Detection of the Intention to Perform a Two-Staged Movement from EEG Signals

The article explores methods for recognizing neu-rophysiological signals related to the preparation and execution of voluntary movements in the human brain. The experiment involved participants performing a complex self-initiated move-ment, comprising pressing a button and touching a marker around a transparent partition. Various machine learning models were utilized, including linear models with regularization, random forests, and support vector machines. The introduction of a “stacking” model facil-itated the incorporation of new feature types without complete retraining of base models. Hyperparameter optimization was conducted using cross-validation. To address class imbalance, upsampling methods and penalties in the loss functions were applied. Balanced accuracy was chosen as the target metric, considering the disparity between the number of positive and negative epochs. The SHAP method was employed for results interpretation. The best-performing model demonstrated a balanced accuracy of 72% for right-hand presses and 77% for left-hand presses.