Cognitive task difficulty analysis using EEG and data mining

Existing research on task difficulty and program comprehension mainly concentrate on brain areas related to attention and meditation. In this research, an in-depth analysis of Task Difficulty Level (TDL) for program comprehension is proposed with features extracted from different areas of the brain. Two levels of task difficulty were analysed: easy and difficult. Eight students were asked to solve nine Java programs of different difficulty level and the subject's cognitive load was recorded using EEG. Four different feature extraction methods were used for analysis - Energy, Frequency ratio, Event Related De-Synchronization (ERD) and Asymmetry ratio and Naïve Bayes classifier was used for classifying different TDL. The results indicated that the recorded EEG signals could reflect TDL for program comprehension tasks for predicting easy and difficult tasks. The classifier predicted task difficulty (easy/difficult) of a new task with an overall correctly classified accuracy of 76.55% with a precision of 80.03% and recall of 76.66%. This in-depth analysis of TDL for program comprehension tasks using EEG could support in developing future generation learning tools.