Exploring the role of dry-EEG and machine learning methods to profile mild cognitive impairment

Event related potential technique was applied to the EEG and features of interest were extracted for further analysis using supervised machine learning methods. <br><br>Analysis and Results:<br>Analysis was carried out on a group level and individual level. The latter involved training algorithms on a range of data models to identify patterns in the data with feature combinations predicting group membership of individuals with accuracy.

Randomisation tests confirmed that there no differences found at a group level when considering the groups’ performance during episodic memory, language, executive function (working memory) or attention tasks. <br><br>For analysis at an individual level, improved model accuracies with the addition of ERP features to neuropsychological features were reported for the language and attention task. A combination of features, namely an ERP feature and neuropsychological feature were selected by the algorithms as features contributing to the best model.<br><br>Discussion:<br>The four EEG tasks in this study were designed to probe cognitive function known to deteriorate in individuals with MCI.

Considering the trajectory of cognitive decline is essential when interpreting differences, or indeed no differences between the groups. However, a difference between the groups with respect to episodic memory would have been expected since it is the cognitive function upon which their diagnosis is based: this finding most likely points to the low power of the study. Feature selection was of interest across the tasks and provided insight into potential predictors of progression to AD relative to the cognitive function.

However, due to the heterogeneity of MCI, it is with caution that these features are discussed since it is unknown who within the MCI group would progress to AD. <br><br>Conclusion:<br>The study was a proof of concept exploring cognitive function using a wireless dry-EEG headset outside a laboratory or hospital setting. The benefits of this system included a quick and easy set-up without compromising the quality of data.

This approach could provide an efficient and affordable way to extend research into more accessible locations such as health centres and at home.<br>