Giorgos Asfis

Brain–computer interface (BCI)-based neuromotor training is widely recognized as a promising approach for post-stroke motor rehabilitation. However, its practical deployability remains uncertain due to challenges related to EEG headset wearability and the limited validation of emerging algorithms in noisy, real-world environments. These settings demand low-density, affordable EEG systems, robust multimodal neurofeedback paradigms, and validation with a sufficient number of participants in online scenarios. To address these challenges, we developed a BCI system using an 8-channel g.tec Unicorn headset and conducted experiments in a real-world noisy environment with 20 participants for offline analysis and another 20 for online evaluation. The system includes a hand exoskeleton to deliver multimodal concomitant neurofeedback. We evaluated both a traditional method (Common Spatial Pattern+Support Vector Machine(CSP+SVM)) and a deep learning architecture (Filter-Bank Convolutional Neural Network(FBCNet)) across this setup. Our results show that incorporating data preprocessing and a longest consecutive repetition (LCR) based post-processing step significantly (p-value < 0.05) improves FBCNet’s performance in cross-subject decoding. In within-subject scenarios, CSP+SVM remains competitive with FBCNet, offering comparable accuracy with reduced training time. This study sets a comprehensive benchmark for the real-world deployment of wearable BCI systems for neuromotor training and provides a valuable testbed for evaluating future algorithms in practical, noisy environments.