Objective:　Validate　independent　component　analysis　(ICA)　for　removal　of　EMG　contamination　from　EEG,　and　demonstrate　a　heuristic,　based　on　the　gradient　of　EEG　spectra　(slope　of　graph　of　log　EEG　power　vs　log　frequency,　7-70　Hz)　from　paralysed　awake　humans,　to　automatically　identify　and　remove　components　that　are　predominantly　EMG.　Methods:　We　studied　the　gradient　of　EMG-free　EEG　spectra　to　quantitatively　inform　the　choice　of　threshold.　Then,　pre-existing　EEG　from　3　disparate　experimental　groups　was　examined　before　and　after　applying　the　heuristic　to　validate　that　the　heuristic　preserved　neurogenic　activity　(Berger　effect,　auditory　odd　ball,　visual　and　auditory　steady　state　responses).　Results:　(1)　ICA-based　EMG　removal　diminished　EMG　contamination　up　to　approximately　50　Hz,　(2)　residual　EMG　contamination　using　automatic　selection　was　similar　to　manual　selection,　and　(3)　task-induced　cortical　activity　remained,　was　enhanced,　or　was　revealed　using　the　ICA-based　methodology.　Conclusion:　This　study　further　validates　ICA　as　a　powerful　technique　for　separating　and　removing　myogenic　signals　from　EEG.　Automatic　processing　based　on　spectral　gradients　to　exclude　EMG-containing　components　is　a　conceptually　simple　and　valid　technique.　Significance:　This　study　strengthens　ICA　as　a　technique　to　remove　EMG　contamination　from　EEG　whilst　preserving　neurogenic　activity　to　50　Hz.　(c)　2015　International　Federation　of　Clinical　Neurophysiology.　Published　by　Elsevier　Ireland　Ltd.　All　rights　reserved.