Investigation into machine learning algorithms as applied to motor cortex signals for classification of movement stages
Neuroinformatics has recently emerged as a powerful field for the statistical analysis of neural data. This study uses machine learning techniques to analyze neural spiking activities within a population of neurons with the aim of finding spiking patterns associated with different stages of movement...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
IEEE
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/62997 |
| _version_ | 1848760966357975040 |
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| author | Hollingshead, Luke Putrino, D. Ghosh, Soumya Tan, Tele |
| author_facet | Hollingshead, Luke Putrino, D. Ghosh, Soumya Tan, Tele |
| author_sort | Hollingshead, Luke |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Neuroinformatics has recently emerged as a powerful field for the statistical analysis of neural data. This study uses machine learning techniques to analyze neural spiking activities within a population of neurons with the aim of finding spiking patterns associated with different stages of movement. Neural data was recorded during many experimental trials of a cat performing a skilled reach and withdrawal task. Using Weka and the LibSVM classifier, movement stages of the skilled task were identified with a high degree of certainty achieving an area-under-curve (AUC) of the Receiver Operating Characteristic of between 0.900 and 0.997 for the combined data set. Through feature selection, the identification of significant neurons has been made easier. Given this encouraging classification performance, the extension to automatic classification and updating of control models for use with neural prostheses will enable regular adjustments capable of compensating for neural changes. |
| first_indexed | 2025-11-14T10:24:10Z |
| format | Conference Paper |
| id | curtin-20.500.11937-62997 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:24:10Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-629972019-07-30T00:45:01Z Investigation into machine learning algorithms as applied to motor cortex signals for classification of movement stages Hollingshead, Luke Putrino, D. Ghosh, Soumya Tan, Tele Neuroinformatics has recently emerged as a powerful field for the statistical analysis of neural data. This study uses machine learning techniques to analyze neural spiking activities within a population of neurons with the aim of finding spiking patterns associated with different stages of movement. Neural data was recorded during many experimental trials of a cat performing a skilled reach and withdrawal task. Using Weka and the LibSVM classifier, movement stages of the skilled task were identified with a high degree of certainty achieving an area-under-curve (AUC) of the Receiver Operating Characteristic of between 0.900 and 0.997 for the combined data set. Through feature selection, the identification of significant neurons has been made easier. Given this encouraging classification performance, the extension to automatic classification and updating of control models for use with neural prostheses will enable regular adjustments capable of compensating for neural changes. 2014 Conference Paper http://hdl.handle.net/20.500.11937/62997 10.1109/EMBC.2014.6943834 IEEE restricted |
| spellingShingle | Hollingshead, Luke Putrino, D. Ghosh, Soumya Tan, Tele Investigation into machine learning algorithms as applied to motor cortex signals for classification of movement stages |
| title | Investigation into machine learning algorithms as applied to motor cortex signals for classification of movement stages |
| title_full | Investigation into machine learning algorithms as applied to motor cortex signals for classification of movement stages |
| title_fullStr | Investigation into machine learning algorithms as applied to motor cortex signals for classification of movement stages |
| title_full_unstemmed | Investigation into machine learning algorithms as applied to motor cortex signals for classification of movement stages |
| title_short | Investigation into machine learning algorithms as applied to motor cortex signals for classification of movement stages |
| title_sort | investigation into machine learning algorithms as applied to motor cortex signals for classification of movement stages |
| url | http://hdl.handle.net/20.500.11937/62997 |