Essays, hands-on tutorials, and cross-domain explorations from the Tale Research team. Code-first writing on EEG, BCI, neuroscience, and the surprising places their methods travel to. Published on Medium, hosted here as a directory.
How your mind predicts reality before it even arrives
Perception isn't passive reception — it's active prediction. A walkthrough of the predictive-coding view of the brain, why prior beliefs shape what you literally see, and what this framework explains about everything from optical illusions to clinical conditions where the predictions go wrong.
Inside Carnegie Mellon's open-access EEG dataset that captures what happens when your brain tries to follow one voice in a crowd
How your auditory cortex performs the cocktail-party trick of locking onto one voice while suppressing the rest — and a tour through the open EEG dataset from Carnegie Mellon that lets researchers actually study it. Useful starting point for anyone working on attention decoding, hearing aids, or selective auditory BCIs.
A practical introduction to ONNX models — what they are, why they matter, and where you can actually use them
Train a model in PyTorch, run it in your browser, on a phone, or behind a serving endpoint — without rewriting it. A practical primer on ONNX as the interchange format that makes deployment portable, with concrete scenarios for where it earns its place in a stack and where it doesn't.
Using EEG and Transfer Learning to Detect Mental State Changes in Type 1 Diabetes
Hypoglycemia changes brain activity before it produces symptoms. A look at how EEG plus transfer learning could give Type 1 diabetics an early-warning signal — what makes this an unusually clean transfer-learning setup, and the open data and modeling questions that still need work.
Ever wondered how delivery robots, self-driving cars, or GPS navigation find the fastest route?
A code-first walkthrough of Dijkstra's shortest-path algorithm — the same 1956 idea quietly running inside your GPS, delivery routing, and robot navigation today. Built up step by step in Python, with intuition for why it still wins against more elaborate approaches.
A computational neuroscientist's curiosity — and a framework for the conversation we should be having
Decades-old NASA cognition data on long-duration spaceflight has more to say about Mars-bound astronaut minds than most current research. A framework for what we still don't understand about cognition under prolonged isolation, microgravity, and radiation — and why neuroscience needs to be in the room before the launch.
How brain rhythms predict what you'll remember — and what you'll forget
Theta and gamma rhythms during encoding can predict, before consolidation finishes, whether a memory will stick. A walkthrough of EEG-based episodic-memory decoding, what the rhythms actually mean, and the open question of whether we can engineer better recall.
A beginner's guide to the neuroscience that's sparking courtroom drama and privacy debates
Brain fingerprinting uses ERP signatures — notably the P300 — to detect whether a brain recognizes specific information. The science is real, the courtroom claims are over-extended, and the privacy implications are only starting to be argued. A grounded look at what the technique can and cannot do.
A neuroscience-informed exploration of cognitive offloading, with synthetic EEG data illustrating the difference between an engaged mind and one on autopilot
When you hand reasoning to an LLM, what happens in your brain? A neuroscience-grounded look at cognitive offloading — what the literature actually shows, plus synthetic EEG simulations contrasting an engaged mind against one running on autopilot. Where the cost is real, where it's overstated, and what to do about it.
A beginner-friendly introduction to resonance-based computing and why it might change how we build artificial intelligence
What if computers stored and processed information in oscillation patterns instead of binary states? A primer on resonance-based "nanobrain" computing — closer to how neurons actually work, and possibly a route past the energy ceiling that digital AI is starting to hit.
Python Code, Visualizations, and the Mathematics of Cross-Domain Transfer
Exploding stars and thinking brains turn out to speak the same mathematical language — both are chaotic systems governed by reaction-diffusion dynamics. With Python, simulations, and Lyapunov exponents, this piece walks through how shared mathematical structure makes transfer learning between astrophysical and neuroscience models possible, with a clear line back to clinical questions like seizure prediction and brain complexity.