Evoked Potentials Detection During Self-Initiated Movements Using Machine Learning Approach

This paper focuses on the issue of determining the intention to initiate a movement using machine learning approach. In the experiment under consideration, the subjects make spontaneous movements, and their brain activity is recorded using an electroencephalogram. The problem is formulated as a binary classification problem where the model should determine whether the given segment of signal precedes the movement. In order to perform this task the features from both the time and frequency domains were extracted. The process of feature extraction is parametrized through a special procedure, and the parameters of this procedure are selected through a grid-search technique along with model hyperparameters. The best metrics were obtained using Random Forest model that had a balanced accuracy of 77% on the test set. Moreover, the impact of the attention system during the conducted experiments was analyzed. Two paradigms were employed: the oddball paradigm, where deviant stimuli, which occurens leads to the involuntary attention activation, were introduced, and the control paradigm, which omitted any deviant stimuli. Experiments have shown that models perform better under oddball paradigm.