is used to translate raw data into the imagined speech content. Processing is also intended to occur in real-time—the information is processed as it is recorded, which allows for near-simultaneous viewing of the content as the subject imagines it. Decoding Presumably, “thinking in the form of sound” recruits auditory and language areas whose activation profiles may be extracted from the EEG, given adequate processing. The goal is to relate these signals to a template that represents “what the person is thinking about”. This template could for instance be the acoustic envelope (energy) timeseries corresponding to sound if it were physically uttered. Such linear mapping from EEG to stimulus is an example of neural decoding. A major problem however is the many variations that the very same message can have under diverse physical conditions (speaker or noise, for example). Hence one can have the same EEG signal, but it is uncertain, at least in acoustic terms, what stimulus to map it to. This in turn makes it difficult to train the relevant decoder. This process could instead be approached using higher-order (‘linguistic’) representations of the message. The mappings to such representations are non-linear and can be heavily context-dependent, therefore further research may be necessary. Nevertheless, it is known that an 'acoustic' strategy can still be maintained by pre-setting a “template” by making it known to the listener exactly what message to think about, even if passively, and in a non-explicit form. In these circumstances it is possible to partially decode the acoustic envelope of speech message from neural timeseries if the listener is induced to think in the form of sound. Challenges In detection of other imagined actions, such as imagined physical movements, greater brain activity occurs in one hemisphere over the other. This presence of asymmetrical activity acts as a major aid in identifying the subject's imagined action. In imagined speech detection, equal levels of activity commonly occur in both the left and right hemispheres simultaneously. This lack of lateralization demonstrates a significant challenge in analyzing neural signals of this type. Another unique challenge is a relatively low signal-to-noise ratio (SNR) in the recorded data. An SNR represents the amount of meaningful signals found in a data set, compared to the amount of arbitrary or useless signals present in the same set. Artifacts present in EEG data are just one of many significant sources of noise. To further complicate matters,