Hello.
i have obtained motor imagery eeg data using virtual goggles and a monitor screen. my goal is to prove the effectiveness of the virtual goggles or the signals. how should i analyze the signal for this? which feature gives this information? (except accuracy)
Hi,
The framing of you question seems a bit vague. What is your hypothesis? Do you expect to find differences in the signals you have recorded depending on the condition (goggles vs screen)? What kind of differences (e.g. in signal power, in spatial patterns, something else)?
How to assess the differences depends on your question. For example, if you were interested in classification accuracy (which I understand you are not) you could try some simple machine learning algorithms in order to distinguish between the two conditions.
This example combines a bit of spatial topographies and classification. It might be helpful to get you going: Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP) — MNE 1.6.0 documentation.
Hope this helps.
my hypothesis: to prove that virtual reality goggles are more effective on mi signals. for this I found the accuracy rate with a deep learning classifier. but I only want to plot numerical values or graphs that support the accuracy rate.(example psd…)… what can come instead?