Comparison of neural signal preprocessing methods and their impact on classification performance.
<p><b>A</b>, Comparison of neural signal preprocessing strategies: The upper panel illustrates the time-interval-based preprocessing method (quantifying spike counts within fixed time windows); the lower panel shows the equal-segment-based preprocessing method (dividing decision ti...
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2025
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| _version_ | 1855367827718930432 |
|---|---|
| author | Yuhang Zhang (3144870) |
| author2 | Tao Sun (91038) Boyang Zang (21464459) Sen Wan (14509499) |
| author2_role | author author author |
| author_facet | Yuhang Zhang (3144870) Tao Sun (91038) Boyang Zang (21464459) Sen Wan (14509499) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yuhang Zhang (3144870) Tao Sun (91038) Boyang Zang (21464459) Sen Wan (14509499) |
| dc.date.none.fl_str_mv | 2025-08-21T17:57:13Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1013335.g003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Comparison_of_neural_signal_preprocessing_methods_and_their_impact_on_classification_performance_/29963013 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Neuroscience Biotechnology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified term memory network reproducible electrophysiology dataset particularly challenging problem international brain laboratory directional long short channel attention bi making stability within accurately forecasting decision making decoding model making behavior decoding decision proposed model model serves remarkable capacity novel perspective neural decision investigation furnishes ibl ), crucial purpose bilstm ), |
| dc.title.none.fl_str_mv | Comparison of neural signal preprocessing methods and their impact on classification performance. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p><b>A</b>, Comparison of neural signal preprocessing strategies: The upper panel illustrates the time-interval-based preprocessing method (quantifying spike counts within fixed time windows); the lower panel shows the equal-segment-based preprocessing method (dividing decision time uniformly across trials and calculating spike counts within each segment). Due to significant variations in decision times across trials, the equal-segment-based approach enables standardized dimensional representation of data from different trials, effectively minimizing interference from redundant information. <b>B</b>, Performance comparison of preprocessing methods: Scatter plot showing classification accuracy of the CA-BiLSTM model for individual mouse samples under different preprocessing approaches, with triangular markers indicating mean accuracy across all mice. Results demonstrate that the equal-segment-based preprocessing method yields significantly higher accuracy for the majority of samples, validating the effectiveness of the proposed preprocessing strategy.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_ed9300a89b60d7892e8272f8b032adbc |
| identifier_str_mv | 10.1371/journal.pcbi.1013335.g003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29963013 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Comparison of neural signal preprocessing methods and their impact on classification performance.Yuhang Zhang (3144870)Tao Sun (91038)Boyang Zang (21464459)Sen Wan (14509499)NeuroscienceBiotechnologyScience PolicyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedterm memory networkreproducible electrophysiology datasetparticularly challenging probleminternational brain laboratorydirectional long shortchannel attention bimaking stability withinaccurately forecasting decisionmaking decoding modelmaking behaviordecoding decisionproposed modelmodel servesremarkable capacitynovel perspectiveneural decisioninvestigation furnishesibl ),crucial purposebilstm ),<p><b>A</b>, Comparison of neural signal preprocessing strategies: The upper panel illustrates the time-interval-based preprocessing method (quantifying spike counts within fixed time windows); the lower panel shows the equal-segment-based preprocessing method (dividing decision time uniformly across trials and calculating spike counts within each segment). Due to significant variations in decision times across trials, the equal-segment-based approach enables standardized dimensional representation of data from different trials, effectively minimizing interference from redundant information. <b>B</b>, Performance comparison of preprocessing methods: Scatter plot showing classification accuracy of the CA-BiLSTM model for individual mouse samples under different preprocessing approaches, with triangular markers indicating mean accuracy across all mice. Results demonstrate that the equal-segment-based preprocessing method yields significantly higher accuracy for the majority of samples, validating the effectiveness of the proposed preprocessing strategy.</p>2025-08-21T17:57:13ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1013335.g003https://figshare.com/articles/figure/Comparison_of_neural_signal_preprocessing_methods_and_their_impact_on_classification_performance_/29963013CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/299630132025-08-21T17:57:13Z |
| spellingShingle | Comparison of neural signal preprocessing methods and their impact on classification performance. Yuhang Zhang (3144870) Neuroscience Biotechnology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified term memory network reproducible electrophysiology dataset particularly challenging problem international brain laboratory directional long short channel attention bi making stability within accurately forecasting decision making decoding model making behavior decoding decision proposed model model serves remarkable capacity novel perspective neural decision investigation furnishes ibl ), crucial purpose bilstm ), |
| status_str | publishedVersion |
| title | Comparison of neural signal preprocessing methods and their impact on classification performance. |
| title_full | Comparison of neural signal preprocessing methods and their impact on classification performance. |
| title_fullStr | Comparison of neural signal preprocessing methods and their impact on classification performance. |
| title_full_unstemmed | Comparison of neural signal preprocessing methods and their impact on classification performance. |
| title_short | Comparison of neural signal preprocessing methods and their impact on classification performance. |
| title_sort | Comparison of neural signal preprocessing methods and their impact on classification performance. |
| topic | Neuroscience Biotechnology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified term memory network reproducible electrophysiology dataset particularly challenging problem international brain laboratory directional long short channel attention bi making stability within accurately forecasting decision making decoding model making behavior decoding decision proposed model model serves remarkable capacity novel perspective neural decision investigation furnishes ibl ), crucial purpose bilstm ), |