Objective: Localization and mapping of seizure-generating brain tissue, i.e., seizure onset zone (SOZ) is essential to ensure an excellent patient outcome after surgical resection. The clinical approach is to record spontaneous seizures with intracranial EEG (iEEG) and determine SOZ. However, this practice is burdened by inter-patient variability, temporal variability, time-consuming data annotation, and long and variable waiting period for seizures to happen. Approach: Here, we use data from intracranial monitoring of 28 patients with neocortical focal epilepsy. Kurtosis, complexity, activity, mobility, mean, median, min, max, peak to peak, variance, standard deviation, root mean square, and interquartile were extracted as features from the time domain in two frequency bands (12-55 Hz and 55-80 Hz). The features were extracted from segments of inter-ictal iEEG from 8962 channels and tested by Wilcoxon rank sum test with Bonferroni correction of alpha to compare if mean of the feature differs in SOZ versus non-SOZ in each patient individually. Results: From all features, kurtosis, maximum, minimum, peak to peak, standard deviation, root mean square, variance, interquartile shown consistent differences between SOZ and non-SOZ channels across patients (p<0.0004). Conclusion: We analyzed several iEEG time domain features and we found features that significantly differ for data recorded from SOZ channels in most of the dataset with the same trend across patients. Such features can help to automatically differentiate between SOZ and non-SOZ electrodes and a combination of multiple features can yield better classification performance to discover epileptic foci using inter-ictal data without waiting for seizure to be recorded.