A novel theta-controlled vibrotactile brain-computer interface to treat chronic pain: a pilot study

ESI-GAL: EEG source imaging-based trajectory estimation for grasp and lift task

BACKGROUND

Electroencephalogram (EEG) signals-based motor kinematics prediction (MKP) has been an active area of research to develop Brain-computer interface (BCI) systems such as exosuits, prostheses, and rehabilitation devices. However, EEG source imaging (ESI) based kinematics prediction is sparsely explored in the literature.

METHOD

In this study, pre-movement EEG features are utilized to predict three-dimensional (3D) hand kinematics for the grasp-and-lift motor task. A public dataset, WAY-EEG-GAL, is utilized for MKP analysis. In particular, sensor-domain (EEG data) and source-domain (ESI data) based features from the frontoparietal region are explored for MKP. Deep learning-based models are explored to achieve efficient kinematics decoding. Various time-lagged and window sizes are analyzed for hand kinematics prediction. Subsequently, intra-subject and inter-subject MKP analysis is performed to investigate the subject-specific and subject-independent motor-learning capabilities of the neural decoders. The Pearson correlation coefficient (PCC) is used as the performance metric for kinematics trajectory decoding.

RESULTS

The rEEGNet neural decoder achieved the best performance with sensor-domain and source-domain features with the time lag and window size of 100ms and 450ms, respectively. The highest mean PCC values of 0.790, 0.795, and 0.637 are achieved using sensor-domain features, while 0.769, 0.777, and 0.647 are achieved using source-domain features in x, y, and z-directions, respectively.

CONCLUSION

This study explores the feasibility of trajectory prediction using EEG sensor-domain and source-domain features for the grasp-and-lift task. Furthermore, inter-subject trajectory estimation is performed using the proposed deep learning decoder with EEG source domain features.

Closed-loop neural interfaces for pain: Where do we stand?

Advances in closed-loop neural interfaces and neuromodulation have offered a potentially effective and non-addictive treatment for chronic pain. These interfaces link neural sensors with device outputs to provide temporally precise stimulation. We discuss challenges and trends of state-of-the-art neural interfaces for treating pain in animal models and human pilot trials.

Evaluating the effectiveness of neurofeedback in chronic pain management: a narrative review

The prevalence and impact of chronic pain in individuals worldwide necessitate effective management strategies. This narrative review specifically aims to assess the effectiveness of neurofeedback, an emerging non-pharmacological intervention, on the management of chronic pain. The methodology adopted for this review involves a meticulous search across various scientific databases. The search was designed to capture a broad range of studies related to neurofeedback and chronic pain management. To ensure the quality and relevance of the included studies, strict inclusion and exclusion criteria were applied. These criteria focused on the study design, population, intervention type, and reported outcomes. The review synthesizes the findings from a diverse array of studies, including randomized controlled trials, observational studies, and case reports. Key aspects evaluated include the types of neurofeedback used (such as EEG biofeedback), the various chronic pain conditions addressed (like fibromyalgia, neuropathic pain, and migraines), and the methodologies employed in these studies. The review highlights the underlying mechanisms by which neurofeedback may influence pain perception and management, exploring theories related to neural plasticity, pain modulation, and psychological factors. The results of the review reveal a positive correlation between neurofeedback interventions and improved pain management. Several studies report significant reductions on pain intensity, improved quality of life, and decreased reliance on medication following neurofeedback therapy. The review also notes variations in the effectiveness of different neurofeedback protocols and individual responses to treatment. Despite the promising results, the conclusion of the review emphasizes the need for further research. It calls for larger, well-designed clinical trials to validate the findings, to understand the long-term implications of neurofeedback therapy, and to optimize treatment protocols for individual patients.