5. Conclusion

motor actions. By extension, there is thus also the implicit subordination of this strategy to ontological demands, that is, actions undertaken for the good of the organism. Hence, they entail more than the execution of actions, a traditional objective performed in BCI, and so also include the formulation of organismal goals. For BCI therapy, accordingly, this formulation of representational content will be a critical objective for therapeutic strategy, encompassing

For the motor image, notably, it is apparent that representational content is articulated at multiple levels, built upon a dynamical syntax that acquires semantic content by binding representational, feature-specific, i.e., simulated, forms together. Distinguishing the level of functional disturbance therefore is an objective needed in order to administer therapy adequately. Yet, in decoding approaches that have evolved to date, the central technical concern is that of classification, that is, the mapping of a brain state in its activity patterns to an external object or event. Older techniques like mass univariate analysis sequentially evaluate brain regions for a specific activity at a specific location. Measuring covariance between multiple single units is thereby taken as a diagnostic feature of how select images are encoded, like the activation of long regions of the occipital cortex on presentation of a single object. Discerning the underlying structure of the representational content, therefore, remains unknown and an

In more recently developed multivariate classification approaches, previously determined activity patterns are linked to specific object features that can assess or predict the content of a specific activity. While this approach can be employed without the presentation of an object, many potential representations are left unclassifiable. These limitations have led to current model-based classification approaches that use models to predict patterns not elicited by training data. Such promising efforts seek to extract greater information content from patterned activity than obtained from linear mapping strategies alone. These latter strategies are likely to be strengthened by expanding the capacity to extract information content by combining deep neural learning with wavelet analysis, like that seen in Chapter 2. Hence, they can be expected to extrapolate from syntactical structure to simulated actions; that is, they will be better capable of extracting how meaning is formulated in the assembly of simulated executable sequences. Enlisting technological methods that can optimize distinctions between signal and noise, like that of Chapter 3, can be expected to further this capacity and particularly evident where discerning the syntactical expression of dynamical architectures is key, in order

Crucially, issues of deciphering multilevel representational content and formulating semantic architectures for action-oriented goal seeking enter into primitive motor assembly levels, where, for example, the capacity for assimilating meaningful content is impaired. These will require new therapeutic paradigms where BCI may be one among several adjunct approaches used together to restore the functional modalities needed for simulated motor articulation. In practice, these paradigms will need to recreate the multilevel, brain-based operation that occurs in motor planning, like that used in sensory motor coupling. Models of such therapy, for example, are

diagnosis and therapy, and dictated at syntactic and semantic levels.

to communicate the motor image, as in Chapters 6, 7, and 8 of this text.

presented in Chapters 4 and 5 of the current volume.

obstacle to focal BCI therapy [40].

8 Evolving BCI Therapy - Engaging Brain State Dynamics

Novel insights into the multilevel construction of representational content promise a new phase of BCI therapy, embracing not only the restoration of executable actions but also the formulation of the motor image and motor planning sequences. Built upon the fundamentally distinct syntactic and semantic architecture of dynamic cognition, new forms of therapy will undertake to simulate the brain's approach to information transfer and to attain goal-directed planning. These will likely entail enhanced information extraction in classification and predictive technology, dynamically structured command and communication methodologies, and integrative, mixed-mode BCI approaches that can restructure motor semantics.
