EEG features, especially entropy, from code comprehension and resting-state data classify programmer expertise levels with 91.83% binary and 78.15% multi-class accuracy in stratified cross-validation.
Use of common average reference and large-laplacian spatial-filters enhances eeg signal-to-noise ratios in in- trinsic sensorimotor activity,
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Neural Signatures of Programming Expertise: Classifying Programmer Skill Levels Using EEG Data
EEG features, especially entropy, from code comprehension and resting-state data classify programmer expertise levels with 91.83% binary and 78.15% multi-class accuracy in stratified cross-validation.