Multiresolution analysis over graphs for a motor imagery based online BCI game

Abstract

Multiresolution analysis (MRA) over graph representation of EEG data has proved to be a promising method for offline brain-computer interfacing (BCI) data analysis. For the first time we aim to prove the feasibility of the graph lifting transform in an online BCI system. Instead of developing a pointer device or a wheel-chair controller as test bed for human-machine interaction, we have designed and developed an engaging game which can be controlled by means of imaginary limb movements. Some modifications to the existing MRA analysis over graphs for BCI have also been proposed, such as the use of common spatial patterns for feature extraction at the different levels of decomposition, and sequential floating forward search as a best basis selection technique. In the online game experiment we obtained for three classes an average classification rate of 63.0% for fourteen naive subjects. The application of a best basis selection method helps significantly decrease the computing resources needed. The present study allows us to further understand and assess the benefits of the use of tailored wavelet analysis for processing motor imagery data and contributes to the further development of BCI for gaming purposes.

Characterization of Visuomotor/Imaginary Movements in EEG: An Information Theory and Complex Network Approach

“…Thus, it is extremely useful to investigate new methodologies that can offer a better understanding of how motor imagined patterns and connectivity differs from the non-imagined/realized activities. Recently, new research has investigated the possibility of taking measures that were originally developed in graph theory for data classification as they could provide important information about the connectivity [80]. In particular, a recent study has shown that graph metrics can be used for EEG-BCIs based on hand motor imagery graphs, as they are a feasible option for classification of hand motor imagined signals [81].…”

Neurological Classifier Committee Based on Artificial Neural Networks and Support Vector Machine for Single-Trial EEG Signal Decoding

“…Thus, [21] applied the common spatial pattern method, [22] implicated modified regression algorithm, [4] used coefficient of determination for the evaluation of spectral features topographies. The mapping method realized in this work allowed obtaining decoding accuracies for each channel that makes the selection of informative channels numerically validated but requires computational resources.…”

Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG‐based BCI systems

“…In the same way, wavelet analysis has been widely used in EEG-based MI applications due to its ability to offer temporal-spectral analysis across different resolution levels [ 7 ]. Most recently, there have been a few attempts to use graph metrics as features for MI classification algorithms [ 8 ]. The main difference in this research with respect to the above-mentioned analysis lies in the fact that the classification process is made using graph theory features extracted from the synchrostates, a novel concept never used before for classification of MI tasks.…”