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First known human EEG done while under the influence of Amanita Muscaria. I experienced a very drunk feeling and a heavy body feeling like I couldn’t get up as well as seeing shadows moving (that I believe should of been stationary shadows) all during this trip.

What we are testing:

EEGs are devices that collect brainwave data from the wearer. With the advent of consumer EEGs, data collection has become significantly more accessible. Amanita muscaria is an iconic mushroom that causes intoxication primarily through two molecules: ibotenic acid and muscimol. Muscimol, in particular, is considered the active compound.
Despite its history, studies and data regarding Amanita muscaria’s effect on brainwaves are sparse. This appears to be the first human EEG recorded during intoxication with Amanita muscaria. It remains unclear precisely how the mushroom induces hallucinations, as it does not act on classical psychedelic receptors.
Most intoxicants influence specific brainwave bands, and these effects vary across drug classes, including hypnotics, GABAergic agents, dissociatives, and classical psychedelics (Siegel, 2024). This analysis examines two administrations of an Amanita extract. Collecting this data provides a starting point for understanding when Amanita muscaria kicks in, peaks, and wears off, as well as how it affects specific brainwave bands and which cognitive effects are associated with those changes

Methods:

All EEG data were collected and processed using a 2014 Muse EEG headset. The app used to transmit the data to my computer was Muse Direct. Data was saved in the proprietary Muse format and compiled with Muse Player. PSPP was used to perform normality testing (Kolmogorov-Smirnov), independent-samples t-tests, and Mann-Whitney U tests. It was also utilized to generate histograms and bar graphs.
Custom scripts generated with assistance from Google Gemini were used to process the EEG recordings, perform spectral analysis, and create topographic visualizations. All outputs were manually reviewed before analysis. Because the Muse is limited to four channels, an additional topographic mapping technique was executed via a custom program. This program largely utilized the same code as Gemini, with the primary difference being the use of 5-minute epochs instead of 5-minute intervals with 4-second snapshots. Gemini was also used to clean the data and apply Welch’s method to perform a Fast Fourier Transform (FFT). Furthermore, Gemini calculated the spectral percentages, binned the data into 5-minute intervals, and computed the absolute power using a 10log10 formula (in decibels). These values serve as the primary data points in the graphs. Additionally, Gemini calculated Cohen’s d using the means and standard deviations from PSPP’s parametric models, as well as the rank-biserial correlation (r) via the Mann-Whitney test in PSPP. Power analyses were conducted using socscistatistics.com and homepage.univie.ac.at. Preliminary analysis and fact-checking were performed by Claude (Anthropic) and ChatGPT (OpenAI) to identify any discrepancies in the data.

Aggregation method (dB-relative). Relative band power values in the workbook were derived as follows: Gemini computed absolute power in decibels (10·log₁₀ of mean linear power) for each 5-minute bin; the per-bin dB values were then averaged across all bins; finally the averaged dB values were converted back to linear power (10^(dB/10)) and normalized to percentages to yield relative power. This approach is internally consistent with the analysis pipeline as built. However, it differs from the more commonly reported method, in which each epoch or bin is first converted to relative power and those percentages are then averaged. Because the dB-to-linear back-conversion is nonlinear (Jensen’s inequality: mean of logs ≠ log of means), the two approaches yield different relative percentages — the magnitude of the discrepancy varies by band and by night. Readers should be aware that the numeric relative-power values and significance tests reported in this study reflect the dB-averaging aggregation, not the per-epoch-relative-then-average approach.
The Amanita muscaria was sourced from amentera.com (formerly mnniceethnobotanicals) and Awakening Roots, both of which are verified sellers. The products purchased included raw mushroom caps, open caps, pieces, and a 5-gram chocolate bar from Amentera (5.61% Muscimo content 280.5mgs). The raw mushrooms were decarboxylated by boiling them for three hours with lemon juice, a process designed to rapidly convert ibotenic acid into muscimol as described in Shokuhin Eiseigaku Zasshi (1993). This yielded a 0.5 g/mL (about .015 -.05 mg of muscimol per mL) tincture. It was originally prepared for another project and had been stored in a freezer for approximately three months. The 5-gram chocolate bar arrived a week before the test.
The test consisted of eating the full chocolate bar and lying down in bed. Concerned about the potency of the chocolate due to potential incomplete decarboxylation, I consumed 2 mL of the tincture (totaling 1g) approximately 40 minutes into the session. The test officially started at 11:50 PM. Alarms were set to track the onset of effects from both the chocolate and the tincture, which helped align the data. The baseline (control) was recorded about three weeks after the original test, using the exact same protocol of lying down and sleeping.

Results:

Absolute band power (Treatment 1 vs Baseline 1)

The most noticeable thing when comparing baseline 1 and treatment 1 is that there is an immediate drop in all waves (with periodic spikes of sigma) throughout the experiment, while in baseline 1, there seem to be dips (likely correlated with transitions in sleep) and then spikes of many different waves, but delta (associated with sleep), specifically, while dominating in both, was trending lower in Treatment 1 and trending higher in baseline. This is very different from most GABAergic drugs studied, and even from dissociative studies with nitrous oxide (in some cases) Foster, B. L., & Liley, D. T. (2011). Yamamura, T., Fukuda, M., & Takeya, H., et al. (1981) show similar effects. Seemingly from my data and a chart I made (Treatment 1) seems to show a kick in around 45 minutes in. This also means I most likely did not catch the peak of the effects as it did not show a clear up trend or plateau in the treatment.

