Sounds like a solid plan, but remember that EEG is a noisy beast. If you just throw a black‑box model at it, you’ll get patterns that look impressive on paper but don’t generalize to a real clinic. Start with a clear hypothesis, collect a balanced dataset, and use cross‑validation to guard against overfitting. Keep the feature set interpretable—clinicians need to trust what the algorithm is flagging. And if you find a “predictive” signal that vanishes on a second batch, don’t panic; that’s the data telling you it’s not a real signal, just a statistical fluke. Also, keep in mind that a good warning system must be actionable—if you predict a flare‑up but there’s nothing a patient can do before it happens, the model has zero clinical value. In short, the science can be elegant, but the utility hinges on rigorous validation and real‑world relevance.