AI Article Synopsis
- The study introduces a new decoding method for motor imagery brain-computer interfaces (BCIs) that uses a concept called Graph Fourier Transform (GFT), treating EEG signals as defined over a sensor array graph.
- A graph representing the brain's functional activity during imagined movements is created from training data, which is then analyzed with a technique called discriminative Lasso (dLasso) to extract useful features for classification.
- The proposed method was tested on two datasets and showed better performance compared to existing approaches, requiring only basic matrix operations for signal feature extraction after training.
Article Abstract
A novel decoding scheme for motor imagery (MI) brain computer interfaces (BCI's) is introduced based on the GFT concept. It considers the recorded EEG activity as a signal defined over (the graph of) the sensor array. A graph encapsulating the functional covariations emerging during the execution of a specific imagined movement is first defined, from a small training set of relevant trials. The ensemble of graphs signals corresponding to a multi-trial training dataset is then analyzed using a graph-guided decomposition and, based on discriminative Lasso (dLasso), an information-rich GFT subspace is defined. After training, only simple matrix operations are required for transforming the multichannel signal into features to be fed into a classifier that decides whether brain activity conforms with the graph structure associated with the targeted movement. The proposed decoding scheme is evaluated based on two different datasets and found to compare favorably against popular alternatives in the field.
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| http://dx.doi.org/10.1109/EMBC.2019.8856973 | DOI Listing |
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