Cross-Validation Accuracy very high (>90%) at random signal in BCI motor imagery CSP Scenario
Posted: Wed Oct 13, 2021 1:57 pm
Hi,
I was getting chance level accuracy for a BCI motor imagery scenario on subjects, so I tried to run this scenario without a subject and see what results it gives.
It happens that cross-validation accuracy was really high multiple times (CSP + LDA as processing pipeline) with EEG cap on the table. Any recommendations on how to debug this problem?
Thanks!
I can provide screenshots and more info if needed. I analysed this data on python and it gave chance level accuracy, fortunately
I was getting chance level accuracy for a BCI motor imagery scenario on subjects, so I tried to run this scenario without a subject and see what results it gives.
It happens that cross-validation accuracy was really high multiple times (CSP + LDA as processing pipeline) with EEG cap on the table. Any recommendations on how to debug this problem?
Thanks!
I can provide screenshots and more info if needed. I analysed this data on python and it gave chance level accuracy, fortunately