Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity
Author:
Collazos-Huertas Diego Fabian1ORCID, Álvarez-Meza Andrés Marino1ORCID, Cárdenas-Peña David Augusto2ORCID, Castaño-Duque Germán Albeiro3ORCID, Castellanos-Domínguez César Germán1ORCID
Affiliation:
1. Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia 2. Automatics Research Group, Universidad Tecnológica de Pereria, Pereira 660003, Colombia 3. Cultura de la Calidad en la Educación Research Group, Universidad Nacional de Colombia, Manizales 170003, Colombia
Abstract
Motor Imagery (MI) refers to imagining the mental representation of motor movements without overt motor activity, enhancing physical action execution and neural plasticity with potential applications in medical and professional fields like rehabilitation and education. Currently, the most promising approach for implementing the MI paradigm is the Brain-Computer Interface (BCI), which uses Electroencephalogram (EEG) sensors to detect brain activity. However, MI-BCI control depends on a synergy between user skills and EEG signal analysis. Thus, decoding brain neural responses recorded by scalp electrodes poses still challenging due to substantial limitations, such as non-stationarity and poor spatial resolution. Also, an estimated third of people need more skills to accurately perform MI tasks, leading to underperforming MI-BCI systems. As a strategy to deal with BCI-Inefficiency, this study identifies subjects with poor motor performance at the early stages of BCI training by assessing and interpreting the neural responses elicited by MI across the evaluated subject set. Using connectivity features extracted from class activation maps, we propose a Convolutional Neural Network-based framework for learning relevant information from high-dimensional dynamical data to distinguish between MI tasks while preserving the post-hoc interpretability of neural responses. Two approaches deal with inter/intra-subject variability of MI EEG data: (a) Extracting functional connectivity from spatiotemporal class activation maps through a novel kernel-based cross-spectral distribution estimator, (b) Clustering the subjects according to their achieved classifier accuracy, aiming to find common and discriminative patterns of motor skills. According to the validation results obtained on a bi-class database, an average accuracy enhancement of 10% is achieved compared to the baseline EEGNet approach, reducing the number of “poor skill” subjects from 40% to 20%. Overall, the proposed method can be used to help explain brain neural responses even in subjects with deficient MI skills, who have neural responses with high variability and poor EEG-BCI performance.
Funder
PROGRAMA DE INVESTIGACIÓN RECONSTRUCCIÓN DEL TEJIDO SOCIAL EN ZONAS DE POSCONFLICTO EN COLOMBIA Código SIGP Código Colombia Científica Universidad Nacional de Colombia and Universidad de Caldas
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference66 articles.
1. Motor Imagery Practice and Cognitive Processes;Moran;Front. Psychol.,2020 2. Children’s motor imagery modality dominance modulates the role of attentional focus in motor skill learning;Bahmani;Hum. Mov. Sci.,2021 3. Behrendt, F., Zumbrunnen, V., Brem, L., Suica, Z., Gäumann, S., Ziller, C., Gerth, U., and Schuster-Amft, C. (2021). Effect of motor imagery training on motor learning in children and adolescents: A systematic review and meta-analysis. Int. J. Environ. Res. Public Health, 18. 4. Said, R.R., Heyat, M.B.B., Song, K., Tian, C., and Wu, Z. (2022). A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain–Computer Interface Based on Movement-Related Cortical Potentials. Biosensors, 12. 5. Identifying Thematics in a Brain-Computer Interface Research;Alharbi;Comput. Intell. Neurosci.,2023
|
|