Novel Single Trial Movement Classification Based On Temporal Dynamics Of Eeg Article Swipe
Maitreyee Wairagkar
,
Ian Daly
,
Yoshikatsu Hayashi
,
Slawomir J. Nasuto
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3217/978-3-85125-378-8-81
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3217/978-3-85125-378-8-81
Various complex oscillatory processes are involved in the generation of the motor command. The temporal dynamics of these processes were studied for movement detection from single trial electroencephalogram (EEG). Autocorrelation analysis was performed on the EEG signals to find robust markers of movement detection. The evolution of the autocorrelation function was characterised via the relaxation time of the autocorrelation by exponential curve fitting. It was observed that the decay constant of the exponential curve increased during movement, indicating that the autocorrelation function decays slowly during motor execution. Significant differences were observed between movement and no moment tasks. Additionally, a linear discriminant analysis (LDA) classifier was used to identify movement trials with a peak accuracy of 74%.
Related Topics
Concepts
Autocorrelation
Movement (music)
Electroencephalography
Artificial intelligence
Linear discriminant analysis
Pattern recognition (psychology)
Dynamics (music)
Exponential function
Autocorrelation technique
Computer science
Mathematics
Speech recognition
Psychology
Statistics
Physics
Neuroscience
Mathematical analysis
Acoustics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://centaur.reading.ac.uk/37412/1/Graz%20conference%202014-Final%20version.pdf
- OA Status
- green
- Cited By
- 5
- References
- 4
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W37422268
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W37422268Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3217/978-3-85125-378-8-81Digital Object Identifier
- Title
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Novel Single Trial Movement Classification Based On Temporal Dynamics Of EegWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-04-07Full publication date if available
- Authors
-
Maitreyee Wairagkar, Ian Daly, Yoshikatsu Hayashi, Slawomir J. NasutoList of authors in order
- Landing page
-
https://centaur.reading.ac.uk/37412/1/Graz%20conference%202014-Final%20version.pdfPublisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3217/978-3-85125-378-8-81Direct OA link when available
- Concepts
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Autocorrelation, Movement (music), Electroencephalography, Artificial intelligence, Linear discriminant analysis, Pattern recognition (psychology), Dynamics (music), Exponential function, Autocorrelation technique, Computer science, Mathematics, Speech recognition, Psychology, Statistics, Physics, Neuroscience, Mathematical analysis, AcousticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2022: 1, 2018: 1, 2017: 1, 2016: 1, 2015: 1Per-year citation counts (last 5 years)
- References (count)
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4Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.with | 110 |
| abstract_inverted_index.(LDA) | 102 |
| abstract_inverted_index.curve | 61, 73 |
| abstract_inverted_index.decay | 68 |
| abstract_inverted_index.motor | 11, 85 |
| abstract_inverted_index.these | 17 |
| abstract_inverted_index.trial | 26 |
| abstract_inverted_index.(EEG). | 28 |
| abstract_inverted_index.decays | 82 |
| abstract_inverted_index.during | 75, 84 |
| abstract_inverted_index.linear | 99 |
| abstract_inverted_index.moment | 95 |
| abstract_inverted_index.robust | 39 |
| abstract_inverted_index.single | 25 |
| abstract_inverted_index.slowly | 83 |
| abstract_inverted_index.tasks. | 96 |
| abstract_inverted_index.trials | 109 |
| abstract_inverted_index.Various | 0 |
| abstract_inverted_index.between | 91 |
| abstract_inverted_index.complex | 1 |
| abstract_inverted_index.markers | 40 |
| abstract_inverted_index.signals | 36 |
| abstract_inverted_index.studied | 20 |
| abstract_inverted_index.accuracy | 113 |
| abstract_inverted_index.analysis | 30, 101 |
| abstract_inverted_index.command. | 12 |
| abstract_inverted_index.constant | 69 |
| abstract_inverted_index.dynamics | 15 |
| abstract_inverted_index.fitting. | 62 |
| abstract_inverted_index.function | 49, 81 |
| abstract_inverted_index.identify | 107 |
| abstract_inverted_index.involved | 5 |
| abstract_inverted_index.movement | 22, 42, 92, 108 |
| abstract_inverted_index.observed | 65, 90 |
| abstract_inverted_index.temporal | 14 |
| abstract_inverted_index.detection | 23 |
| abstract_inverted_index.evolution | 45 |
| abstract_inverted_index.increased | 74 |
| abstract_inverted_index.movement, | 76 |
| abstract_inverted_index.performed | 32 |
| abstract_inverted_index.processes | 3, 18 |
| abstract_inverted_index.classifier | 103 |
| abstract_inverted_index.detection. | 43 |
| abstract_inverted_index.execution. | 86 |
| abstract_inverted_index.generation | 8 |
| abstract_inverted_index.indicating | 77 |
| abstract_inverted_index.relaxation | 54 |
| abstract_inverted_index.Significant | 87 |
| abstract_inverted_index.differences | 88 |
| abstract_inverted_index.exponential | 60, 72 |
| abstract_inverted_index.oscillatory | 2 |
| abstract_inverted_index.discriminant | 100 |
| abstract_inverted_index.Additionally, | 97 |
| abstract_inverted_index.characterised | 51 |
| abstract_inverted_index.Autocorrelation | 29 |
| abstract_inverted_index.autocorrelation | 48, 58, 80 |
| abstract_inverted_index.electroencephalogram | 27 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.6700000166893005 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.38833116 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |