This study was designed to investigate corticospinal excitability of lumbar muscles using transcranial magnetic stimulation (TMS) in patients with chronic low back pain and correlate this with self-rated measures of disability and pain. Twenty-four patients with chronic low back pain and 11 healthy control subjects were used in this study. TMS was delivered through an angled double-cone coil, with its cross-over on the vertex and a posterior-to-anterior current flow in the brain. Electromyographic (EMG) recordings were made from erector spinae (ES) muscles at the fourth lumbar level. Motor cortical excitability was assessed using motor threshold (MTh) for motor evoked potentials (MEPs) and threshold for silent period (SP) during facilitation of the back muscles. Latency, duration, and area of MEPs and SPs were also measured. The latency, duration, and size of MEPs and SPs did not differ between the left and right ES muscles in either the patients or the control subjects and also did not differ between the patients and the control subjects. However, there was a significantly higher MTh and threshold for the SP in the patients as compared with the control subjects; the full significance of this requires further investigation. Interestingly, there was a positive correlation between the self-rated measure of disability (the Oswestry Disability Index score) and both the MTh and the threshold for the SP in the patients. There was also a positive correlation between the self-rated index of back pain and the threshold for the SP in the patients. This finding of an association between clinical and neurophysiologic measures reinforces the need for further research to establish the clinical relevance of these rises in MTh and SP threshold. In summary, this study has revealed that corticospinal excitability, driving ES muscles close to the site of pain, is lowered in patients with chronic low back pain.

Objective: Several features of the surface electromyography (sEMG) signal are related to muscle activity and fatigue. However, the time-evolution of these features are non- stationary and vary between subjects. The aim of this study is to investigate the use of adaptive algorithms to forecast sMEG feature of the trunk muscles. Methods: Shallow models and a deep convolutional neural network (CNN) were used to simultaneously learn and forecast 5 common sEMG features in real-time to provide tailored predictions. This was investigated for: up to a 25 second horizon; for 14 different muscles in the trunk; across 13 healthy subjects; while they were performing various exercises. Results: The CNN was able to forecast 25 seconds ahead of time, with 6.88% mean absolute percentage error and 3.72% standard deviation of absolute percentage error, across all the features. Moreover, the CNN outperforms the best shallow model in terms of a figure of merit combining accuracy and precision by at least 30% for all the 5 features. Conclusion: Even though the sEMG features are non-stationary and vary between subjects, adaptive learning and forecasting, especially using CNNs, can provide accurate and precise forecasts across a range of physical activities. Significance: The proposed models provide the groundwork for a wearable device which can forecast muscle fatigue in the trunk, so as to potentially prevent low back pain. Additionally, the explicit real-time forecasting of sEMG features provides a general model which can be applied to many applications of muscle activity monitoring, which helps practitioners and physiotherapists improve therapy.