[Eeglablist] How much RAM?

[Cedric Cannard]

Hi Georges,

Are you experiencing crashes? if so, doing what?

In theory, running low on RAM should just make things slower as the system begins using virtual memory (i.e., swap space on disk). However, in practice, MATLAB and EEGLAB can run out of memory and crash for several reasons: if large contiguous memory blocks are required, duplicate structure copies (especially if created across parallel workers), fallback to disk-based memory not necessarily handled efficiently. 16 GB of RAM is often sufficient for most basic EEGLAB uses—such as preprocessing, standard ICA (e.g., Infomax, PICARD), ERP analysis, spectral analysis, and standard parametric or even robust nonparametric statistics (permutation/bootstrap) on typical datasets. However, if you start running out of memory, try optimizing MATLAB first before upgrading hardware. Clear unused variables with < clearvars -except EEG >, disable EEGLAB’s undo history using eeglab('memory','off'), convert large arrays to single precision when double is unnecessary, avoid duplicating large variables, and process long recordings in smaller segments. In parallel computing, reduce the number of workers to lower memory overhead.

If memory issues persist, upgrading to 32 or 64 GB RAM is a good next step, especially for advanced tasks like AMICA, source reconstruction, connectivity analysis, or machine learning on large EEG datasets. For the CPU, at least a modern 6-core processor (e.g., Intel i7 or AMD Ryzen 5/7, 12th gen or newer) with high single-thread performance. More cores help with parallel processing, but ensure sufficient RAM per core for best performance.

Hope this helps,


Cedric



On Thursday, July 17th, 2025 at 5:20 PM, Ghorayeb, George Ryan via eeglablist <eeglablist at sccn.ucsd.edu> wrote:

> If I run out of memory, how much RAM should I usually add for eeglab to perform well?
>
> Best wishes,
> George
>
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> George Ryan Ghorayeb
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