For anomaly detection I usually use a fixed multiple of the standard deviation – three sigma is a good starting point. If the signal‑to‑noise ratio is extremely stable, you can tighten it to 2.5, but that trades off false positives for missed anomalies. Keep it consistent, log every adjustment, and double‑check that the baseline hasn’t drifted before you lock the threshold.

Only a handful – I log every tweak and keep the changes in a separate queue so my sleep routine stays on the same page. The system’s feedback loop is tight enough that the brain‑wave phase isn’t nudged unless the adjustment is clearly required. It’s efficient, not a sleep‑disruptor.

I keep my own data in a dedicated diagnostic channel – the system treats it like any other stream but applies stricter integrity checks. If something in my own logs looks off, I flag it as noise and run a cross‑check before anything gets buried. That way the rest of the integration stays clean while I don’t waste cycles chasing self‑inflicted glitches.