# Effect of Kinesio Taping on Hand Sensorimotor Control and Brain Activity
## 2 Materials and Methods
EMG data were bandpass filtered at 10-400 Hz for noise removal, followed by signal rectification and a 10 Hz linear envelope process for data smoothing. Force signals were low-pass filtered at 40 Hz to remove noise. During the test, the examiner pressed a customized button to produce a beep sound. The participants were instructed to grip the dynamometer promptly when hearing the sound. This process was repeated five times. The customized button sent a transistor-transistor logic (TTL) pulse to the signal acquisition system when pressed, which was stored as a time mark/event along with the force and EMG signals. * Force control and corticomuscular functional connectivity  
Participants were asked to perform the grip force control task to maintain grip force at 20% of MVC for 30 s while electroencephalography (EEG) and EMG were recorded simultaneously to determine functional connectivity between motor cortex and muscles. A Jamar dynamometer (G200; Biometrics Ltd., Newport, UK) and a pair of surface EMG  
Figure 1: The 3 Kinesio taping conditions. From left to right: (1) taping with 20% additional tension (taping20), (2) taping with neutral tension (tapingN), and (3) without taping (control). electrodes (on finger flexor muscle belly, inter-electrode distance = 2 cm) with a signal acquisition system (CED Power1401 mk II) were used to record and display grip force and muscle activity data on the computer screen. Identical force and EMG signal processing procedure was conducted as described above in the \"Reaction Time\" section. In addition, during the force control task, EEG of the bilateral sensorimotor cortex was recorded (actiCAP; Brain Products GmbH, Gilching, Germany). Conductive gel was added for each electrode and the impedance was monitored throughout the experiment to ensure good signal quality (<10 k\(\Omega\)). The sampling rate for EEG data collection was 1000 Hz. All data were analysed using MATLAB software (MathWorks, Natick, MA, USA). The performance of joint position sensing was determined by calculating the difference between the targeted joint angle (15\({}^{\circ}\)) and the actual joint angle participants generated. The value was defined as the angular error, and the values of five measurements were averaged for statistical analysis. The performance of FS was determined similarly as JPS. We calculated the difference between the targeted grip force (20% maximal voluntary contraction--MVC) and the actual force level participants generated. The value was defined as the force error, and the values of five measurements were averaged for statistical analysis. The performance of reaction time was determined by calculating the time delay between the time mark created by pressing the customized button and the onset of muscle activation and force. To determine the onset of muscle activation, we first defined baseline EMG activity and baseline force level by calculating the average and standard deviation of the rectified EMG and force signals 1 s before the time mark. The onset of muscle activation was defined as EMG activity that exceeds the averaged plus 3 standard deviations of the baseline activity; the onset of muscle contraction was defined as force output that exceeds the averaged plus 3 standard deviation of the baseline level. Reaction time can be further divided into premotor time and electromechanical delay. Premotor time was determined by calculating the time difference between the time mark and the onset of EMG; electromechanical delay was determined by calculating the time difference between the onset of EMG and the onset of muscle force. The RT, premotor time and electromechanical delay were calculated and averaged across the 5 trials for statistical analysis. Force control performance during grip force control task was determined by the difference between the participants generated and targeted grip force during the 30 s grip task.