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

Abstract

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, and \textit{Kappa} index 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.

Influence of Temporal and Frequency Selective Patterns Combined with CSP Layers on Performance in Exoskeleton-Assisted Motor Imagery Tasks

“…For this reason, the selection of an appropriate filter bank is not trivial. In previous studies [26,32,38], our findings with FBCSSP were better than those with conventional CSP using the 3-filter configuration of 8-15 Hz, 15-22 Hz, and 22-30 Hz, which was our motivation. However, it was possible to observe that further analysis with different configurations is necessary to improve the performance metrics of the classifiers.…”

“…In our previous studies related to temporal and frequency evaluation and CSP layer enhancement, we determined time windows and specific filter banks to find EEG patterns that allow to differentiate MI tasks with greater accuracy [26,32,38]. For instance, in [26], these methods were compared to decode MI of the right and left hands in two databases, finding significant differences when varying the length of the time window and the filter bank configurations.…”

“…It should be noted that in these previous studies were based on left-and right-hand classification, which diverge from those presented during this study. In another approach reported in [38], CSP techniques were evaluated during the execution of lower limb movements, such as classification between pedaling and non-pedaling tasks from EEG signals.…”

“…The variation implemented to increase the number of CSP layers uses a filter bank, previously extracted using FBCSP, and applies an additional CSP layer that combines each of the sub-CSPs calculated for each filter bank [32,38]. This method is called FBCSSP, see Figure 1.…”

“…Layer 1: This layer enables the extraction of the CSP from filtered signals after employing the filter bank. Its importance stems from its capability to emphasize differences in variances between classes for particular frequency-selective patterns [38]. • Layer 2: CSP is used to generate more selective features associated with the spatial and spectral domains.…”