Classification of Brain Attention based on EEGNet with Fewer Channels
Pages 55 - 61
Abstract
In modern society, the management of attention has become increasingly complex and important. To understand the attention state of drivers during driving, we have been looking for effective identification and monitoring methods until the development of Brain-Computer Interface (BCI) technology. Electroencephalogram (EEG) has gradually become an indispensable tool in neuroscience research. However, traditional EEG technology usually requires many electrode channels to obtain sufficient information, which not only increases the cost but also increases the technical complexity. To overcome these challenges, this study uses EEG data of driver attention and adopts a new Convolutional Neural Network (CNN) model called EEGNet for model training and testing. Through optimizing parameter settings and appropriate feature extraction, as well as selecting necessary channels and adjusting the appropriate number of channels, we evaluate the performance of the method in terms of accuracy and practicality. This is to achieve accurate identification of the driver's brain attention under fewer channels and to explore the impact of model parameters and the number of channels on accuracy. We expect that this brain attention identification method based on the EEGNet model with fewer channels can achieve higher accuracy while reducing equipment costs and system complexity. This provides a more economical and effective solution for the development of intelligent driving systems, and also provides new possibilities for the application of BCI technology in the field of intelligent transportation.
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Index Terms
- Classification of Brain Attention based on EEGNet with Fewer Channels
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July 2024
179 pages
ISBN:9798400718038
DOI:10.1145/3688268
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Published: 18 December 2024
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ICCCM 2024
ICCCM 2024: 2024 the 12th International Conference on Computer and Communications Management (ICCCM)
July 19 - 21, 2024
Kagoshima-shi, Japan
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