Feature Space Reduction for Single Trial EEG Classification based on Wavelet Decomposition

DGPDR: discriminative geometric perception dimensionality reduction of SPD matrices on Riemannian manifold for EEG classification

Manifold learning with Symmetric Positive Definite (SPD) matrices has demonstrated potential for classifying Electroencephalography (EEG) in Brain-Computer Interface (BCI) applications. However, SPD matrices may lead to crucial information loss of EEG signals. This paper proposes a dimensionality reduction method based on discriminative geometric perception on the Riemannian manifold to enhance SPD matrix discriminability. Experiments on BCI Competition IV Dataset 1 and Dataset 2a show the proposed method improves accuracy by 5.0% and 19.38% respectively, demonstrating that applying discriminative geometric perception can effectively maintain robust performance associated with the dimensionality-reduced SPD matrix.

Siamese Neural Networks for EEG-based Brain-computer Interfaces

Motivated by the inconceivable capability of human brain in simultaneously processing multi-modal signals and its real-time feedback to the outer world events, there has been a surge of interest in establishing a communication bridge between the human brain and a computer, which are referred to as Brain-computer Interfaces (BCI). To this aim, monitoring the electrical activity of brain through Electroencephalogram (EEG) has emerged as the prime choice for BCI systems. To discover the underlying and specific features of brain signals for different mental tasks, a considerable number of research works are developed based on statistical and data-driven techniques. However, a major bottleneck in development of practical and commercial BCI systems is their limited performance when the number of mental tasks for classification is increased. In this work, we propose a new EEG processing and feature extraction paradigm based on Siamese neural networks, which can be conveniently merged and scaled up for multi-class problems. The idea of Siamese networks is to train a double-input neural network based on a contrastive loss-function, which provides the capability of verifying if two input EEG trials are from the same class or not. In this work, a Siamese architecture, which is developed based on Convolutional Neural Networks (CNN) and provides a binary output on the similarity of two inputs, is combined with One vs. Rest (OVR) and One vs. One (OVO) techniques to scale up for multi-class problems. The efficacy of this architecture is evaluated on a 4-class Motor Imagery (MI) dataset from BCI Competition IV_2a and the results suggest a promising performance compared to its counterparts.