Spinal Cord Injury Patients Exhibit Changes in Motor-Related Activity and Topographic Distribution.

Spinal Cord Injury (SCI) is a common disease that usually limits the patient's independence by affecting their motor function. SCI patients usually present neuroplasticity, which allows brain signals transmission through spread pathways. Some innovative rehabilitation therapies, such as functional electrical stimulation (FES) or Brain-computer interfaces (BCIs) jointly with motor neuroprostheses, provide hope for functional restoration. BCIs require the analysis of event-related EEG potentials (ERPs). Movement-related cortical potentials (MRCPs) and event-related desynchroni-zation and synchronization (ERD/ERS) are the most commonly studied ERPs during motor activity. ERPs of healthy subjects may vary from SCI patients. Thus, this study aimed to compare ERPs between healthy subjects and SCI patients during upper-limb movements (forearm supination and pronation, and hand open). Differences between controls and SCI patients were shown in terms of ERPs' amplitude as well as in topographic maps. Changes in amplitude were more substantial in ERD potentials than in MRCPs, while topographic maps showed better localization of all features in healthy patients. The level of SCI injury determines the patients' mobility. A comparison between complete, partial and no motor function subjects showed lower values of feature's amplitudes in the latter group.Clinical Relevance- This demonstrates the existence of significant statistical differences between healthy and SCI subjects, and might be helpful when performing SCI rehabilitation techniques such as designing BCI and neuroprostheses, or analyzing and understanding the brain plasticity process.

Influence of the number of trials on evoked motor cortical activity in EEG recordings.

Objective. Improvements in electroencephalography enable the study of the localization of active brain regions during motor tasks. Movement-related cortical potentials (MRCPs), and event-related desynchronization (ERD) and synchronization are the main motor-related cortical phenomena/neural correlates observed when a movement is elicited. When assessing neurological diseases, averaging techniques are commonly applied to characterize motor related processes better. In this case, a large number of trials is required to obtain a motor potential that is representative enough of the subject's condition. This study aimed to assess the effect of a limited number of trials on motor-related activity corresponding to different upper limb movements (elbow flexion/extension, pronation/supination and hand open/close).Approach. An open dataset consisting on 15 healthy subjects was used for the analysis. A Monte Carlo simulation approach was applied to analyse, in a robust way, different typical time- and frequency-domain features, topography, and low-resolution electromagnetic tomography.Main results. Grand average potentials, and topographic and tomographic maps showed few differences when using fewer trials, but shifts in the localization of motor-related activity were found for several individuals. MRCP and beta ERD features were more robust to a limited number of trials, yielding differences lower than 20% for cases with 50 trials or more. Strong correlations between features were obtained for subsets above 50 trials. However, the inter-subject variability increased as the number of trials decreased. The elbow flexion/extension movement showed a more robust performance for a limited number of trials, both in population and in individual-based analysis.Significance. Our findings suggested that 50 trials can be an appropriate number to obtain stable motor-related features in terms of differences in the averaged motor features, correlation, and changes in topography and tomography.

Electrocardiographic Imaging: A Comparison of Iterative Solvers.

Cardiac disease is a leading cause of morbidity and mortality in developed countries. Currently, non-invasive techniques that can identify patients at risk and provide accurate diagnosis and ablation guidance therapy are under development. One of these is electrocardiographic imaging (ECGI). In ECGI, the first step is to formulate a forward problem that relates the unknown potential sources on the cardiac surface to the measured body surface potentials. Then, the unknown potential sources on the cardiac surface are reconstructed through the solution of an inverse problem. Unfortunately, ECGI still lacks accuracy due to the underlying inverse problem being ill-posed, and this consequently imposes limitations on the understanding and treatment of many cardiac diseases. Therefore, it is necessary to improve the solution of the inverse problem. In this work, we transfer and adapt four inverse problem methods to the ECGI setting: algebraic reconstruction technique (ART), random ART, ART Split Bregman (ART-SB) and range restricted generalized minimal residual (RRGMRES) method. We test all these methods with data from the Experimental Data and Geometric Analysis Repository (EDGAR) and compare their solution with the recorded epicardial potentials provided by EDGAR and a generalized minimal residual (GMRES) iterative method computed solution. Activation maps are also computed and compared. The results show that ART achieved the most stable solutions and, for some datasets, returned the best reconstruction. Differences between the solutions derived from ART and random ART are almost negligible, and the accuracy of their solutions is followed by RRGMRES, ART-SB and finally the GMRES (which returned the worst reconstructions). The RRGMRES method provided the best reconstruction for some datasets but appeared to be less stable than ART when comparing different datasets. In conclusion, we show that the proposed methods (ART, random ART, and RRGMRES) improve the GMRES solution, which has been suggested as inverse problem solution for ECGI.

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