Aura Ximena González-Cely

A stroke can affect postural control, balance, and walking, thereby limiting the ability to perform activities of daily living. Although conventional physical therapy supports motor recovery through active walking, researchers have increasingly investigated advanced modalities, such as integrating motor-imagery-based brain–computer interfaces (BCIs) with proprioceptive feedback (PF). This combination aims to engage both the peripheral and central nervous systems. However, this integrated approach remains underexplored, particularly in the domain of lower-limb rehabilitation. To address this gap, this study developed and evaluated a BCI with five stroke survivors. The BCI provided treadmill-based PF at three different speeds, controlled by relative power changes detected in the participants’ brain signals during kinesthetic motor imagery (KMI). Cortical activity was analyzed across three states: baseline period, active walking, and KMI + PF. Specifically, active walking exhibited lower event-related desynchronization values compared to the KMI state combined with feedback. These findings suggest that the detection of relative power changes may provide a basis for establishing a common threshold in future MI classification strategies. In terms of clinical outcomes, preliminary improvements were observed across all five participants. However, the functional ambulation category metric, which reflects the level of assistance required for walking, remained the same in two out of the five participants. In addition, the lack of a control group limits our analysis to a pilot study rather than determining clinical improvement. These results support the potential of integrating brain signals with PF and provide a basis for exploring classification strategies that could reduce calibration time and improve the applicability of BCI systems in clinical settings.

Decoding motor imagery (MI) of lower-limb movements from electroencephalography (EEG) signals remains a challenge due to the involvement of deep cortical regions, limiting the applicability of Brain–Computer Interfaces (BCIs). This study proposes a novel protocol that combines passive pedaling (PP) as sensory priming with MI at different speeds (30, 45, and 60 rpm) to improve EEG-based classification. Ten healthy participants performed PP followed by MI tasks while EEG data were recorded. An increase in spectral relative power around Cz associated with both PP and MI was observed, varying with speed and suggesting that PP may enhance cortical engagement during MI. Furthermore, our classification strategy, based on Convolutional Neural Networks (CNNs), achieved an accuracy of 0.87–0.89 across four classes (three speeds and rest). This performance was also compared with the standard Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA), which achieved an accuracy of 0.67–0.76. These results demonstrate the feasibility of multiclass decoding of imagined pedaling velocities and lay the groundwork for speed-adaptive BCIs, supporting future personalized and user-centered neurorehabilitation interventions.

Recently, robotic wheelchairs commanded by Human-Machine Interfaces (HMIs) have gained recognition for enhancing the quality of life of people with physical disabilities. In this sense, implementing sensors that allow for accurate and comfortable user intention recognition remains challenging. Additionally, posture monitoring for pressure ulcer prevention in wheelchair users is often overlooked. In this study, three HMIs are proposed to recognize the user’s intention using information linked to head, and neck movements, and visually evoked potentials to control an electric-powered wheelchair in four directions: forward, left, right, and back. These HMIs incorporate technologies, such as a IMU-based system, pressure sensors based on Polymeric Optical Fiber (POF), and steady-state visual evoked potential-based brain-computer interface (BCI) to generate control commands. The POF-based pressure sensors also allow for posture classification. The HMIs were evaluated functionally, and the user’s experience was considered from healthy subjects. The head-motion-based system obtained the highest Accuracy (ACC) rate (~0.99) and less workload. In contrast, the BCI reached the highest satisfaction and usability, whereas the neck-motion-based system achieved the lowest latency (~28 ms). The posture classification system achieved an acceptable ACC (~0.80), latency (~117 ms), and good perception. These results have great implications for the design of wheelchair systems to improve the independence of people with reduced mobility using information from multiple sources and for posture monitoring towards the prevention of pressure ulcers generation.