Javier V. Juan

(1) Background: Motor imagery (MI) analysis via electroencephalography (EEG) is crucial for Brain–Machine Interfaces (BMIs) and neurorehabilitation, though inherent EEG noise challenges robust decoding algorithm development for assistive devices aiding patient recuperation. (2) Methods: This study introduces a deep learning pipeline utilizing the IFNet convolutional neural network with frequency filtering and two scaling methods to decode pedaling MI. To mitigate catastrophic forgetting during transfer learning, a rotative Elastic Weight Consolidation (rEWC)-based weight adjustment procedure was incorporated. The pipeline was validated with 23 non-disabled participants across four sessions each, evaluating four approaches via pseudo-online (open-loop) and real-time online (closed-loop) analyses. (3) Results: The decoding accuracy for MI exceeded 80% (up to 95% individually) in selected pseudo-online approaches, while real-time online classification averaged 70% accuracy with a slight bias towards the relax task, confirming statistical significance for both scenarios. (4) Conclusions: These findings demonstrate that mitigating catastrophic forgetting in transfer learning within deep learning models and applying proper scaling are crucial for optimal performance. Consequently, the proposed deep learning pipeline holds significant promise for effective, adaptive EEG-based neurorehabilitation systems.

Introduction: In recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation. However, EEG signals present challenges due to their non-stationarity and the substantial presence of noise commonly found in recordings, making it difficult to design highly effective decoding algorithms. These algorithms are vital for controlling devices in neurorehabilitation tasks, as they activate the patient's motor cortex and contribute to their recovery.Methods: This study proposes a novel approach for decoding MI during pedalling tasks using EEG signals. A widespread approach is based on feature extraction using Common Spatial Patterns (CSP) followed by a linear discriminant analysis (LDA) as a classifier. The first approach covered in this work aims to investigate the efficacy of a task-discriminative feature extraction method based on CSP filter and LDA classifier. Additionally, the second alternative hypothesis explores the potential of a spectro-spatial Convolutional Neural Network (CNN) to further enhance the performance of the first approach. The proposed CNN architecture combines a preprocessing pipeline based on filter banks in the frequency domain with a convolutional neural network for spectro-temporal and spectro-spatial feature extraction.Results and discussion: To evaluate the approaches and their advantages and disadvantages, EEG data has been recorded from several able-bodied users while pedalling in a cycle ergometer in order to train motor imagery decoding models. The results show levels of accuracy up to 80% in some cases. The CNN approach shows greater accuracy despite higher instability.

Resumen El principio clave de la decodificación de la imaginación motora (MI, Motor Imagery) para las Interfaces Cerebro-Máquina (BMIs, Brain-Machine Interfaces) basadas en electroencefalogramas (EEG) consiste en extraer características discriminativas de tarea de los dominios espectrales, espaciales y temporales de manera conjunta y eficiente. A este respecto, la no estacionalidad de las señales EEG y su alta relación señal/ruido representan un desafío para el diseño avanzado de algoritmos de decodificación efectivos. Por su parte estos algoritmos permiten el control de dispositivos en tareas de neurorrehabilitación, activando la corteza motora del paciente y contribuyendo a su recuperación. Por estas razones, este estudio ha optimizado un algoritmo de decodificación de MI basado en características espaciales, el cual ha sido probado en dos sujetos sanos alcanzando rangos de acierto de hasta el 80 %, lo que supone una mejora respecto a estudios previos.

One important point in the development of a brain-machine Interface (BMI) commanding an exoskeleton is the assessment of the cognitive engagement of the subject during the motor imagery tasks conducted. However, there are not many databases that provide electroencephalography (EEG) data during the use of a lower-limb exoskeleton. The current paper presents a database designed with an experimental protocol aiming to assess not only motor imagery during the control of the device, but also the attention to gait on flat and inclined surfaces. The research was conducted as an EUROBENCH subproject in the facilities sited in Hospital Los Madroños, Brunete (Madrid). The data validation reaches accuracies over 70% in the assessment of motor imagery and attention to gait, which marks the present database as a valuable resource for researches interested on developing and testing new EEG-based BMIs.