382c2d91ceb4f04eed438

48. Image-Based Graph Visualization \- webspace.science.uu.nl, https://webspace.science.uu.nl/\~telea001/uploads/PAPERS/PhD/ersoy13.pdf

49. GOAL-GUIDED NEURAL CELLULAR AUTOMATA \- OpenReview, https://openreview.net/pdf?id=rNnlEHG16b5

50. The Art of Chaos: Coding Patterns, Life, and Geometry in Cellular, https://medium.com/@sergiosear/the-art-of-chaos-coding-patterns-life-and-geometry-in-cellular-automata-67f8fcc64247

51. Reaction-Diffusion simulation using the Gray-Scott model ... \- GitHub, https://github.com/datavorous/Gray-Scott-Reaction-Diffusion-Model

52. Programming Reaction Diffusion Models \- YouTube, https://www.youtube.com/watch?v=COMvgTLTw6g

53. jonradoff/threelab \- GitHub, https://github.com/jonradoff/threelab

54. Pattern formation in the Gray-Scott reaction-diffusion equations, https://polymathic-ai.org/the\_well/datasets/gray\_scott\_reaction\_diffusion/

55. Reaction-diffusion models and Turing patterns \- Nils, https://nils-olovsson.se/articles/reaction\_diffusion\_models\_and\_turing\_patterns/

56. Pattern Formation in the Gray-Scott Reaction-Diffusion System, https://itp.uni-frankfurt.de/\~gros/StudentProjects/Applets\_2014\_GrayScott/

57. Project 3: Reaction-Diffusion, https://faculty.cc.gatech.edu/\~turk/bio\_sim/hw3.html

58. Geometry-Informed Neural Networks \- Arturs Berzins, https://arturs-berzins.github.io/GINN/

59. Geometry-Informed Neural Networks \- arXiv, https://arxiv.org/html/2402.14009v3

60. Growing Neural Cellular Automata \- Distill.pub, https://distill.pub/2020/growing-ca/

61. Neural Cellular Automata Core Copernicus node \- SideFX, https://www.sidefx.com/docs/houdini/nodes/cop/neural\_cellularautomatacore.html

62. Bio-feedback Interactive Art Installation by Alma Digital Studio., https://www.neuroartsresourcecenter.com/post/neurcosom-bio-feedback-interactive-art-installati

63. EEG Art Exploration: Beyond The Canvas \- Neuroelectrics, https://www.neuroelectrics.com/blog/eeg-art-exploration-beyond-the-canvas

64. (PDF) BioMediation: A Biofeedback Audiovisual Performance, https://www.researchgate.net/publication/280722873\_BioMediation\_A\_Biofeedback\_Audiovisual\_Performance

65. Embedded Arts Interactive Technology Helps Patients in, https://wexnermedical.osu.edu/mediaroom/pressreleaselisting/embedded-arts-interactive-technology-helps-patients-in-neurohabilitation-therapy