Spatial interpretability of time-frequency relevance optimized in motor imagery discrimination using Deep&Wide networks. (July 2021)

• Record Type: Journal Article
• Authors: Collazos-Huertas, D.F.; Álvarez-Meza, A.M.; Castellanos-Dominguez, G.
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Motor imagery -- Time-frequency -- Spatial relevance -- Deep&Wide network; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102626
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: We develop a CNN-based learner with an interpretation of spatial neural patterns. Dealing with nonstationarity, bidomain t-f representation is extracted, CWT and CSP. ORDW uses Deep&Wide learning, assessing the spatial relevance of neural responses. ORDW enhances the performed relevant spatial t-f representations, preserving accuracy. ORDW reveals explainable information about electrodes with higher spatial relevance. Abstract: Medical diagnosis and monitoring benefit from exploiting the advantages of motor imagery (MI) training, which highly depends on the proper interpretation of elicited brain activity responses. Convolutional neural networks (CNN) are increasingly used to improve MI classification performance by employing multi-view extracted features (time, frequency, and spatial). However, deep learning gains knowledge from abundant, complex neural models, resulting in poor interpretability assessments. Here, to enhance the understanding of imagined actions, we develop the relevance analysis of topographic time-frequency representation, preserving an adequate classification performance. Namely, to deal better with the subject variability, a 2D feature combination of continuous wavelet transform and common spatial patterns is extracted to feed a Deep&Wide learning model, assessing the relevance of input multi-view representation that contribute the best to the classifier accuracy. We estimate the feature contribution through the information back-propagated across the hidden layers' weights before predicting the output label. Evaluation, presented in two databases with bi- and four-label MI tasks, proves that the developed Deep&Wide-based relevance analysis reveals insights about the electrodes, time segments, and frequency bands with relevant MI neural responses, favoring explanation of inter and intra-subject variability because of coordination skills.