Spectral Graph Theory-Based Spatio-spectral Filters for Motor Imagery Brain–Computer Interface
01 Jan 2021-pp 247-256
TL;DR: A novel approach that utilizes graph theory-based unsupervised feature selection method to determine a reduced set of non-redundant and relevant frequency bands is proposed and shows improvement in classification performance.
Abstract: Motor imagery brain–computer interfaces are one of the widely adopted techniques for imparting basic communication capability to motor disabled patients The preciseness of a motor imagery BCI task classification is highly dependent on identifying the subject-specific relevant subset of frequency filters This article proposes a novel approach that utilizes graph theory-based unsupervised feature selection method to determine a reduced set of non-redundant and relevant frequency bands The empirical analysis of the proposed method is conducted on publicly available datasets, and the obtained results show improvement in classification performance Further, the performed Friedman statistical test also establishes that the proposed approach surpasses the baseline techniques in classification accuracy
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13 Dec 2019
TL;DR: A metric is developed to assess the relevance of Common Spatial Patterns using a mapping through Kernel Principal Component Analysis with the benefit of improved interpretation that allows evaluating the zones, which contribute the most to the motor imagery classification accuracy.
Abstract: Motor Imagery handles the brain activity patterns of motor action without explicit movements. For extracting the discriminating features, Common Spatial Patterns are the most widely used algorithm that is very sensitive to artifacts and prone to overfitting. Here, we develop a metric to assess the relevance of Common Spatial Patterns using a mapping through Kernel Principal Component Analysis with the benefit of improved interpretation that allows evaluating the zones, which contribute the most to the motor imagery classification accuracy. Validation is carried out on a real-world database, appraising two labels of Motor Imagery activity. From the obtained results, we prove that the developed approach allows the performance enhancement, at the time, the relevant set decreases the number of channels to feed the classifier, and thus reducing the computational cost.
1 citations