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Objectives: To find out which Quantitative EEG (QEEG) parameters could best distinguish patients with Parkinson's disease (PD) with and without Mild Cognitive Impairment from healthy individuals and to find an optimal method for feature selection.Background: Certain QEEG parameters have been seen to be associated with dementia in Parkinson's and Alzheimer's disease. Studies have also shown some parameters to be dependent on the stage of the disease. We wanted to investigate the differences in high-resolution QEEG measures between groups of PD patients and healthy individuals, and come up with a small subset of features that could accurately distinguish between the two groups.Methods: High-resolution 256-channel EEG were recorded in 50 PD patients (age 68.8 ± 7.0 year; female/male 17/33) and 41 healthy controls (age 71.1 ± 7.7 year; female/male 20/22). Data was processed to calculate the relative power in alpha, theta, delta, beta frequency bands across the different regions of the brain. Median, peak frequencies were also obtained and alpha1/theta ratios were calculated. Machine learning methods were applied to the data and compared. Additionally, penalized Logistic regression using LASSO was applied to the data in R and a subset of best-performing features was obtained.Results: Random Forest and LASSO were found to be optimal methods for feature selection. A group of six measures selected by LASSO was seen to have the most effect in differentiating healthy individuals from PD patients. The most important variables were the theta power in temporal left region and the alpha1/theta ratio in the central left region.Conclusion: The penalized regression method applied was helpful in selecting a small group of features from a dataset that had high multicollinearity.

Aims: The objective of this study was to investigate the relation between impaired fine motor skills in Parkinson disease (PD) patients and their cognitive status, and to determine whether fine motor skills are more impaired in PD patients with mild cognitive impairment (MCI) than in non-MCI patients. Methods: Twenty PD MCI and 31 PD non-MCI patients (mean age 66.7 years, range 50-84, 36 males/15 females), all right-handed, took part in a motor performance test battery. Steadiness, precision, dexterity, velocity of arm-hand movements, and velocity of wrist-finger movements were measured and compared across groups and analyzed for confounders (age, sex, education, severity of motor symptoms, and disease duration). Statistical analysis included t tests corrected for multiple testing, and a linear regression with stepwise elimination procedure was used to select significant predictors for fine motor function. Results: PD MCI patients performed significantly worse in precision (p < 0.05), dexterity (p < 0.05), and velocity (arm-hand movements; p < 0.05) compared to PD non-MCI patients. The fine motor function skills were confounded by age. Conclusions: Fine motor skills in PD MCI patients are impaired compared to PD non-MCI patients. Investigating the relation between the fine motor performance and MCI in PD might be a relevant subject for future research.