## 2 Materials and Methods
### Independent Component Analysis and IC Weight Based Topological Plot  
ICA is a blind source separation method used to solve the cocktail party problem. The following assumptions must be met when applying the ICA model: (1) the measured signals are linearly mixed independent signals, and (2) the sources are non-Gaussian distributed signals [33]. It was assumed that the best signal location for estimating motion  
Figure 2: Thirty-two-channel multiarray electrode forearm gloves and the Perception Neuron Motion Capture System. The blue point on each location represents the sensor of the perception neuron.  
was the primary active muscle associated with the motion. The ICA was applied to the EMG signal of the forearm to identify the active muscles associated with each action. The ICA can be expressed as:  
\[x=As, \tag{1}\]  
where \(x\) is the EMG data matrix obtained from measurements at \(M\times N\) time point, and \(M\) represents the number of channels. \(A\) is the mixing matrix of \(M\times M\). \(s\) is the \(M\times N\) matrix of the source signals, which are assumed to be independent of each other.  
It is difficult to determine the IC matrix \(s\) or mixing matrix \(A\) when they are unknown. Thus, to separate the mixed EMG signals, a separation matrix \(W\) must be obtained. Once matrix \(W\) is calculated, the approximation of the source signal \(\hat{s}\) is given by Equation (2):  
\[\hat{s}=Wx. \tag{2}\]  
Figure 3 illustrates the flow of the ICA process. First, the EMG data were loaded in MATLAB and imported into EEGLAB. As a preprocessing step, band-pass filtering between 3 and 500 Hz was performed to reduce noise and artifacts. Adaptive component analysis (AMICA) was selected owing to the advantages mentioned by [PERSON] et al. [34]. More importantly, AMICA exhibits a better separation performance than other ICA methods for very short data lengths [35].  
After decomposing the thirty-two channels of EMG signals with AMICA, the IC signals were rectified and low-pass filtered with a cut-off frequency of 3 Hz [36]. The filtered IC signals were used to extract muscle synergy, and the weights of the ICs were converted into a topological plot for analysis. Figure 3 shows a topological plot of the IC weights for the subjects during middle finger flexion. As shown in Figure 3, the IC weights were highly localized during single-finger flexion, indicating that the muscles in this location experienced intense activity during this action. Channels with low signal energies are typically muscles that are not associated with action. Therefore, by post-analysis of the IC weights, the topological plot of the IC weights can be compared with muscle anatomy data.  
Figure 3: Independent component analysis was performed on the raw EMG data. After independent component analysis, IC signals were rectified and filtered at a cut-off frequency of 3 Hz by a low-pass filter. The weights of IC were then converted into a topological plot. The yellow locations were assigned high weight values, and the blue locations were assigned low weight values. A higher weight value indicates greater exertion in the muscles in that area during the particular motion.