The ability to learn motor tasks is important in both healthy and pathological conditions. Measurement tools commonly used to quantify the neurophysiological changes associated with motor training such as transcranial magnetic stimulation and functional magnetic resonance imaging pose some challenges, including safety concerns, utility, and cost. EEG offers an attractive alternative as a quantification tool. Different EEG phenomena, movement-related cortical potentials (MRCPs) and sensorimotor rhythms (event-related desynchronization—ERD, and event-related synchronization—ERS), have been shown to change with motor training, but conflicting results have been reported. The aim of this study was to investigate how the EEG correlates (MRCP and ERD/ERS) from the motor cortex are modulated by short (single session in 14 subjects) and long (six sessions in 18 subjects) motor training. Ninety palmar grasps were performed before and after 1 × 45 (or 6 × 45) min of motor training with the non-dominant hand (laparoscopic surgery simulation). Four channels of EEG were recorded continuously during the experiments. The MRCP and ERD/ERS from the alpha/mu and beta bands were calculated and compared before and after the training. An increase in the MRCP amplitude was observed after a single session of training, and a decrease was observed after six sessions. For the ERD/ERS analysis, a significant change was observed only after the single training session in the beta ERD. In conclusion, the MRCP and ERD change as a result of motor training, but they are subject to a marked intra- and inter-subject variability.

Background/ObjectiveAutoimmune diseases such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) have been associated with an impaired function of the autonomic nervous system and reduced vagus nerve (VN) tone measured through lower heart rate variability (HRV). Targeting the VN through electrical stimulation has been proposed as a treatment strategy with promising results in patients with RA. Moreover, it has been suggested that the VN can be stimulated physiologically through deep breathing. In this study, the aim was to investigate if the VN can be stimulated through deep breathing in patients with RA and SLE, as measured by HRV.MethodsFifty-seven patients with RA and SLE performed deep breathing exercises for 30 minutes in this explorative study. Before the breathing exercise, 2 electrocardiogram recordings were obtained to determine the patient's baseline HRV during rest. After the 30-minute breathing exercise, 5 minutes of electrocardiogram recordings were obtained to determine postintervention HRV and used as a measure of vagal activity.ResultsNo change was observed in the HRV between the 2 recordings prior the exercise, but the heart rate and HRV significantly decreased and increased, respectively, after the deep breathing exercise.ConclusionsHRV can be modulated in patients with RA and SLE; this may have implications for future treatment with medications in conjunction with deep breathing. However, the biological and clinical effect of deep breathing must be investigated in future studies.

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.

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Brain-computer interface (BCI) driven electrical stimulation has been proposed for neuromodulation for stroke rehabilitation by pairing intentions to move with somatosensory feedback from electrical stimulation. Movement intentions have been detected in several studies using different techniques, with temporal and spectral features being the most common. A few studies have compared temporal and spectral features, but conflicting results have been reported. In this study, the aim was to investigate if complexity measures can be used for movement intention detection and to compare the detection performance based on features extracted from three different domains (time, frequency and complexity) from single-trial EEG. Two data sets were used where four different isometric palmar grasps or dorsiflexions were performed while continuous EEG was recorded. 39 healthy subjects performed or imagined these movements and 11 stroke patients attempted to perform the movements. The EEG was pre-processed and divided into two epoch classes: Background EEG (2 s) and movement intention (2 s). To obtain an estimated detection performance, temporal, spectral and complexity features were extracted and classified (linear discriminant analysis) after the feature vector was reduced using sequential forward selection. The results show that accuracies between 82-87% and 74-80% are obtained for foot and hand movements, respectively. The temporal feature domain was the most dominant for foot movement intention detection, while the spectral features contributed more to the hand movement detection. The complexity features could be used to detect movement intentions, but the performance was much lower compared to temporal and spectral features.