ML Democracy: An Enhanced Voting Algorithm for Model Selection for Efficient EEG Data Assessment

Timothy Flavin, Thomas Steiner, John Reaves, Julian Abhari, Zoe Delap, Bhaskar Mitra, Vidhyashree Nagaraju

Abstract: Brain-Computer Interface (BCI) technology has created solutions to assist and augment human cognitive or sensory motor functions through several communication channels between the brain and external devices. BCI operates by processing temporal features extracted from neurological data such as EEG due to its relatively inexpensive and non-intrusive nature. However, EEG data can be very difficult to classify with machine learning and statistical methods due to its high signal-to-noise ratio as well as non-linear and non-stationary characteristics of data. Therefore, this paper proposes a novel approach to algorithm selection for complicated data such as EEG by creating a voting algorithm, `ML Democracy’. ML Democracy combines multiple machine learning algorithms through bootstrap aggregating, which is also known as bagging and model stacking. Three weighting methods will be presented in this paper including (i) distance from randomness, (ii) distance from perfect accuracy, and (iii) learned weights by a neural network. Several algorithms including neural network and ensemble learning methods are considered. The efficacy of proposed approach is demonstrated using a Physionet motor-imagery EEG data set [17].