GPT & Brainwave Entrainment [Help]

ChatGPT
1

Hello all,

Since a few weeks, I use GPT to create sessions for my AVS device—you could picture binaural beats with pulsating lights. I’m trying to design a protocol that causes long-term entrainment in the Alpha, Theta, and Delta frequency bands. The main obstacle in this is the habituation factor that plays a role when being exposed to the same sessions for multiple days. GPT advised me to alter the settings between the sessions. I made a prompt/script whereby GPT creates 22 sessions. One of the parameters is that the frequency should fluctuate between 1.3 Hz and 1.8 Hz, staying at the new frequency between 120 and 240 seconds.

However, GPT continues to make errors—for instance, jumping from 8.3 Hz to 5.7 Hz, which breaks the 1.3–1.8 Hz step rule. I’ve flagged this several times, and while it acknowledges the problem, the mistakes keep happening. I spent my entire evening yesterday trying to correct them. I tested this using GPT‑3.5, GPT‑4o, and even GPT‑4o with the “think longer” tool enabled, but the issue still persists.

I know there are a bunch of bright minds on this forum who have more understanding of GPT than I do, so I was wondering if somebody can guide me in the right direction.

SCRIPT:

Create 22 Kasina Basic Sessions targeting [Target Frequency], following this structure:

  • Name sessions by brainwave band, target frequency (Hz), “Session,” and number.

Example: Theta 6 Hz Session 1, Alpha 8 Hz Session 2, Delta 2 Hz Session 1.

  • Vary order, timing, frequencies, pitch, and colors to avoid exact repetition. Alternate pitch or color schemes. Slightly vary ramp lengths and starting frequencies.

  • Segment 1: Start near 14.5 Hz (optional intro), duration 0.01–3.5 min.

  • Segment 2: Ramp down smoothly from ~14.5 Hz to 8 Hz between 540 -650 seconds Label as ramp.

  • Segments 3–7 (or more): Hover around 8 Hz for 10–15 min total, alternating strictly between frequencies differing by exactly 1.3–1.8 Hz per step.

Each hover segment lasts 2–4 min. No frequency changes outside 1.3–1.8 Hz allowed.

  • Remaining segments: Ramp down smoothly to [Target Frequency] (no step restriction), then fluctuate ±1.3–1.8 Hz strictly alternating above and below target for 40.4%–44.44% of total session time.

Segments duration between 120 and 240 seconds.

  • Total session length flexible to fit above 40% time-at-target rule.

  • All hover segments last 2–4 min.

  • Same number of segments across all sessions

  • Specify unique L & R Pitch (equal), 100–160 Hz.

  • List all segment durations explicitly in seconds.

  • Include beat frequency sequence summary.

  • Specify total time at [Target Frequency] in minutes and % of session.

Use these RGB color levels for variation (from full white):

  1. R100% G100% B100%

  2. R100% G90% B90%

  3. R90% G100% B90%

  4. R90% G90% B100%

  5. R100% G80% B80%

  6. R80% G100% B80%

  7. R80% G80% B100%

  8. R100% G85% B95%

  9. R95% G100% B85%

  10. R85% G95% B100%

  11. R100% G95% B85%

  12. R85% G100% B95%

  13. R95% G85% B100%

  14. R100% G92% B88%

  15. R88% G100% B92%

  16. R92% G88% B100%

  17. R98% G90% B93%

  18. R93% G98% B90%

  19. R90% G93% B98%

  20. R97% G89% B91%

  21. R91% G97% B89%

  22. R89% G91% B97%

Deliver sessions like this:

Example:

6 Hz Session 1

Total Time: 4200 s (70.0 min)

Color: R100% G100% B100%

L&R Pitch: 110 Hz

Beat Line:

14.5 Hz – Ramp 8 Hz – 8 Hz – 7.2 Hz – 9.0 Hz – 6.5 Hz – 10.5 Hz – 7.8 Hz – 6.0 Hz – 7.2 Hz – 5.1 Hz – 6.9 Hz – 6.2 Hz – 5.8 Hz – 6.5 Hz – 5.5 Hz – 6.8 Hz – 5.9 Hz – 6.2 Hz – 6.4 Hz – 5.7 Hz – 6.0 Hz

Segment Durations:

120 s – (Ramp: 720 s) – 120 s – 180 s – 150 s – 180 s – 150 s – 150 s – 180 s – 180 s – 180 s – 150 s – 150 s – 180 s – 150 s – 150 s – 180 s – 180 s – 150 s – 180 s – 150 s – 180 s

Time at 6 Hz: 2640 s (44.0 min) (62.9%)

2

Hi!
The intent of your question is hard to find in between all the brainwaves but let me give it a shot to be helpful by allowing 4.5 a crack at it. The model generated reply captures what I believe is the issue but take it with a grain of salt.