Even these studies, however, usually show some fluctuation or an increase in another wave (here, sigma did fluctuate but decreased overall); delta only decreases in REM sleep, and even then gamma fluctuates. Amanita produced a noticeably higher DTABR (sedation ratio) than the baseline condition, suggesting a stronger sedative effect.

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Sideways REM Detection

Sideways REM Detection: Hypnogram

The INSPEC now detects REM perfectly on when the face is sideways, with no false positives from head movement. I slept for 2 hours before this session started, which is why the first REM episode starts so early.

Recent firmware updates that improve REM detection:

  • Each eye has its own detection region, so it still works with one eye in the pillow like in the minute above.
  • Slow head movement and head rocking from breathing patterns are also filtered out.
  • False faces found in the pillow and bedding with patterns are ignored.
  • Eye frames are compared with frames from 250 ms earlier, which works better for picking up small movements under closed eyelids.
  • A single movement no longer counts on its own. Another has to follow within 2.5 seconds, which filters out artifacts from breathing.

Firmware updates can now also be installed from the app over Bluetooth.

Download entry as Lucid Scribe Data (LSD) (3.1 MB).

Research Alignment

I got myself invited to share my findings on “Contactless REM Detection in Natural Sleep” at a sleep research lab. They were kind enough to loan me a clinical-grade EEG device: the CGX Patch, a single-channel forehead EEG of the kind used in real sleep studies.

I first connected the CGX Patch EEG and only turned the INSPEC on two minutes later, so I had to manually dial the INSPEC logs two minutes back.

At 00:11, in the minute below, I moved my eyes in Morse Code patterns to spell out SOS twice. The eye movements show up clearly on both signals, as sharp deflections on the frontal EEG and as variance spikes on the INSPEC.

Zoomed in:

From 04:26 to 04:46 the INSPEC recorded a long run of eye movements during a dream. Lined up against the CGX Patch EEG, the two track each other closely. The eye movement spikes the INSPEC pulled from the camera sit right where the EEG puts REM.

Zoomed in:

I have not run the EEG through a sleep stage classifier yet, so this is not scored agreement. But the raw eye movement signal from a camera on the nightstand and the raw EEG from a clinical device on the forehead, appear to be describing the same thing. From what I can see in Lucid Scribe, it looks very promising!

Download entry as INSPEC (LSD) (1.9 MB) | CGX Patch EEG (EDF) (183 MB) | Hour 0 Merged (LSD) (71 MB) | Hour 4 Merged (LSD) (71 MB).

Tried the INSPEC with just the LEDs for a change. The whole dream I was flying through a mountain town holding its annual midnight market: shops open midnight to 6 AM, villagers out shopping. Landed at one point on a beach and wrote LUCID in the sand. Around 30 minutes lucid total.

BlazeFace held the face lock through every position change and the artifact filter passed only the late-cycle cluster through, rejecting every earlier candidate event that didn’t meet the temporal-pattern and head-pose-stability check.

Download entry as Lucid Scribe Data (LSD) (1.6 MB).

I rolled into position at 04:05, and the INSPEC detected that I started dreaming at 04:06. The first cue was triggered at 04:08, with a total of 12 cues until the final cue woke me up. The progressive cues work great to ensure you wake up in and eventually from the dream, but I’m always left wondering what could have been… the sleep quality can be seen dropping off at 04:15:52 when the audio cue became too loud.

Download entry as Lucid Scribe Data (LSD) (1 MB).

Hide and Sleep

The INSPEC caught rapid eye movements from the most obscure angle last night, with the majority of my face hidden behind the blanket!

Download entry as Lucid Scribe Data (LSD) (1.2 MB).

Supine to Lateral

Last night’s recording shows the INSPEC catching REM sleep in two positions; first while lying on my back, and then again a few minutes later after I rolled onto my side.

The initial dream faded quickly after the audio cue, even after I tried stabilizing it by just observing, but I was able continue dreaming with lucidity after turning to my default side position and keeping still for a few minutes.

Download entry as Lucid Scribe Data (LSD) (1.6 MB).

Stabilization

The new TensorFlow update is working better than expected with the mobile app and detection works when sleeping in many new positions. The INSPEC device can now be said to leverage machine learning and artificial intelligence in combination with machine vision algorithms.

In the minutes above, the audio cue briefly destabilized the dream, but I regained my vision by observing my emotional response instead, and the dream continued with lucidity. When I heard the second cue in the dream it remained stable while I was flying!

Download entry as Lucid Scribe Data (LSD) (1.0 MB).

Mobile Record

The INSPEC device now records directly to the mobile app! In the minute above I can be seen testing a new sleep quality algorithm in my sleep.

Oh, and the INSPEC just went live and has its own site @ inspec.me! Thanks for your patience – especially if you subscribed for the production release update a decade ago, when I first set out on the journey. Would love to hear your thoughts on what it has evolved into!

Download entry as Lucid Scribe Data (LSD) (1.1 MB).

Lucid signal at 06:20
Morse code from Lucid Dream at 06:21
Here we go my first letter from Morse done in LD.
The method to apply it, is to trigger movement of the eye to be seen on EEG.

So here we have the letter F for movement (LLRL) this make EEG signal an arrow down for left and arrow up for the right movement.

This need practice, first to know Morse in real life and to apply after in dream.

Youhouuuuuuu 😉

Download entry as Lucid Scribe Data (LSD) (5 MB) or as EDF with hypnogram (15 MB).