# Early Predictability of Grasping Movements by Neurofunctional Representations: A Feasibility Study
## 2 Methods
### Data Processing and Epoching  
The EEG signal had 31 channels and was processed separately for each subject using the Toolbox EEGLAB [33] for MATLAB (Vers. R2020a, The MathWorks Inc., Natick, MA, USA). The raw data were notch- (50 Hz) and bandpass-filtered using the finite impulse response filter. To investigate possible influences of frequency bands on the classification results, the bandpass filtering was performed using the combined alpha and beta sub-bands of the waking state (\(f_{\text{a-}\beta}\) = 8-30 Hz), as well as using the whole EEG signal spectrum covering sub-bands from delta up to gamma (\(f_{\text{S-}\gamma}\) = 0.5-100 Hz). To separate actual brain signals from interference signals resulting from muscle and eye artifacts and superimposed brain signals, a linear decomposition was performed using independent component analysis [33]. Confining events, such as \"initial movement\" and \"object grasped\", were identified manually with the aid of synchronized videos, according to the protocol of [PERSON] et al. [18], which allowed us to separate the reaching phase from the action phase (Figure 2). In all ADLs, the reaching phase had a duration of at least 800 ms, which resulted in the earliest occurring MGA interval of 488-520 ms [18]. For the discrimination, two data epochings of different durations were chosen within the limits of when neuronal representations should already be reliably detectable [23], but at which point the hand is not yet preparing for the respective grasping posture, i.e., fulfilling a pre-shaping for the grasping object. Hence, discrimination must be accomplished before the MGA is reached; the duration \(T_{1}\) was thus defined as 500 ms after the initial move  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Var.** & **No.** & **Grip Pattern** & **ADLs** \\ \hline \multirow{6}{*}{G6} & 1 & Power prismatic grip & 1, 9, 10 \\  & 2 & Power prismatic adducted thumb & 2, 5 \\  & 3 & Power lateral pinch & 3, 12 \\  & 4 & Power circular grip & 4, 6, 14, 15 \\  & 5 & Key pinch grip & 7, 16 \\  & 6 & Precision & 8, 11, 13 \\ \hline \multirow{6}{*}{C5} & 1 & Power prehensile lateral pinch & 3, 12 \\  & 2 & Power prehensile circular sphere & 4, 15 \\ \cline{1-1}  & 3 & Power prehensile prismatic wrap & 1, 9 \\ \cline{1-1}  & 4 & Precision circular sphere & 6, 14 \\ \cline{1-1}  & 5 & Precision prismatic thumb 3 finger & 8 \\ \hline \multirow{6}{*}{F4} & 1 & Power palm & 1, 4, 9, 15 \\  & 2 & Intermediate side & 7, 10, 11, 13, 16 \\ \cline{1-1}  & 3 & Precision pad 2-5 thumb abduction & 6, 14 \\ \cline{1-1}  & 4 & Precision pad 2–5 thumb adduction & 3, 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grasping posture categorizations for multiarticular prosthetic hands (G6), and for the grasping taxonomies according to [PERSON] [29] (C5) and [PERSON] [30] (F4).  
ment, which is in line with grasping phase latencies [34]. A long epoch \(E_{L}\) resulted from the total sum of two equal lasting time windows, \(T_{2}\) and \(T_{3}\), prior to the initial movement (\(E_{L}=T_{1}+T_{2}+T_{3}=1500\) ms [\(-1000\) ms \(\leq E_{L}\leq 500\) ms]). To determine whether an earlier discrimination might allow a prosthetic device more time for hand preparation execution for grasping tasks, a short epoch \(E_{S}\) was defined prior to the initial movement (\(E_{S}=T_{2}+T_{3}=1000\) ms [\(-1000\) ms \(\leq E_{S}\leq 0\) ms]).