4. BCI therapy and biological design

represent external input information. This is significant for relating the structure of the network in its connectivity features to a dynamical generation of symbolical structures that

Yet, the generation of symbolical content is not the only consequence of acquiring temporal independence. The manner in which syntactical elements are assembled is also altered. As cyclical patterns it is only through their modular assemble into larger architectures that they can yield representational variation. Significantly, this change offers the immediacy of parallelbased representation. Hence, the role of syntax as representational sign, that is, as in a symbolical, Peircean coding, is itself transformed, linked instead to semantic elements that duplicate through self-organization feature-specific content of the external world [24]. For BCI, information extraction premised on symbolical articulation alone and not accounting for such modular

The complexity and magnitude of dynamical variation encountered in the state space of the brain, moreover, is a capacity amenable to environmental exigencies, in much the manner that field-situated robotic artifacts become amenable to local input by transferring responsivity from programmatic architectures to distributed processing. Here, sensorial input can elicit motor responsivity directly, structuring forms that directly respond to molding stimuli [25].

Some of the essence of this process of feature-specific duplication can be seen in the motor image, a covert action that is a representation of a non-executed action. The concept of the motor image itself evolved from several experimental legacies. Classical observations made by Lashley [26, 27] in a subject with a deafferented limb showed that humans, and animals, were able to generate actions without sensorial input, in contrast to the broadly assumed hypothesis prevalent in the nineteenth century. Later, experiments in monkeys showed that with deafferentation of spinal dorsal motor roots the animals nonetheless could execute pointing movements in all the phases of motion [28]. This indicated that the movement was predetermined centrally. How this was done and how executed became apparent in studies of ongoing motion. Held [29] observed that limb movements in such circumstances usually do not correspond to their expected trajectories, but entail a misreaching followed by progressive compensatory movements. To explain his finding he proposed Von Holst and Mittelstaedt [30] hypothesis that the command for the executed movement was stored as an efference copy, sent to the sensory cortex, where it was then compared with the actual movement undertaken so as to correct the misaligned motions. The experimental observation of misalignment and correction seen experimentally served as evidence of the memorized storage. A corollary of this hypothesis was that self-made motions could be contextualized to the individual who initiated the actions, a conclusion drawn by Frith in his comparator model [31, 32]. This is to say that the comprehension of the actions as those of one's own was a necessary feature of movement; while the actions could be initiated without afferences, they nonetheless required them for motor cognitions in order to be understood as

establish equivalency with external representation, that is, as codes that map content.

3. Feature-specific representation and semantic construction in BCI

assembly reduces structural content, diminishing the capacity for representation.

therapy

6 Evolving BCI Therapy - Engaging Brain State Dynamics

self-executed functions.

Taken together, what is made apparent in analogizing from a linguistic perspective is the strategical implementation of multilevel representational content to structure goal-oriented 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 diagnosis and therapy, and dictated at syntactic and semantic levels.

5. Conclusion

Author details

Denis Larrivee1,2\*

References

\*Address all correspondence to: sallar1@aol.com

2 Mind and Brain Institute, University of Navarra, Spain

Humans. pp. 2018-2023. DOI: 10.1109/SMC.2017.8122915

Development. Oxford: Oxford University Press; 2009

Principles of Brain Dynamics. London: MIT Press; 2013

of Neural Systems and Rehabilitation Engineering. 2010;2010:1-10

1 Loyola University Chicago, USA

Bioengineering. 1973;2:157-180

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

Introductory Chapter: Multilevel Representational Content in BCI Therapy - Extending Syntactic and Semantic…

http://dx.doi.org/10.5772/intechopen.80232

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[1] Vidal J. Toward direct brain-computer communication. Annual Review of Biophysics and

[2] Cong P. Neural interfaces for implantable medical devices: Circuit design considerations for sensing, stimulation, and safety. IEEE Solid States Circuits Magazine. Fall, 2016;48:1-6 [3] Larrivee D. Implantable medical devices and brain attractors: Network modulation and design practice. IEEE Transactions on Systems Man and Cybernetics-Part A Systems and

[4] 2nd International Conference on Neurological Rehabilitation. 2017. Available from: https:// www.allcongress.com/medical-congress/2nd-international-conference-on-neurorehabilitation

[5] Jackson A, Hall TM. Decoding local field potential for neural interfaces. IEEE Transactions

[6] Schoner G. Development as change of system dynamics: Stability, instability, and emergence. In: Spencer J, Thomas MSC, McClelland JL, editors. Toward a Unified Theory of

[7] Friston K. Free energy and global dynamics. In: Rabinovich M, Friston KJ, Varona P, editors.

integrative, mixed-mode BCI approaches that can restructure motor semantics.

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 obstacle to focal BCI therapy [40].

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

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 presented in Chapters 4 and 5 of the current volume.
