## 4 Discussion
The deep flexor digitorum profundus and flexor digitorum superficialis muscles correspond to these locations. The former is responsible for flexion motions of the distal joints of the index, middle, ring, and little fingers, whereas the latter mainly acts on the metacarpophalangeal and interphalangeal joints of the flexed fingers. In the extension motion of the index finger, the muscle synergy representing the extension consisted mainly of data around locations 7, 15, 24, and 30 in the electrode positions. These locations corresponded to the extensor digitorum muscle. Therefore, we created a topological plot based on the best model for each subject to investigate the calculated muscle synergies further. Analysis of the topological plot for all subjects demonstrated that they showed similar motor patterns when performing specific movements, despite the fact that each subject's best model had a different number of ICs. Moreover, unique motor activation patterns were confirmed in other joints such as the middle finger, thumb, and wrist. The motion patterns corresponding to the different joints were also confirmed in the topological plot of other joints, such as the middle finger, thumb, and wrist. Compared with the Synergy-MSM, the traditional MSM selects only the two ICs with the highest CC for flexion and extension motions as inputs. In contrast, the proposed ICA-synergy extraction method can afford a higher prediction accuracy by extracting the synergy representing both extensor and flexor muscle groups by selecting deep and superficial muscle locations with a high contribution to the movement according to the movement situation. As shown in Figure 5, the predictive performance of the Synergy-MSM was better than that of the MSM when the two ICs were used as inputs for the model. The MSM and Synergy-MSM use the same ICs, differing only in that the Synergy-MSM extracts muscle synergies using IC and then uses them as inputs for the model. This suggests that the ICA-synergy provides better predictive performance than the ICA-EMG counterparts. As mentioned in the Introduction, human motion may be controlled by a set of low-dimensional excitation primitives, rather than by each muscle. In our opinion, this is precisely why the muscle synergy extracted by NMF can better represent motor commands, and thus enables more accurate prediction. The proposed method is also advantageous for compressing multidimensional data into two input signals, thereby reducing computational costs, which is conducive to real-time control systems. At this stage, to investigate the use of ICA-synergy to predict the maximum performance of a single joint, the IC was obtained using only the motion data of the corresponding joint. Although the IC weights of the thumb and wrist had corresponding movement patterns in the topological plot, the movement patterns of the index and middle fingers were more similar. The reason for this is related to the fact that the dominant muscles of the index and middle fingers are the same group of muscles. This may affect the performance of simultaneous multifinger predictions. Perhaps the use of all types of movement data to derive weights and spherical matrices for ICAs containing multiple motion patterns to calculate the ICs could improve the performance of the proposed method for real-time control and simultaneous multifinger prediction. Compared with recent studies that predict finger movements, the Synergy-MSM exhibits higher performance and better robustness [50; 51]. Knowledge of the structure of the muscle position is crucial for acquiring a better EMG signal. This is a non-negligible burden on users who do not have relevant knowledge. With this in mind, our method uses an array EMG system to acquire EMG signals. The advantage of this is that the use of the array EMG system largely reduces the impact of the user's placement in the sensor. In addition, we used ICA-synergy to identify the activated muscles of each subject and to compress the dimensionality of the input signal.