Target detection during serial visual presentation tasks is an active research topic in the brain-computer interface (BCI) community as this type of paradigm allows to take advantage of event-related potentials (ERPs) through electroencephalography (EEG) recordings to enhance the accuracy of target detection. The detection of brain evoked responses at the single-trial level remains a challenging task and can be exploited in various applications. Typical non-invasive BCIs based on event-related brain responses use EEG. In clinical settings, brain signals recorded with magnetoencephalography (MEG) can be advantageously used thanks to their high spatial and temporal resolution. In this study, we address the problem of the relationships between behavioral performance and single-trial detection by considering a task with different levels of difficulty. We consider images of faces with six different facial expressions (anger, disgust, fear, neutrality, sadness, and happiness). We consider MEG signals recorded on ten healthy participants in six sessions where targets were one of the six types of facial expressions in each session. The results support the conclusion that a high performance can be obtained at the single-trial level (AUG = 0.903±0.045), and that the performance is correlated with the behavioral performance (reaction time and hit rate).