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Implementation and Evaluation of Python's Automatic Parallelization Function for Accelerating EEG Feature Extraction
https://ipsj.ixsq.nii.ac.jp/records/241864
https://ipsj.ixsq.nii.ac.jp/records/241864a79ab680-79aa-4cb4-a9e2-24f9a454d198
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-APRIS2024004.pdf (1.2 MB)
2026年12月27日からダウンロード可能です。
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Copyright (c) 2024 by the Information Processing Society of Japan
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| 非会員：¥0, IPSJ:学会員：¥0, EMB:会員：¥0, DLIB:会員：¥0 | ||
| Item type | Symposium(1) | |||||||||
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| 公開日 | 2024-12-27 | |||||||||
| タイトル | ||||||||||
| タイトル | Implementation and Evaluation of Python's Automatic Parallelization Function for Accelerating EEG Feature Extraction | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Implementation and Evaluation of Python's Automatic Parallelization Function for Accelerating EEG Feature Extraction | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||||||
| 資源タイプ | conference paper | |||||||||
| 著者所属 | ||||||||||
| Shibaura Institute of Technology | ||||||||||
| 著者所属 | ||||||||||
| Shibaura Institute of Technology | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Shibaura Institute of Technology | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Shibaura Institute of Technology | ||||||||||
| 著者名 |
Soma, Kato
× Soma, Kato
× Midori, Sugaya
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| 著者名(英) |
Soma, Kato
× Soma, Kato
× Midori, Sugaya
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | In recent years, with the widespread adoption of IoT, the number of devices connected to networks has increased, leading to the generation of vast amounts of sensing data. High-performance and high-efficiency processing of this data is increasingly expected. One application that handles such large volumes of sensing data is EEG (electroencephalography). EEG is widely utilized not only for medical purposes but also in research fields such as cognitive science, psychology, and physiology, as well as in areas like neuromarketing. Its use is anticipated to expand further in the future. However, there are several challenges when it comes to efficiently processing EEG data. The first challenge is that while individual algorithms have been accelerated, general-purpose acceleration methods have not been sufficiently discussed. The second challenge is that there is no adequately discussed, straightforward method for speeding up the calculation of EEG features, leading to time-consuming processes. This study aims to accelerate EEG processing and reduce the effort required by parallelizing the calculation of EEG features. Additionally, as a method that allows for easy description, we propose an automatic parallelization method that utilizes YAML for describing the processing flow. Multiple parallelization methods were implemented, and their performance was evaluated by comparing processing times. As a result, the Buffering method, in particular, achieved up to approximately 9 times faster processing compared to sequential processing. | |||||||||
| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | In recent years, with the widespread adoption of IoT, the number of devices connected to networks has increased, leading to the generation of vast amounts of sensing data. High-performance and high-efficiency processing of this data is increasingly expected. One application that handles such large volumes of sensing data is EEG (electroencephalography). EEG is widely utilized not only for medical purposes but also in research fields such as cognitive science, psychology, and physiology, as well as in areas like neuromarketing. Its use is anticipated to expand further in the future. However, there are several challenges when it comes to efficiently processing EEG data. The first challenge is that while individual algorithms have been accelerated, general-purpose acceleration methods have not been sufficiently discussed. The second challenge is that there is no adequately discussed, straightforward method for speeding up the calculation of EEG features, leading to time-consuming processes. This study aims to accelerate EEG processing and reduce the effort required by parallelizing the calculation of EEG features. Additionally, as a method that allows for easy description, we propose an automatic parallelization method that utilizes YAML for describing the processing flow. Multiple parallelization methods were implemented, and their performance was evaluated by comparing processing times. As a result, the Buffering method, in particular, achieved up to approximately 9 times faster processing compared to sequential processing. | |||||||||
| 書誌情報 |
Proceedings of Asia Pacific Conference on Robot IoT System Development and Platform 巻 2024, p. 22-28, 発行日 2024-12-27 |
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| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
