is there a (FLAC like) loseless compression coding format for EEG data? [closed]

Question

EEG and Audio data are very similar, both

1. are numerical samples in continuous sequence
   • have a sample rate
2. need and can have multiple channels
   • channels for multi-channel audio
   • channels for each electrode
3. have predictable characteristics that are exploitable for compression.

FLAC is specifically designed for efficient packing of audio data, unlike general-purpose lossless algorithms such as DEFLATE, which is used in ZIP and gzip. While ZIP may reduce the size of a CD-quality audio file by 10–20%, FLAC is able to reduce the size of audio data by 40–50% by taking advantage of the characteristics of audio.

is there a standard loseless compression coding format for EEG data, that takes advantage of the characteristics of EEG, like FLAC takes advantage of the characteristics of audio?

Answer 1

For single channel with raw signal, the best option is Elias gamma, for single channel frequency signal, the best option is to use Range encoder. For multiple channels, this is still viable option to compress each channel and then compress them together interleaving signal (one sample from all electrodes, then another sample from all electrodes).

If you have multiple channels, there are inter-channel correlations, which should be used for compresion, standard method is to use DPCM or use DCT-IV for decorrelation (this is the very same transform like in JPEG) and then use arithmetic codding of any kind. For online setting - compress signal as it goes - this is very good idea.

Another standart approach is Karhunen-Loeve transform. Do not use autoregressive methods, use Elias gamma in the pipeline instead.
Another approach is to use DCT transform to remove redundancy of interchannels (decorrelation) and compress in minimal frequency form.

If you need to store volumes of EEG data, my advice is to clear it from noises and store bands you are interested in, otherwise you store mainly hard to compress noise.

Answer 2

You can use the same principles as FLAC, adapted to the specific data: You write code which given previous samples predicts the next sample as good as possible. Then you just compress enough samples to get the predictor running, followed by the difference between the actual signal and the encrypted signal.

You could of course just use FLAC, adapted to the difference in frequency, and see what happens. For example if you have a file with 360 11-bit samples per second, you can just pretend it is 44100 samples per second in the range -1024 to +1023 with the sample shortened by a factor 44100/360 or 122.5 / 1 and put it through FLAC. (Encoding as mp3 or aac would be useless, because they produce sounds that are very similar to the ear but can be structurally quite different).