Introduction: MEG has the advantage of both a high temporal and a spatial resolution in measuring neural activity. The event-related field (ERF) have been extensively explored in psycholinguistic research. For example, the N400m was found to be sensitive to semantic violations (Helenius, 2002). On the other hand, induced oscillatory responses of the EEG and MEG during langauge comprehension are less commonly investigated. Oscillatory dynamics have been shown to also contain relevant information, which can be measured amongst others by time-frequency (TF) analyses of power and /or coherence changes (Bastiaansen & Hagoort, 2006; Weiss et al., 2003). In the present study we explicitly investigate whether there is a (signal-analytic) relationship between MEG oscillatory dynamics (notably power changes) and the N400m. Methods: There were two types of auditory sentences, in which the last words were either semantically congruent (C) or incongruent (IC) with respect to the sentence context. MEG signals were recorded with a 151 sensor CTF Omega System, and MRIs were obtained with a 1.5 T Siemens system. We segmented the MEG data into trials starting 1 s before and ending 2 s after the onset of the critical words. The ERFs were calculated by averaging over trials separately for two conditions. The time frequency representations (TFRs) of the single trials were calculated using a Wavelet technique, after which the TFRs were averaged over trials for both conditions. A cluster-based random permutation test (Maris & Oostenveld, 2007) was used to assess the significance of the difference between the two conditions, both for the ERFs and the TFRs. In order to characterize the relationship between beta power (see results) and N400m, we performed a linear regression analysis between beta power and N400m for the sensors that showed significant differences in ERFs or TFRs between the two conditions. In the end, a beamforming approach [Dynamic Imaging of Coherent Sources (DICS)] was applied to identify the sources of the beta power changes. Results: The ERF analysis showed that approximately between 200ms and 700ms after the onset of the critical words, the IC condition elicited larger amplitudes than the C condition over bilateral temporal areas, with a clear left hemisphere preponderance (Fig. 1A). Statistical analysis revealed significant differences over the left temporal area (Fig. 1B). In a similar time window (200 - 700ms), a beta power suppression (16 - 19 Hz) was found only for the IC condition, but not for the C condition (Fig. 2A). The statistical analysis of the beta power difference between the two conditions revealed a significantly lower beta power for the IC than C condition over left temporal cortex (Fig. 2B). The comparable topographies for N400m and beta differences suggest a relationship between these two effects. In order to evaluate this relationship, we performed a linear regression between beta power and N400m for both IC and C conditions in both the post-stimulus time window (200 - 700ms) and the pre-stimulus time window (-600 - -200ms). In the time window of 200 - 700ms, we found a positive linear regression between beta power and N400m for the IC condition (R = .32, p = .03) but not for the C condition (p = .83). For the IC condition, we found that the lower the beta power, the lower the N400m amplitude. In the time window of -600 - -200ms, the C condition showed a positive linear regression between beta power and N400m (R = .27, p = .06), but the IC condition did not show this (p = .74). The source modeling analysis allows us to estimate the generators of the beta suppression for the IC relative to C condition. The source of the beta suppression (around 18 Hz) within 200 - 700 ms was identified in the left inferior frontal gyrus (LIFG, BA 47) (Fig. 3). Conclusions: The ERF difference between the two conditions is consistent with previous MEG studies. However, it is the first time that the beta power suppression is related with the amplitude of the N400m. When the input is highly predictable (C condition), the lower beta power in the pre-stimulus interval predicts a better performance (smaller N400m); while the low predictability (IC condition) of the input produced an association between the N400m and the beta power in the post-stimulus interval. Moreover, the generator of the beta suppression was identified in the LIFG, which has been related to semantic unification (Hagoort, 2005). Together with other studies on the role of beta oscillations across a range of cognitive functions (Pfurtscheller, 1996; Weiss, 2005; Hirata, 2007; Bastiaansen, 2009), we propose that beta oscillations generally reflect the engagement of brain networks: a lower beta power indicates a higher engagement for information processing. 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