The problem you’re experiencing arises because GPT alone isn’t consistently accurate in enforcing numerical constraints strictly via text. GPT comprehends your rule conceptually, but without computational checks, it occasionally miscalculates frequency steps.

The solution is straightforward: prompt GPT to use computational tools, particularly Python / Code Interpreter, to explicitly calculate and verify each frequency step.

Here’s how you could adjust your prompt:

Ask GPT explicitly to generate short Python code snippets to select the next frequency step within your specified constraint (1.3–1.8 Hz).

Have GPT provide frequencies and durations as structured JSON or a Python list for computational verification.

Example Python snippet:

import numpy as np

current_frequency = 8.3
frequency_step = np.random.uniform(1.3, 1.8)
next_frequency = current_frequency - frequency_step  # or +frequency_step
print(f"Next Frequency: {next_frequency:.1f} Hz")

This approach ensures compliance with your numerical constraints every time.

1 Like
3

I asked GPT to create a Python script based on my earlier prompt and combine it with the script you provided. When I requested a 5Hz session, the frequencies in the target block still didn’t follow the 1.3Hz–1.8Hz step rule.

I pointed this out to GPT and asked for the script to be corrected, but it keeps making the same mistakes.

14.5 → 8.0 (ramp, OK)
8.0 → 6.6 = 1.4 Hz :check_mark:
6.6 → 8.1 = 1.5 Hz :check_mark:
8.1 → 6.3 = 1.8 Hz :check_mark:
6.3 → 8.0 = 1.7 Hz :check_mark:
8.0 → 6.5 = 1.5 Hz :check_mark:
6.5 → 5.0 (ramp, OK)
5.0 → 6.7 = 1.7 Hz :check_mark:
6.7 → 3.8 = 2.9 Hz ✘ (too big)
3.8 → 5.1 = 1.3 Hz :check_mark:
5.1 → 3.2 = 1.9 Hz ✘ (slightly over)
3.2 → 5.8 = 2.6 Hz ✘
5.8 → 3.7 = 2.1 Hz ✘
3.7 → 5.5 = 1.8 Hz :check_mark:
5.5 → 3.5 = 2.0 Hz ✘

import numpy as np
import random

def generate_hover_block(center_freq: float, segments: int):
    freqs = [round(center_freq, 1)]
    direction = -1
    for _ in range(segments - 1):
        step = np.random.uniform(1.3, 1.8)
        next_freq = freqs[-1] + direction * step
        next_freq = max(next_freq, 0.1)
        next_freq = round(next_freq, 1)
        freqs.append(next_freq)
        direction *= -1
    return freqs

def generate_target_fluctuations(target_freq: float, segment_count: int):
    freqs = [round(target_freq, 1)]
    direction = 1
    for _ in range(segment_count - 1):
        step = np.random.uniform(1.3, 1.8)
        next_freq = freqs[-1] + direction * step
        if abs(next_freq - target_freq) > 3.6:
            next_freq = target_freq + direction * step
        next_freq = max(next_freq, 0.1)
        next_freq = round(next_freq, 1)
        freqs.append(next_freq)
        direction *= -1
    return freqs

def generate_session(target_freq: float, session_num: int, total_time_sec: int):
    session = {}

    # Naming convention
    session["name"] = f"Theta {target_freq} Hz Session {session_num}"

    # Color override for Session 1
    if session_num == 1:
        session["color"] = "R100% G100% B100%"
    else:
        session["color"] = random.choice([
            "R100% G100% B100%", "R100% G90% B90%", "R90% G100% B90%", "R90% G90% B100%",
            "R100% G80% B80%", "R80% G100% B80%", "R80% G80% B100%", "R100% G85% B95%",
            "R95% G100% B85%", "R85% G95% B100%", "R100% G95% B85%", "R85% G100% B95%",
            "R95% G85% B100%", "R100% G92% B88%", "R88% G100% B92%", "R92% G88% B100%",
            "R98% G90% B93%", "R93% G98% B90%", "R90% G93% B98%", "R97% G89% B91%",
            "R91% G97% B89%", "R89% G91% B97%"
        ])

    session["pitch"] = random.randint(100, 160)
    session["beat_line"] = []
    session["durations"] = []

    # Intro
    session["beat_line"].append(round(np.random.uniform(14.3, 14.6), 1))
    session["durations"].append(random.randint(60, 180))

    # Ramp to 8