CNN-based framework using spatial dropping for enhanced interpretation of neural activity in motor imagery classificatio
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CNN‑based framework using spatial dropping for enhanced interpretation of neural activity in motor imagery classification D. F. Collazos‑Huertas1* , A. M. Álvarez‑Meza1, C. D. Acosta‑Medina1, G. A. Castaño‑Duque2 and G. Castellanos‑Dominguez1
Abstract Interpretation of brain activity responses using motor imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra- and inter-subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with an enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wave‑ let Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. We evaluate two labeled scenarios of MI tasks: bi-class and three-class. Obtained results in an MI database show that the thresholding strategy combined with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with a differentiated behavior of rhythms µ and β. Keywords: Motor imagery, Convolutional Neural Networks, Spatial dropping 1 Introduction The motor imagery (MI) paradigm is a form of brain– computer interface (BCI) that performs the imagination of a motor action without real execution, relying on the similarities between imagined and executed actions at the neural level. MI is usually measured with electroencephalography (EEG) to register brain activity on the scalp surface. Thus, assessment and interpretation of MI brain dynamics in the sensorimotor cortex may contribute to applications ranging from evaluation of pathological conditions and rehabilitation of motor functions [1, 2], motor learning and performance [3], improving the learning of different abilities [4], among others. In education scenarios, the Media and Information Literacy *Correspondence: [email protected] 1 Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, Colombia Full list of author information is available at the end of the article
methodology proposed by the UNESCO covers several competencies that are vital for people to be effectively engaged in all aspects of human development [5]. Nevertheless, one of the main challenges in implementing MI practice is recognizing and identifying the imagined actions since EEG signals have substantial intra- and inter-subject variability [6]. Currently, there is an increasing interest in deep learning models that are composed of multiple processing layers of inference usin
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