5 minute read

I’ve been thinking about a strange gap in how we approach AI creativity.

Large language models have gotten remarkably good at generating text, code, and ideas. But when we want them to be more “creative,” the standard approach is to crank up the temperature parameter. Higher temperature means more randomness in token sampling, which produces more diverse outputs. The problem is that this randomness is just noise. Push it too far and you get incoherent word salad.

This is interesting because human creativity works completely differently. When we enter altered states of consciousness, whether through meditation, flow states, or other means, something specific happens in the brain: neural entropy increases, default mode network activity shifts, and global connectivity patterns change. The result is divergent thinking and novel associations, but grounded in lived experience rather than statistical noise.

So I started wondering: what if we could use real-time brain signals to dynamically steer LLM generation? Instead of simulating creativity through temperature hacking, we could amplify authentic cognitive states.

The Temperature Problem

When researchers study LLM creativity, they consistently find the same pattern. Temperature does boost novelty, but there’s a sharp trade-off with coherence. A study by Peeperkorn et al. in 2024 found that higher temperatures modestly increase divergent outputs, but the effect is nuanced and task-dependent. Temperature is a blunt dial, not a creativity enhancer.

The fundamental issue is that LLM randomness has no grounding. It’s just noise injected into probability distributions. Human altered states work differently. The increased entropy comes with preserved meta-cognitive awareness. You’re making strange connections, but you know you’re making them. The experience has structure.

What’s Happening in the Brain During Altered States

The neuroscience here is fascinating. During meditation, flow states, and other altered states, researchers observe consistent changes:

Alpha and theta wave ratios shift in characteristic patterns. Gamma bursts correlate with insight moments. The default mode network, which normally maintains our sense of separate self, becomes less dominant. Global brain connectivity increases.

Robin Carhart-Harris’s “entropic brain” hypothesis proposes that consciousness exists on a spectrum of entropy. Normal waking awareness sits in a middle zone. Sleep and focused states have lower entropy. Altered states push toward higher entropy, which enables novel pattern recognition but can also produce fragmentation if pushed too far.

The key insight is that these states produce genuine cognitive flexibility, not just random noise. And we can measure them.

Current Attempts at “Getting LLMs High”

There’s already work happening in this space, though mostly through simulation.

A project called Pharmaicy trains modules on trip reports scraped from forums and subreddits. These modules modify LLM behavior to produce more free-associative, divergent outputs. It’s clever, and it works to an extent. WIRED covered it in 2025, noting that people are paying to make their chatbots behave as if under altered states.

Academic work by Girn et al. in 2024 used LLMs to profile consciousness changes from first-person reports of altered states. The models could discriminate between different substance effects with reasonable accuracy.

But all of this is indirect mimicry. The LLM is trained on descriptions of altered states, not connected to actual brain dynamics. It’s a proxy built from text, prone to artifacts and lacking real-time authenticity.

The Idea: Real-Time BCI-Driven Generation

Here’s what I’ve been thinking about. What if we closed the loop?

Modern EEG headsets are getting cheaper and more accurate. Consumer-grade devices can already pick up alpha/theta ratios, detect meditation states, and identify attention patterns. The signal isn’t perfect, but it’s increasingly usable.

Imagine connecting that to LLM generation parameters in real-time:

You enter a meditation or focused altered state. EEG detects the characteristic neural signatures. Those signals map to generation parameters: temperature, top-p sampling, attention weights, maybe even prompting strategies. The model generates output that reflects your actual cognitive state. You experience the amplified output, which potentially modulates your state further.

This creates a feedback loop. The AI becomes a prosthetic extension of your cognition, not a simulation of someone else’s experience.

Precedents in Generative Art

Refik Anadol has done impressive work in this direction. His “Melting Memories” installation used EEG data from Alzheimer’s research to drive generative visuals. “Unsupervised” at MoMA generated visuals from the museum’s collection metadata, though without direct neural input.

These projects show that brain-driven generative systems can produce compelling output. But they’re mostly one-way: brain signals drive art generation, but there’s no closed loop where the output influences the user’s state.

The missing piece is interactivity. A system where the human and AI are genuinely co-regulating, where the amplified output becomes part of the experience that shapes subsequent generation.

Why This Matters

The point isn’t to make weird art, though that’s fine too. It’s about what kind of tool AI creativity assistance becomes.

Right now, LLM creativity is detached from embodiment. You prompt, it generates, you evaluate. The model has no access to your actual cognitive state. It can’t tell if you’re in a focused flow state or distracted and tired. It generates the same way regardless.

A BCI-mediated system could be genuinely responsive. When you’re in a state that supports divergent thinking, the model could amplify that. When you’re more focused and convergent, it could adjust accordingly. The AI becomes a tool that extends your current cognitive mode rather than ignoring it.

There are also potential therapeutic applications. Guided insight sessions where the AI helps externalize and explore thoughts that arise during meditation or other contemplative practices. The model as a mirror for consciousness exploration.

Challenges

This is not easy to build.

EEG signals are noisy, especially from consumer devices. Extracting reliable cognitive state markers requires careful signal processing and individual calibration. What works for one person’s brain may not generalize.

There are ethical questions about altered state induction. Even with meditation rather than pharmacological approaches, nudging people into altered states via feedback loops raises concerns about consent and unintended effects.

Validation is tricky. How do you measure whether BCI-driven generation actually produces more creative or valuable outputs? Creativity metrics are notoriously slippery.

And there’s the basic engineering challenge of real-time integration: low-latency signal processing, mapping functions that feel natural rather than jarring, and interfaces that don’t break the altered state you’re trying to amplify.

Where This Goes

I don’t think we’re far from working prototypes. The components exist: decent consumer EEG, fast inference from local or API-based models, and basic understanding of which neural markers correlate with creative states.

The first versions will be crude. Map alpha/theta ratio to temperature, maybe add some gamma-triggered prompt injection. See what happens. Iterate from there.

The longer-term vision is AI as a genuine cognitive prosthetic. Not replacing human creativity, but extending it. Making the strange connections we make in altered states more accessible, more explorable, more shareable.

Pharmaicy and similar projects prove the concept: altered-state inspiration unlocks interesting LLM behaviors. The next step is making that connection direct rather than simulated.

If you’re working on BCI, generative AI, or the intersection of consciousness research and computing, I’d be curious to compare notes.

This post started as a tweet thread exploring the gap between LLM creativity simulation and genuine altered-state cognition. The core question: can we connect them through real-time brain interfaces?

Find me at @hammadtariq