Biodesign hero

2025

Algorithmic Morphogenesis

Human–Machine Interaction · BioDesign
Affective Computing

Role: Concept Design, System Design, Biosensing and Signal Processing, Mechanical Engineering and Fabrication

Inscribing neurological data (EEG) into microorganisms for human–bio symbiosis.

Algorithmic Morphogenesis explores how human memories, cognition, and emotions might be translated into the growth language of living systems. By bridging neuroscience, biological computation, and Human–Computer Interaction Design, this project proposes a hybrid interface where memory is no longer stored as digital data but grown as living structure. The project reimagines memory storage as an ecological process, inviting audiences to reflect on the relationships between perception, technology, and the living world.

Physical Materials

Algae Acrylic Aluminium Water

Fabrication

CNC Laser Cut 3D Print Band Saw CAD

Software

Python MatLab Processing TouchDesigner MindMonitor

Hardware

Arduino Uno Muse 2 EEG Conductive Gel

Design & Production Process

Encoding Process

System diagram — encoding process

System flow diagram, sensing to actuation

System Input: Sensing + Multi-modality Data

  • Physiology: 4-channel EEG signal from Muse 2 and MindMonitor
  • Audio: Natural language input of 5-min verbal memory recall

Computation: Signal Processing & Affective Computing

  • Raw EEG data denoise and filtering to isolate frequency bands
  • Identify key signals for memories and emotions: Arousal Index (β + γ) / α
  • Memory Index (MI): total_power = mean(1–40 Hz total spectrum)

System Output: Actuation Control

  • Map time(t), Arousal Index (AI), Memory Index (MI) into angle control (θ₂) and light intensity
  • 2-axis robotic mechanism: joint rotates over time (t), link rotates per MI, LED intensity changes per AI

Result

The 2-axis robot plots participants' cognition data into a petri dish in the form of light. Based on biological phototropism, this guides algae growth and morphing over time.

Result 04 Result 05 User test

Technical

Computation Process

EEG signal 01 EEG signal 02 EEG signal 05

Output

Decoding Process

Decoding process Decoding detail
Result 06 Result 07

Making

Fabrication Process

Process overview Process 01 Process 02 Process 03 Process 04 Process 05 Process 06 Process 07 Process 08 Process 09 Process 10 Process 11 Process 12 Detail Detail 35 Detail 36 Result 03

CAD & Technical Drawings

CAD 03 CAD 04 CAD 06 CAD 07 CAD 08 CAD 10

Special thanks to the Harvard John A. Paulson School of Engineering and Applied Sciences Wet Lab for supporting this research.

Results

Final outcome 01 Final outcome 02

Bibliography

Picard, R. W. (2000). Affective computing. MIT Press.

Farahi, B., Zhang, H., Kim, S., Mutis, S., Wang, Y., & Dai, C. (2025). Gaze to the Stars: AI, storytelling and public art. NeurIPS 2025 Creative AI Track. openreview.net/forum?id=Eh84s4DiSC

Seow, O., Honnet, C., Perrault, S., & Ishii, H. (2022). Pudica: A framework for designing augmented human–flora interaction. Proceedings of AHs 2022. ACM. https://doi.org/10.1145/3519391.3519394

Coan, J. A., & Allen, J. J. (2004). The family of frontal EEG asymmetry: A review. Biological Psychology, 67(1–2), 7–49. https://doi.org/10.1016/j.biopsycho.2004.03.002

Harmon-Jones, E., Gable, P., & Peterson, C. (2010). The role of frontal alpha asymmetry in emotion. Biological Psychology, 84(3), 451–462. https://doi.org/10.1016/j.biopsycho.2009.09.007

Davidson, R. J. (1992). Anterior EEG asymmetry and the nature of emotion. Psychological Science, 3(1), 23–27. https://doi.org/10.1111/j.1467-9280.1992.tb00254.x

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