Mental states
A fourth area where BCI assistive technology can benefit from recent research is in the recognition of the user's mental states (mental workload, stress level, tiredness, attention level) and cognitive processes (awareness to errors made by the BCI), which could facilitate interaction and reduce the user's cognitive effort by making the BCI assistive device react to the user. This is again another aspect of self-adaptation: for instance, in case of high mental workload or stress level, the dynamics and complexity of the interaction will be simplified or it will trigger the switch to stop brain interaction and move on to muscle-based interaction (see above). As another example, in the case of detection of excessive fatigue, the mobile robot would take over complete control and move autonomously to its base station close to the user's bed. Pioneering work in this area deals with the recognition of mental states (such as mental workload, Kohlmorgen et al., 2007; attention levels, Hamadicharef et al., 2009; and fatigue, Trejo et al., 2005) and cognitive processes (such as error-related potentials, Blankertz et al., 2003; Ferrez and Millán, 2005, 2008a,b; and anticipation, Gangadhar et al., 2009) from EEG. In the latter case, Ferrez and Millán (2008a,b) have shown that errors made by the BCI can be reliably recognized and corrected, thus yielding significant improvements in performance.
Also, as mentioned before, mental states can provide useful information to estimate the reliability of the individual channels. For instance, in the case of a high attention level, we could assign a large weight to the EEG channel, while this weight would be small in the case of high mental workload. Also, repetitive error-related potentials should reduce the weight of the channels that mainly contributed to the estimation of the user's intent.