The radio frequency spectrum is occupied with authorized and unauthorized user activities which might include noise and interference. Detection of signals-of-interest (SOI) and differentiation from non-signals-of-interest (NSOI) are therefore crucial for frequency use management. There is a wide variety of signals in a desired radio spectrum band, which leads to the application of Signal Intelligence (SIGINT) to detect and identify signals in real-time. In this paper, we study the problem of non-Gaussian signal detection when the receivers are configured with a large number of antennas (or the massive antenna regime). First, we investigate the performance of signal detection with massive MIMO when the transmitted signals are generated from a Gaussian distribution. For the detection of Gaussian signals, we consider the Neyman-Pearson (NP) detector. Then, we focus on the performance of non-Gaussian signal detection with massive MIMO, which is one of the main objectives of this paper. We show that the NP detector gives poor performance for non-Gaussian signals in low signal-to- noise-ratio (SNR). Therefore, we propose to use a bispectrum detector, which contains the Gaussian noise and reveals the non-Gaussian information that exists in the signal. We present the theoretical analysis for asymptotic behavior of Probability of False Alarm (PFA) and Probability of Detection (PD) when the transmitter sends Gaussian and non-Gaussian signals. We show the performance of signal detection (for both Gaussian and non-Gaussian signals) as a function of the number of antennas and sampling rate. We also obtain the scaling behavior of the performance in the massive antenna regime.