Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals.

Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8-19 Hz and 19-30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, andKappaindex of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. Therefore, our method can be applied in the rehabilitation context, such as MMEB-based BCIs, in the future.

Dynamic Modeling of Common Brain Neural Activity in Motor Imagery Tasks.

Evaluation of brain dynamics elicited by motor imagery (MI) tasks can contribute to clinical and learning applications. The multi-subject analysis is to make inferences on the group/population level about the properties of MI brain activity. However, intrinsic neurophysiological variability of neural dynamics poses a challenge for devising efficient MI systems. Here, we develop a time-frequency model for estimating the spatial relevance of common neural activity across subjects employing an introduced statistical thresholding rule. In deriving multi-subject spatial maps, we present a comparative analysis of three feature extraction methods: Common Spatial Patterns, Functional Connectivity, and Event-Related De/Synchronization. In terms of interpretability, we evaluate the effectiveness in gathering MI data from collective populations by introducing two assumptions: (i) Non-linear assessment of the similarity between multi-subject data originating the subject-level dynamics; (ii) Assessment of time-varying brain network responses according to the ranking of individual accuracy performed in distinguishing distinct motor imagery tasks (left-hand vs. right-hand). The obtained validation results indicate that the estimated collective dynamics differently reflect the flow of sensorimotor cortex activation, providing new insights into the evolution of MI responses.

Selección de Electrodos Basada en k-means para la Clasificación de Actividad Motora en EEG / Eletrode Selection Based on k-means for Motor Activity Classification in EEG

Se presenta un algoritmo para la selección del grupo de electrodos relacionados con la imaginación de movimiento. El algoritmo utiliza la técnica de agrupamiento llamada k-means para formar grupos de sensores y selecciona el grupo que corresponde a la actividad correlacionada más alta. Para evaluar la selección de electrodos, se calcula el indice de clasificación aplicando la descomposición proyectiva llamada patrones espaciales comunes y un discriminante lineal en una prueba de una sola época para identificar la imaginación del movimiento de mano izquierda vs pie derecho. Esta propuesta reduce significativamente el número de electrodos de 118 a 35, además de mejorar el índice de clasificación.

We present an algorithm for electrodes selection associated with motor imagery activity. The algorithm uses a clustering technique called k-means to form groups of sensors and selects the group corresponding to the highest correlation activity. Then, we evaluate the selected electrodes computing the classification index using the projective decomposition called common spatial patterns and a linear discriminant method in a left hand vs right foot motor imagery classification task. This approach significantly reduces the number of electrodes from 118 to 35 while improving the classification accuracy index.