# Are Electromyography Data a Fingerprint for Patients with Cerebral Palsy (CP)?
Kinematic indices such as the Gait Deviation Index (GDI) [12] and the Gait Profile Score (GPS) [13] are two measures of gait quality whose applicability in the assessment of gait with CP has already been reported in the literature [14; 15; 16; 17]. The Kerpape-Rennes EMG-based Gait Index [18], referred to as the EMG Deviation Index (EDI) in this study, and the EMG Profile Score (EPS) [19] are two EMG-based measures calculated using the same methodology as the GDI and GPS, respectively. EDI quantifies global muscle activity based on the Euclidean distance between a patient's EMG pattern and that of typically developing (TD) subjects using principal component analysis. Scores equal to or above 100 indicate normal gait, with a decrease of 10 points indicating a deviation of one standard deviation (SD) from TD. Additionally, to assess deviations from the norm for individual muscle groups independently, the EPS was calculated based on the mean of the root mean square error (RMSE) for each muscle during the gait, providing a score without units. While these are validated measures [20], there is no study on the use of EMG indices in the CP population. Although EDI and EPS can describe biomechanics by considering patterns, they are relatively weak in presenting the degree of motor involvement of the patient. To address this limitation, [PERSON] et al. introduced the Walking Dynamic Motor Control Index (Walk-DMC) to clinically assess the effect of altered neuromuscular control on treatment outcomes in patients with CP [21]. A higher DMC suggests better motor function, with 100 being the average DMC for TD individuals and 10 points representing one standard deviation from typical development outcomes. The observed DMC was significantly associated with the response to treatment, second only to the pre-treatment level of GDI. However, the treatment in their study included various interventions such as surgery, physical therapy, and selective dorsal rhizotomy. Additionally, the influence of DMC level on individuals with similar gait quality prior to treatment was not investigated. Therefore, it remains unclear how DMC can be helpful for predicting the results of surgery in patients with CP. The primary aim of this study was to investigate the effect of SEMLS on the EMG and gait kinematics of patients with CP. To ensure more homogeneous surgical management across the cohort, we focused on patients who underwent soft tissue surgery on their shank and foot muscles, in addition to potentially other more proximal surgeries. We also evaluated the applicability of EMG indices as global measures for assessing surgical outcomes in the CP population. To the best of our knowledge, this is the first study to measure EDI and EPS in patients with CP. Additionally, we assessed the relationship between motor control (DMC) and surgery outcomes for subgroups with the same level of GDI. Our first hypothesis is that while EMG patterns, as a biomechanical aspect of muscle activity, will change following surgery, motor control remains stable and independent of specific gait patterns. Our second hypothesis is that DMC is resilient to biomechanical corrections after surgery and that pre-treatment DMC levels could potentially serve as a predictor for improvement following SEMLS in CP patients. These hypotheses are clinically significant because they could offer insights into how EMG and motor control measures can be used to predict surgical outcomes, ultimately guiding more personalized treatment strategies for patients with CP.