Computer Science > Machine Learning
arXiv:2303.11371 (cs)
[Submitted on 20 Mar 2023]
Title:Optimized preprocessing and Tiny ML for Attention State Classification
View a PDF of the paper titled Optimized preprocessing and Tiny ML for Attention State Classification, by Yinghao Wang and 4 other authors
View PDFAbstract:In this paper, we present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning (ML) algorithms. We evaluate the performance of the proposed method on a dataset of EEG recordings collected during a cognitive load task and compared it to other state-of-the-art methods. The results show that the proposed method achieves high accuracy in classifying mental states and outperforms state-of-the-art methods in terms of classification accuracy and computational efficiency.
| Subjects: | Machine Learning (cs.LG); Signal Processing (eess.SP) |
| Cite as: | arXiv:2303.11371 [cs.LG] |
| (orarXiv:2303.11371v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2303.11371 arXiv-issued DOI via DataCite | |
| Related DOI: | https://doi.org/10.1109/SSP53291.2023.10207930 DOI(s) linking to related resources |
Full-text links:
Access Paper:
- View PDF
- TeX Source
- Other Formats
View a PDF of the paper titled Optimized preprocessing and Tiny ML for Attention State Classification, by Yinghao Wang and 4 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
IArxiv Recommender(What is IArxiv?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.