Background: Novel paired associative stimulation (novel-PAS), delivered by pairing movement-related cortical potentials (MRCPs) with electrical stimulation of somatosensory afferents, is an innovative neuromodulatory intervention. Novel-PAS results in increased corticomotor excitability and has potential as a rehabilitative adjunct to improve outcomes following stroke. The duration of its excitatory effect has important implications for how this novel PAS intervention might be applied within a traditional therapy session, but previous research has not explored its effects beyond 30 min post-intervention.

Objective: The objective was to explore changes in corticomotor excitability in healthy participants, over a 60-min period following a single session of novel-PAS.

Materials and Method: Ten healthy adults completed a single session of novel-PAS, delivered by pairing 50 MRCPs with peripheral electrical stimulation. TMS was used to elicit motor evoked potentials (MEPs) of the tibialis anterior (TA) muscle, immediately prior to the intervention, and at 0, 30, 45, and 60 min post-intervention.

Results: When compared with pre-intervention, there was a statistically significant increase in the mean TA MEP amplitudes at 0 (p = 0.006), 30 (p = 0.006), 45 (p = 0.027), and 60 min post-intervention (p = 0.020).

Conclusion: Corticomotor excitability is increased for 60 min following this novel-PAS intervention. Future research could investigate the optimal method of combining this neuromodulatory technique with traditional therapy.

The accessibility to Electroencephalogram (EEG) recording systems has enabled the healthcare providers to record the brain activity of patients under treatment, during multiple sessions. Thus brain changes can be observed and evaluated. It has been shown in many studies that the EEG data are never exactly the same when recordings are done in different sessions inducing a shift between the data of multiple sessions. This shift is induced due to the changes in parameters such as: the physical /mental state of the patient, the ambient environment, location of the electrodes, and impedance of the electrodes. The shift can be modelled as a covariate shift between multiple sessions. However, the algorithms that have been developed to tackle this shift assume the presence of training as well as testing data apriori to calculate the importance weights which are then used in the learning algorithm to reduce the mismatch.

This major problem makes them impractical. In this paper, we tackle this, using marginalized stacked denoising autoencoder (mSDAs) while using the data from seven healthy subjects recorded over eight sessions distributed over four weeks. We compare our results with kernel mean matching, a popular
approach for covariate shift adaption. Using support vector machines for classification and reduced complexity of mSDA, we get promising accuracy.

Detection of single-trial movement intentions from EEG is paramount for brain-computer interfacing in neurorehabilitation. These movement intentions contain task-related information and if this is decoded, the neurorehabilitation could potentially be optimized. The aim of this study was to classify single-trial movement intentions associated with two levels of force and speed and three different grasp types using EEG rhythms and components of the movement-related cortical potential (MRCP) as features. The feature importance was used to estimate encoding of discriminative information. Two data sets were used. 29 healthy subjects executed and imagined different hand movements, while EEG was recorded over the contralateral sensorimotor cortex. The following features were extracted: delta, theta, mu/alpha, beta, and gamma rhythms, readiness potential, negative slope, and motor potential of the MRCP. Sequential forward selection was performed, and classification was performed using linear discriminant analysis and support vector machines. Limited classification accuracies were obtained from the EEG rhythms and MRCP-components: 0.48±0.05 (grasp types), 0.41±0.07 (kinetic profiles, motor execution), and 0.39±0.08 (kinetic profiles, motor imagination). Delta activity contributed the most but all features provided discriminative information. These findings suggest that information from the entire EEG spectrum is needed to discriminate between task-related parameters from single-trial movement intentions.