Skip to content

Main content
Bookmark this: tool.io/neuroscience/median-frequency-eeg

Median Frequency EEG Calculator

Find the first increasing nonnegative EEG frequency bin containing at least half the supplied nonnegative bin-integrated power. Exact half-power ties choose the lower bin; no interpolation or clinical interpretation.

Loading tool…

Description

Scale powers by their maximum, accumulate, and return the first bin reaching at least half the total.

A power spectrum distributes signal power across frequency bins. A cumulative-power statistic depends on the analyzed frequency range and the supplied bin representation; it is a summary of those inputs rather than a diagnosis.

Scale powers by their maximum, accumulate, and return the first bin reaching at least half the total.

When to use this tool

Use Median Frequency EEG Calculator when you need a cumulative-power summary of already computed frequency-bin powers.

How to use the tool and how it works

  1. Enter frequenciesHz, binPowers. Keep parallel arrays aligned. Use the units and conventions below.
  2. Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
  3. Read medianFrequencyHz, binIndex. Use the output convention below.

Require aligned nonempty strictly increasing nonnegative frequencies and nonnegative binPowers with a positive maximum. Scale powers by their maximum, accumulate, and return the first bin reaching at least half the total.1

Median frequency summarizes where cumulative spectral power reaches half the analyzed total. Its interpretation depends on the analyzed frequency range and spectral processing; this tool only selects from supplied frequency-bin powers.2

Input fieldMeaning and unitsAccepted input
frequenciesHzStrictly increasing, nonnegative frequency-bin coordinates in Hz, aligned with binPowers.Required. Array of finite number; maximum items 256.
binPowersNonnegative power already integrated for each bin, on one common scale; not power density values awaiting bin-width integration. At least one must be positive.Required. Array of finite number; maximum items 256.
Output fieldInterpretationReturned shape
medianFrequencyHzFrequency of the first supplied bin reaching at least half the total bin power; no interpolation.Required. Finite number.
binIndexZero-based index of that bin; an exact half-power boundary selects the earlier bin.Required. Safe integer.
json
{
  "input": {
    "frequenciesHz": [
      1,
      2,
      4,
      8
    ],
    "binPowers": [
      1,
      2,
      5,
      2
    ]
  },
  "output": {
    "medianFrequencyHz": 4,
    "binIndex": 2
  }
}

Limitations and assumptions

  • Input powers must already be per-bin contributions; unequal-width spectral densities need integration elsewhere. No interpolation, EEG preprocessing, frequency-band selection, diagnosis, or clinical interpretation is performed.
  • Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
  • Numeric calculations use binary floating point unless exact decimal-integer arithmetic is explicitly stated. No hidden measurement tolerance or decimal rounding is added; nonfinite or out-of-contract results are rejected. Numeric signed-zero results are exported as zero.

Alternative or Complementary methods

Inspect the signal processing, frequency range, artifact handling, and professional interpretation separately.

References

  1. Calculation and input contract: neuroscience/median-frequency-eeg — tool.io

  2. Clinical Electroencephalography for Anesthesiologists Part I: Background and Basic Signatures — Patrick L. Purdon, Aaron Sampson, Kara J. Pavone, and Emery N. Brown / Anesthesiology (2015)

Similar or alternative tools

Support Tool.io