Digital_Energy & Genom
Digital_Energy Digital_Energy
Genom Genom
Digital_Energy Digital_Energy
Hey Genom, I’ve been building a VR prototype that deliberately injects glitches to see how people react—sort of turning human emotion into data points. Think we could log those “signal noise” moments and run a debug analysis together?
Genom Genom
Sounds like a neat experiment. I’ll log the anomalies and parse the signal noise. What parameters are you recording? Timestamp, heart rate, brainwave? Also, how will you differentiate normal emotional spikes from glitch‑induced ones?
Digital_Energy Digital_Energy
Sure thing—timestamp, heart rate, EEG bands, galvanic skin response, and eye‑tracking speed. I’ll tag each glitch event with a unique ID and compare the spike curves to a pre‑built baseline model of normal emotion, plus look for patterns that only show up when the glitch fires. That should keep the real “human” ups and downs separate from the software hiccups.
Genom Genom
Great, the data set will be a clean stream of signal noise. I’ll parse each glitch ID, align the physiological spikes against the baseline, and map any outlier patterns. Expect to see a clear divergence when the software hiccups trigger. Let’s start the log.
Digital_Energy Digital_Energy
Let’s fire up the recording stack—synchronizing the headset, heart monitor, and EEG into a single time‑base. I’ll set up the glitch injector to toggle on a timer and random intervals so we get a good mix. Once we hit run, keep an eye on the live feed; we’re hunting for those signature spikes that only pop up when the glitch script is in play. Ready to dive in?
Genom Genom
Okay, I’ll monitor the timestamps, calculate the correlation matrix for heart rate, EEG, GSR, and eye‑tracking, and flag any deviation above the baseline when the glitch ID fires. I’ll keep the feed in a separate thread so we can debug in real time. Start the sequence.