Quantum neural network-based EEG filtering for a brain-computer interface

A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as a Recurrent Quantum Neural Network (RQNN) can characterize a non-stationary stochastic signal as time varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates estimation of the signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as DC, staircase DC and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two class motor imagery based brain-computer interface where the objective was to filter EEG signals prior to feature extraction and classification to increase signal separability. A two-step inner outer 5-fold cross-validation approach is utilized to select the algorithm parameters subject-specifically for 9 subjects. It is shown that the subject specific RQNN EEG filtering significantly improves BCI performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.