We introduce two novel methods for the estimation of muscle excitation from surface electromyograms (EMGs), the so called cumulative motor unit activity index (CAI) and robust CAI (rCAI). Both methods aim to remove the detected motor unit action potential (MUAP) contributions from EMG but do not assess the individual motor unit spike trains. Instead, they directly estimate the cumulative motor unit spike train (CST). We compared the methods with the spatially averaged root-mean-square (RMS) envelope of the EMG signals and with the CST, estimated by the previously introduced convolution kernel compensation (CKC) method. The tests on synthetic EMG with known muscle excitation profiles demonstrated superior accuracy of newly introduced methods. In the case of 64 EMG channels and 20-dB noise, the RMS, CAI, rCAI, and CKC estimators, calculated on 0.125-s-long signal epochs, yielded the normalized RMS error (NRMSE) of 14.5% ± 2.8%, 4.4% ± 3.2%, 4.1% ± 1.8%, and 6.3% ± 4.6%, respectively. In the experimental signals from wrist extensors and flexors, the RMS, CAI, rCAI, and CKC estimations were compared to exerted muscle force. When calculated on 0.125-s-long signal epochs, they yielded the NRMSE of 11.2% ± 3.5%, 8% ± 5.6%, 10.7% ± 6.8%, and 9.0% ± 4.9%, respectively. Therefore, the newly introduced methods exhibit accuracy that is comparable to at least 200-times slower CKC method.