2025
Human-Machine Interaction | BioDesign| Affective Computing
Role: Concept and System, Sensor and Singal Processing, Electronic and Mechanical Engineer, Visual and Motion Design
Algorithmic Morphogenesis explores how human memories, cognition, emotions might be translated into the growth language of living systems.By bridging neuroscience, biological computation, and Human Computure 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.
MATERIALS
PHYSICAL
DIGITAL
DATA
SYSTEMS
SOFTWARE
HARDWARE
FABRICATION
Algea, Acrylic, Aluminium, Water
Audio input, video input
Realtime Streams, Python, MatLab
Processing, TouchDesigner, MindMonitor
Arduini Uno, Muse 2, Conductive Gel
CNC, LaserCut, 3D Print, Band Saw, CAD
DESIGN AND PRODUCTION PROCESS
Encoding Process
- Physiology: 4-channel EEG signal from Muse 2 and MindMonitor
- Audio: Natural Language input of 5-min verbal memory recall
Computation: Signal processing and affective computing
- Raw EEG data denoise and filtering to isolate frequency bands
- Indentify 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 control
- built 2-axis robotic mechanism, joint rotates over time (t), link rotate based on the Memory Index (MI), LED light intensity changes based on arousal (AI).
Result: The 2-axis robot plot participants cognition data into petri dish in the form of light, based on biologica phototropism, guiding algea growth and morphing over time.
Computation process
Fabrication Process
Special Thanks to Harvard John A. Paulson School of Engineering and Applied Sciences Wet Lab
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. https://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 the Augmented Humans International Conference (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.