Advancing brain-machine interfaces: Moving beyond linear state space models
Advances in recent years have dramatically improved output control by Brain-Machine Interfaces (BMIs). Such devices nevertheless remain robotic and limited in their movements compared to normal human motor performance. Most current BMIs rely on transforming recorded neural activity to a linear sta...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2015-07-01
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| Series: | Frontiers in Systems Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnsys.2015.00108/full |
| _version_ | 1811270640291807232 |
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| author | Adam G Rouse Adam G Rouse Adam G Rouse Marc H Schieber Marc H Schieber Marc H Schieber |
| author_facet | Adam G Rouse Adam G Rouse Adam G Rouse Marc H Schieber Marc H Schieber Marc H Schieber |
| author_sort | Adam G Rouse |
| collection | DOAJ |
| description | Advances in recent years have dramatically improved output control by Brain-Machine Interfaces (BMIs). Such devices nevertheless remain robotic and limited in their movements compared to normal human motor performance. Most current BMIs rely on transforming recorded neural activity to a linear state space composed of a set number of fixed degrees of freedom. Here we consider a variety of ways in which BMI design might be advanced further by applying non-linear dynamics observed in normal motor behavior. We consider i) the dynamic range and precision of natural movements, ii) differences between cortical activity and actual body movement, iii) kinematic and muscular synergies, and iv) the implications of large neuronal populations. We advance the hypothesis that a given population of recorded neurons may transmit more useful information than can be captured by a single, linear model across all movement phases and contexts. We argue that incorporating these various non-linear characteristics will be an important next step in advancing BMIs to more closely match natural motor performance. |
| first_indexed | 2024-04-12T22:04:25Z |
| format | Article |
| id | doaj.art-d2ff7bb4e0a24fd098e5e7450384469f |
| institution | Directory Open Access Journal |
| issn | 1662-5137 |
| language | English |
| last_indexed | 2024-04-12T22:04:25Z |
| publishDate | 2015-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Systems Neuroscience |
| spelling | doaj.art-d2ff7bb4e0a24fd098e5e7450384469f2022-12-22T03:14:59ZengFrontiers Media S.A.Frontiers in Systems Neuroscience1662-51372015-07-01910.3389/fnsys.2015.00108133888Advancing brain-machine interfaces: Moving beyond linear state space modelsAdam G Rouse0Adam G Rouse1Adam G Rouse2Marc H Schieber3Marc H Schieber4Marc H Schieber5University of RochesterUniversity of RochesterUniversity of RochesterUniversity of RochesterUniversity of RochesterUniversity of RochesterAdvances in recent years have dramatically improved output control by Brain-Machine Interfaces (BMIs). Such devices nevertheless remain robotic and limited in their movements compared to normal human motor performance. Most current BMIs rely on transforming recorded neural activity to a linear state space composed of a set number of fixed degrees of freedom. Here we consider a variety of ways in which BMI design might be advanced further by applying non-linear dynamics observed in normal motor behavior. We consider i) the dynamic range and precision of natural movements, ii) differences between cortical activity and actual body movement, iii) kinematic and muscular synergies, and iv) the implications of large neuronal populations. We advance the hypothesis that a given population of recorded neurons may transmit more useful information than can be captured by a single, linear model across all movement phases and contexts. We argue that incorporating these various non-linear characteristics will be an important next step in advancing BMIs to more closely match natural motor performance.http://journal.frontiersin.org/Journal/10.3389/fnsys.2015.00108/fullHandMotor CortexBrain-computer interfaceneuroprostheticsMuscle Synergykinematic synergy |
| spellingShingle | Adam G Rouse Adam G Rouse Adam G Rouse Marc H Schieber Marc H Schieber Marc H Schieber Advancing brain-machine interfaces: Moving beyond linear state space models Frontiers in Systems Neuroscience Hand Motor Cortex Brain-computer interface neuroprosthetics Muscle Synergy kinematic synergy |
| title | Advancing brain-machine interfaces: Moving beyond linear state space models |
| title_full | Advancing brain-machine interfaces: Moving beyond linear state space models |
| title_fullStr | Advancing brain-machine interfaces: Moving beyond linear state space models |
| title_full_unstemmed | Advancing brain-machine interfaces: Moving beyond linear state space models |
| title_short | Advancing brain-machine interfaces: Moving beyond linear state space models |
| title_sort | advancing brain machine interfaces moving beyond linear state space models |
| topic | Hand Motor Cortex Brain-computer interface neuroprosthetics Muscle Synergy kinematic synergy |
| url | http://journal.frontiersin.org/Journal/10.3389/fnsys.2015.00108/full |
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