The concept of "monitoring" refers to our ability to control our actions on-line. Monitoring involved in speech production is often described in psycholinguistic models as an inherent part of the language system. We probed the specificity of speech monitoring in two psycholinguistic experiments where electroencephalographic activities were recorded. Our focus was on a component previously reported in nonlinguistic manual tasks and interpreted as a marker of monitoring processes. The error negativity (Ne, or error-related negativity), thought to originate in medial frontal areas, peaks shortly after erroneous responses. A component of seemingly comparable properties has been reported, after errors, in tasks requiring access to linguistic knowledge (e.g., speech production), compatible with a generic error-detection process. However, in contrast to its original name, advanced processing methods later revealed that this component is also present after correct responses in visuomotor tasks. Here, we reported the observation of the same negativity after correct responses across output modalities (manual and vocal responses). This indicates that, in language production too, the Ne reflects on-line response monitoring rather than error detection specifically. Furthermore, the temporal properties of the Ne suggest that this monitoring mechanism is engaged before any auditory feedback. The convergence of our findings with those obtained with nonlinguistic tasks suggests that at least part of the monitoring involved in speech production is subtended by a general-purpose mechanism.

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Research on the neural basis of language processing has often avoided investigating spoken language production by fear of the electromyographic (EMG) artifacts that articulation induces on the electro-encephalogram (EEG) signal. Indeed, such articulation artifacts are typically much larger than the brain signal of interest. Recently, a Blind Source Separation technique based on Canonical Correlation Analysis was proposed to separate tonic muscle artifacts from continuous EEG recordings in epilepsy. In this paper, we show how the same algorithm can be adapted to remove the short EMG bursts due to articulation on every trial. Several analyses indicate that this method accurately attenuates the muscle contamination on the EEG recordings, providing to the neurolinguistic community a powerful tool to investigate the brain processes at play during overt language production.