# Somatosensory Mismatch Response in Patients with Cerebral Palsy
## 2 Methods
### Experimental Procedure and Data Analysis
#### 2.2.3 Data Processing  
Data preprocessing was performed using the EEGlab v2023.0 toolbox running on Matlab R2020a. Data were first band-pass-filtered between 1 and 30 Hz and then re-referenced to the common average. After re-referencing, the three sets of 500 trials were concatenated together. The data were examined for possible bad channels using Kurtosis statistics with a  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Participant** & **Gender** & **Age** & **GMFCS** & **MACS** & **MACS** & **Diagnosis** \\  & & **Year** & & **L** & **R** & \\ \hline P01 & Female & 23 & I & 4 & 1 & Unilateral CP \\ \hline P02 & Male & 45 & III & 1 & 1 & Bilateral CP \\ \hline P03 & Male & 39 & III & 3 & 3 & Bilateral CP \\ \hline P04 & Male & 36 & IV & 4 & 4 & Bilateral CP \\ \hline P05 & Female & 53 & IV & 2 & 2 & \multicolumn{2}{c}{Atoxic CP} \\ \hline P06 & Female & 47 & II & 2 & 4 & Unilateral spastic CP \\ \hline P07 & Male & 25 & III & 2 & 2 & \multicolumn{2}{c}{Bilateral spastic CP} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic characteristics of enrolled participants and diagnosis classifications of their mobility according to GMFCS and according to their hand’s performance in daily life, i.e., MACS level.  
threshold value of 2. Since no channels were found bad, data from all the channels were used for the analysis. The continuous merged data were then decomposed by independent component analysis (ICA) using the 'runica' function of the EEGlab toolbox. The decomposed data were manually inspected individually, and the non-neuronal originated artifacts such as components related to muscle activity and eye blinks were identified on the basis of their scalp topography and component activity power spectrum, and removed from the data set. Artifact-corrected data were then used to study event-related EEG responses. The data epochs time-locked to standard and deviant events were extracted from \(-200\) to \(800\) ms relative to stimulus onset from the resulting continuous data signals. A baseline correction of \(200\) ms was applied. Event segments with amplitudes larger than \(\pm 120\)\(\mu Volt\) were removed for further analysis. On average, \(1315\) epochs per subject were accepted for further analyses.  
In line with our primary objectives, the key analysis strategy involved determining our ability to extract somatosensory mismatch-related ERP responses. Additionally, we aimed to verify the following assumption in the case of adult patients with CP: the presence of sMMN, elicited at about \(150\)-\(250\) ms after the stimulus onset over the frontcentral regions, as either a negative or positive component [6, 11, 12], followed by LDN, a second component in the difference signal, at an approximate latency of \(350\) ms [25, 40]. This assumption finds support in studies mostly involving healthy young and elderly adults. Therefore, a further statistical test was performed exclusively on the data obtained from patients. Wilcoxon signed-rank tests were performed on standard and deviant responses for each channel within two predefined time ranges, averaged across patients. The selected time ranges were between \(150\)-\(250\) ms for sMMN [12, 41] and \(350\)-\(450\) ms for LDN [24] based on literature where mismatch-related activities are expected. The nonparametric test statistics determined the sum of the ranks of positive differences between the observations in the samples, in this case, the differences between the two traces obtained for frequent and infrequent stimuli.