Improved decoding for brain-machine interfaces for continuous movement control
Main Author: | |
---|---|
Language: | English |
Published: |
Case Western Reserve University School of Graduate Studies / OhioLINK
2011
|
Subjects: | |
Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=case1301667321 |
id |
ndltd-OhioLink-oai-etd.ohiolink.edu-case1301667321 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-OhioLink-oai-etd.ohiolink.edu-case13016673212021-08-03T05:34:10Z Improved decoding for brain-machine interfaces for continuous movement control Marathe, Amar Ravindra Biomedical Engineering Brain-machine interfaces electrocorticography Brain-computer interfaces People with severe paralysis have limited options for commanding assistive device movements. Accessing movement commands directly from the brain will increase the options available to this population and could enable them to control complex device movements.The studies discussed here focus on improving decoding algorithms and strategies commonly used in brain machine interfaces (BMIs) for continuous device movements. These studies focus on improving micro‐electrocorticography (μECoG) based BMIs that decode naturalistic arm movements, but many of the results shown here can be applied to all forms of continuous‐movement BMIs.In the first phase of the project, we evaluated how various spatial filtering methods affect decoding quality in μECoG‐based BMIs. Spatial filtering is the process of creating new signals from linear combinations of the raw signals in order to remove common noise and maximize the detection of unique information from the entire set of electrodes. Many novel variants of the common spatial pattern spatial filter were compared to three standard methods to determine which methods are most effective at improving decoding performance. Our results suggest that some novel variants of common spatial patterns developed here can dramatically improve field potential decoding.In the second phase of this project, we determined which movement parameters should be decoded from the brain to maximize BMI performance. Furthermore, in situations where the ideal parameter cannot be decoded, various options for using one decoded movement parameter to control another aspect of device movement may improve BMI performance.The final phase of the project evaluated how different characteristics of decoders vary as a consequence of using increased amounts of past data to predict the current arm state. We also quantified how subtle changes in offline decoders affect BMI performance during online use. This knowledge will enable people to develop BMIs that will be effective in real‐time.These studies taken together lay the foundation for developing a μECoG‐based BMI that can effectively control the movements of a device using neural signals associated with naturalistic arm movements. 2011-04-20 English text Case Western Reserve University School of Graduate Studies / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=case1301667321 http://rave.ohiolink.edu/etdc/view?acc_num=case1301667321 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
collection |
NDLTD |
language |
English |
sources |
NDLTD |
topic |
Biomedical Engineering Brain-machine interfaces electrocorticography Brain-computer interfaces |
spellingShingle |
Biomedical Engineering Brain-machine interfaces electrocorticography Brain-computer interfaces Marathe, Amar Ravindra Improved decoding for brain-machine interfaces for continuous movement control |
author |
Marathe, Amar Ravindra |
author_facet |
Marathe, Amar Ravindra |
author_sort |
Marathe, Amar Ravindra |
title |
Improved decoding for brain-machine interfaces for continuous movement control |
title_short |
Improved decoding for brain-machine interfaces for continuous movement control |
title_full |
Improved decoding for brain-machine interfaces for continuous movement control |
title_fullStr |
Improved decoding for brain-machine interfaces for continuous movement control |
title_full_unstemmed |
Improved decoding for brain-machine interfaces for continuous movement control |
title_sort |
improved decoding for brain-machine interfaces for continuous movement control |
publisher |
Case Western Reserve University School of Graduate Studies / OhioLINK |
publishDate |
2011 |
url |
http://rave.ohiolink.edu/etdc/view?acc_num=case1301667321 |
work_keys_str_mv |
AT maratheamarravindra improveddecodingforbrainmachineinterfacesforcontinuousmovementcontrol |
_version_ |
1719421835505827840 |