The understanding of how sensory information is coded, processed, and related to motor actions is critical to developing effective rehabilitation protocols and assistive technologies, such as neural modulatory procedures and brain-machine interfaces. An important question is whether there is a sensorimotor 'common coding' or whether afferent information can be dissociated from efferent information. Likewise, it is critical to know how does a neuroplasticity process, related to a rehabilitation protocol, affect and is affected by this afferent-efferent relationship? Depending on how the sensorimotor code is understood, different mathematical and computational techniques for decoding the associated neural patterns can be adopted. Therefore, what are the best strategies to characterize, identify, and classify sensorimotor patterns? How do these strategies impact the development of protocols and assistive devices for people with sensorimotor dysfunctions? What are the best signal features and parameters associated with these decoding strategies?
This Research Topic aims to gather insightful articles to increase our knowledge regarding the mathematical and computational techniques applied to the characterization, identification, and classification of sensorimotor patterns. Mainly, we welcome novel insights into either computational modeling or data analysis techniques that can help to clear the whole of sensory processing into motor control and vice-versa. Additionally, how can these approaches be associated with rehabilitation protocols and assistive technologies to improve their performance and effectiveness?
1. Brain Computer-Interface as rehabilitation/assistive technology
2. Hybrid Brain Computer-Interface as integrative systems
3. Rehabilitation protocols for enhancement of motor control
4. Linear and non-Linear techniques for characterization and classification of sensorimotor patterns
5. Adaptive approaches in sensory-motor decoding
6. Neuroplasticity and adaptative classification cycle
7. Impact of brain computer-interface protocols on the sensorimotor activity
8. Computational modeling of sensorimotor dysfunctions
9. Brain complex networks and topological predictors for sensorimotor dysfunction
10. Spatial-temporal brain network modeling related to sensorimotor rhythms
11. Phase-frequency and phase-amplitude coupling as biomarkers for clinical rehabilitation
12. Mathematical modeling of perception-action and the related sensorimotor circuitry
The understanding of how sensory information is coded, processed, and related to motor actions is critical to developing effective rehabilitation protocols and assistive technologies, such as neural modulatory procedures and brain-machine interfaces. An important question is whether there is a sensorimotor 'common coding' or whether afferent information can be dissociated from efferent information. Likewise, it is critical to know how does a neuroplasticity process, related to a rehabilitation protocol, affect and is affected by this afferent-efferent relationship? Depending on how the sensorimotor code is understood, different mathematical and computational techniques for decoding the associated neural patterns can be adopted. Therefore, what are the best strategies to characterize, identify, and classify sensorimotor patterns? How do these strategies impact the development of protocols and assistive devices for people with sensorimotor dysfunctions? What are the best signal features and parameters associated with these decoding strategies?
This Research Topic aims to gather insightful articles to increase our knowledge regarding the mathematical and computational techniques applied to the characterization, identification, and classification of sensorimotor patterns. Mainly, we welcome novel insights into either computational modeling or data analysis techniques that can help to clear the whole of sensory processing into motor control and vice-versa. Additionally, how can these approaches be associated with rehabilitation protocols and assistive technologies to improve their performance and effectiveness?
1. Brain Computer-Interface as rehabilitation/assistive technology
2. Hybrid Brain Computer-Interface as integrative systems
3. Rehabilitation protocols for enhancement of motor control
4. Linear and non-Linear techniques for characterization and classification of sensorimotor patterns
5. Adaptive approaches in sensory-motor decoding
6. Neuroplasticity and adaptative classification cycle
7. Impact of brain computer-interface protocols on the sensorimotor activity
8. Computational modeling of sensorimotor dysfunctions
9. Brain complex networks and topological predictors for sensorimotor dysfunction
10. Spatial-temporal brain network modeling related to sensorimotor rhythms
11. Phase-frequency and phase-amplitude coupling as biomarkers for clinical rehabilitation
12. Mathematical modeling of perception-action and the related sensorimotor circuitry
