TO THE EDITOR: A major constraint in the analysis of motor unit activity in humans is that relatively few active motor units can be detected concurrently due to the high selectivity of needle electrodes commonly used for electromyographic (EMG) recordings. Furthermore, the accurate decomposition of intramuscular EMG signals is usually not possible at high muscle forces due to the many motor unit action potentials that overlap in time. De Luca and Hostage (2010) appear to have developed a noninvasive solution to these problems. They assert that it is possible to decompose surface EMG signals and detect with confidence the concurrent activity of 20–30 motor units at contractions up to the maximal force. Moreover, their decomposition method requires only four surface EMG channels. The main difficulty associated with processing surface EMG recordings is the greater number of overlapping action potentials than that in intramuscular EMG. For example, the second discharges of motor units# 17 to# 24 in their Fig. 10 occur at a similar instant in time and thus the action potentials of these eight motor units must partially overlap. Indeed, close inspection of Fig. 10 suggests that even more action potentials overlap at this point in time and at other times throughout the recording, as can also be appreciated from the interference surface EMG signal (see their Fig. 8). This is not surprising given the many action potentials that contribute to the surface EMG signal at each instant in time. Consequently, relatively few action potentials of each unit are detected as isolated in time from those of other units (Nawab et al. 2010). In principle, the overlapping action potentials can be disentangled by estimating the likelihood of each event in the multiple possible sets of matching templates. However, the global optimization of overlapping action potentials is a nondeterministic polynomial–type hard problem that cannot be solved by polynomial complexity algorithms (Ge et al. 2010). The maximal number of overlapping potentials that can be distinguished in each segment is practically limited by the size of the search space. A potential solution is to limit the search space to the most likely combinations and to include some constraints on the estimated discharge pattern (Nawab et al. 2010), but the likelihood of finding the correct solution decreases with the size of the search space (Ge et al. 2010). Consequently, the results achieved by De Luca and Hostage (2010) to decompose surface EMG signals are impressive. Such a remarkable achievement, however, requires an extremely convincing validation of the decomposition results. The results of the study by De Luca and Hostage (2010) were tested with an approach called the reconstruct-and-test procedure (Nawab et al. 2010), which they suggest is superior to the two-sensor method (Mambrito and De Luca 1984). The new validation approach involves generating a synthetic signal from the decomposed trains of action potentials and reapplying the same decomposition method to this synthetic signal to which noise is added with a power similar to that of the residual in the first decomposition (see their Fig. 9 and detailed description by Nawab et al. 2010). The accuracy in the identification of each train of action potentials is then estimated in the same way as in the two-sensor method; that is, as the percentage of action potentials that are identified by the two decompositions with respect to the total. We beg to differ that such an approach provides a valid demonstration of the accuracy of the decomposition algorithm. One of the problems with the reconstruct-and-test procedure is that missed discharges do not influence the index of …