Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis.

Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal-based abnormality in ALS. High-density resting-state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24-month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A-D) using K-means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late-stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher-order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.

Changes in electroencephalography complexity and functional magnetic resonance imaging connectivity following robotic hand training in chronic stroke.

Introduction: In recent years, robotic training has been utilized for recovery of motor control in patients with motor deficits. Along with clinical assessment, electrical patterns in the brain have emerged as a marker for studying changes in the brain associated with brain injury and rehabilitation. These changes mainly involve an imbalance between the two hemispheres. We aimed to study the effect of brain computer interface (BCI)-based robotic hand training on stroke subjects using clinical assessment, electroencephalographic (EEG) complexity analysis, and functional magnetic resonance imaging (fMRI) connectivity analysis. Method: Resting-state simultaneous EEG-fMRI was conducted on 14 stroke subjects before and after training who underwent 20 sessions robot hand training. Fractal dimension (FD) analysis was used to assess neuronal impairment and functional recovery using the EEG data, and fMRI connectivity analysis was performed to assess changes in the connectivity of brain networks. Results: FD results indicated a significant asymmetric difference between the ipsilesional and contralesional hemispheres before training, which was reduced after robotic hand training. Moreover, a positive correlation between interhemispheric asymmetry change for central brain region and change in Fugl Meyer Assessment (FMA) scores for upper limb was observed. Connectivity results showed a significant difference between pre-training interhemispheric connectivity and post-training interhemispheric connectivity. Moreover, the change in connectivity correlated with the change in FMA scores. Results also indicated a correlation between the increase in connectivity for motor regions and decrease in FD interhemispheric asymmetry for central brain region covering the motor area. Conclusion: In conclusion, robotic hand training significantly facilitated stroke motor recovery, and FD, along with connectivity analysis can detect neuroplasticity changes.

Muscle endurance time estimation during isometric training using electromyogram and supervised learning.

Constant-force isometric muscle training is useful for increasing the maximal strength , rehabilitation and work-fatigue assessment. Earlier studies have shown that muscle fatigue characteristics can be used for evaluating muscle endurance limit. STUDY OBJECTIVE: To predict muscle endurance time during isometric task using frequency spectrum characteristics of surface electromyography signals along with analysis of frequency spectrum shape and scale during fatigue accumulation. METHOD: Thirteen subjects performed isometric lateral raise at 60% MVC of deltoid (lateral) till endurance limit. Time windowed sEMG frequency spectrum was modelled using 2-parameter distributions namely Gamma and Weibull for spectrum analysis and endurance prediction. RESULTS: Gamma distribution provided better spectrum fitting (P < 0.001) than Weibull distribution. Spectrum Distribution demonstrated no change in shape but shifted towards lower frequency with increase of magnitude at characteristic mode frequency. Support Vector Regression based algorithm was developed for endurance time estimation using features derived from fitted frequency spectrum. Time taken till endurance limit for acquired dataset 38.53 ± 17.33 s (Mean ± Standard Deviation) was predicted with error of 0.029 ± 4.19 s . R-square: 0.956, training and test sets RMSE was calculated as 3.96 and 4.29 s respectively. The application of the algorithm suggested that model required 70% of sEMG signal from maximum time of endurance for high prediction accuracy. CONCLUSION: Endurance Limit prediction algorithm was developed for quantification of endurance time for optimizing isometric training and rehabilitation. Our method could help personalize and change conventional training method of same weight and duration for all subjects with optimized training parameters, based upon individual sEMG activity.

Effects of Electromyographically-driven Neuromuscular Stimulatio Cycling System on the Lower-Limb of Stroke Survivors.

This paper describes the design of an Electromyographically(EMG)-driven Neuromuscular Electrical Stimulation (NMES) cycling system. It utilises real-time EMG from actively participating stroke survivors as feedback control to drive the cycling system for rehabilitation. The user controls the speed of the cycling system using muscle activities of the side affected recorded by EMG electrodes. Additionally, adaptable NMES stimulations; also EMG based, were provided in cyclic pattern to the respective muscle groups in order to improve muscle coordination. The targeted muscle groups used to control the system were the Hamstring (HS), Tibialis Anterior (TA), Quadriceps (QC), Gastrocnemius Lateralis (GL) of the leg on the affected side. Using the system, 20 30-minutes sessions were conducted with chronic stroke survivors (n=10) at frequency of 2-4 sessions per week. Clinical assessment scores, namely FMA_LE, BBS and 6MWT were calculated before the first session and after the completion of 20 sessions. All the assessment scores showed significant improvement after using the system; FMA_LE(P=0.0244), BBS(P=0.0156), 6MWT(P=0.0112), and SI (P=0.0258), showing that the EMG-driven NMES cycling system provides effective rehabilitation for stroke survivors in terms of muscle strength and balance.