Apply 2D convolution kernels on EEGNet to improve classification accuracy
Pages 153 - 157
Abstract
The electroencephalogram (EEG) is crucial in both medical and research categories, providing insights into continuous and irregular electrical potential fluctuations on the scalp surface through Brain-Computer Interface (BCI). The processing time required by BCI in handling EEG data is a significant consideration, as it can impact not only researchers' result interpretations but also play a crucial role in medical professionals' assessment of patients' conditions. The EEGNet is an improved convolutional Neural Network (CNN) architecture designed to classification of specific brainwave patterns in EEG. The original version of EEGNet is one dimensional convolution kernels. Multi-channel data are gathered by electrodes sticked on scalp. However, the original EEGNet pay most attention on the horizontal and vertical features one by one channel. Some features across channels will be omit. Moreover, the processing time are varying for 2D kernels size. This study demonstrates the impact of 2D convolution kernels on accuracy and processing time. Our research shows that 2D convolution kernels enhances accuracy while reducing processing time.
References
[1]
Wei Mu 2022. EEG Channel Selection Methods for Motor Imagery in Brain Computer Interface. In 2022 10th International Winter Conference on Brain-Computer Interface (BCI), Gangwon-do, Korea, Republic of, pp. 1-6.
[2]
Rabie A. Ramadan, Athanasios V. Vasilakos. 2017. Brain computer interface: control signals review. Neurocomputing, vol. 223, pp. 26-44, ISSN 0925-2312.
[3]
Yong-Jeong Kim, No-Sang Kwak, and Seong-Whan Lee. 2018. Classification of motor imagery for Ear-EEG based brain-computer interface. In 2018 6th International Conference on Brain-Computer Interface (BCI), Gangwon, Korea (South), pp. 1-2.
[4]
Jonathan R. Wolpaw 2002. Brain-computer interfaces for communication and control. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, vol. 113, no. 6, pp. 767-791.
[5]
Netiwit Kaongoen and Sungho Jo. 2018. An auditory P300-based brain-computer interface using Ear-EEG. In 2018 6th International Conference on Brain-Computer Interface (BCI), Gangwon, Korea (South), pp. 1-4.
[6]
Netiwit Kaongoen and Sungho Jo. 2020. An Ear-EEG-based Brain-Computer Interface using Concentration Level for Control. In 2020 8th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, Korea (South), pp. 1-4.
[7]
Kamran Ansari-Asl, Gaelle Chanel, and Thierry Pun. 2007. A channel selection method for EEG classification in emotion assessment based on synchronization likelihood. In 2007 15th European Signal Processing Conference, Poznan, Poland, pp. 1241-1245.
[8]
Ziyan Zou and Kuiying Yin. 2021. EEG Effective Channel Selection based on Target Detection. In 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China, pp. 523-526.
[9]
Elena L. Glassman and John V. Guttag. 2006. Reducing the number of channels for an ambulatory patient-specific EEG-based epileptic seizure detector by applying recursive feature elimination. In Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, vol. 2006, pp. 2175-8.
[10]
Chu-Hui Lee, Bo-Yu Jiang, Chun-Ming Huang, and Chien-Ming Wu. 2023. Effectively reduce the number of EEG channels by deleting nonessential channels. In 2023 12th International Conference on Awareness Science and Technology (iCAST), Taichung, Taiwan, pp. 295-300.
[11]
MNE Dataset: https://mne.tools/stable/index.html
[12]
Ruimin Peng 2022. TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification. In IEEE Transactions on Neural Systems and Rehabilitation Engineering: A Publication of the IEEE Engineering in Medicine and Biology Society, vol. 30, pp. 2567-2576.
[13]
Shaokang Yin 2022. Intelligent Classification for Emotional Issues by Deep Learning Network on EEG Signal Processing. In 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), Changchun, China, pp. 742-747.
[14]
Veronika Guleva, Alessandra Calcagno, Pierluigi Reali, and Anna Maria Bianchi. 2022. Personality traits classification from EEG signals using EEGNet. In 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), Palermo, Italy, pp. 590-594.
[15]
Mengyuan Liu, Fucheng Cao, Xinzui Wang, and Yanli Yang. 2023. A study of EEG classification based on attention mechanism and EEGNet Motor Imagination. In 2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS), Chengdu, China, pp. 976-981.
[16]
Vernon J. Lawhern 2018. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. Journal of Neural Engineering, vol. 15, no. 5, pp. 056013.
[17]
Zehong Cao, Chun-Hsiang Chuang, Jung-Kai King, and Chin-Teng Lin. 2019. Multi-channel EEG recordings during a sustained-attention driving task. Sci Data 6, 19.
[18]
Avilov, Oleksii 2021. Optimizing Motor Intention Detection With Deep Learning: Towards Management of Intraoperative Awareness. IEEE Transactions on Bio-Medical Engineering, vol. 68, no. 10, pp. 3087-3097.
[19]
Rongrong Fu 2023. EEGNet-MSD: A Sparse Convolutional Neural Network for Efficient EEG-Based Intent Decoding. IEEE Sensors Journal, vol. 23, no. 17, pp. 19684-19691, 1 Sept. 2023.
[20]
Xin Deng 2021. Advanced TSGL-EEGNet for Motor Imagery EEG-Based Brain-Computer Interfaces. IEEE Access, vol. 9, pp. 25118-25130, 2021.
[21]
Wenkai Huang 2020. S-EEGNet: Electroencephalogram Signal Classification Based on a Separable Convolution Neural Network With Bilinear Interpolation. IEEE Access, vol. 8, pp. 131636-131646, 2020.
[22]
Donghyun Park 2023. Spatio-Temporal Explanation of 3D-EEGNet for Motor Imagery EEG Classification Using Permutation and Saliency. IEEE Transactions on Neural Systems and Rehabilitation Engineering: A Publication of the IEEE Engineering in Medicine and Biology Society, vol. 31, pp. 4504-4513.
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- Apply 2D convolution kernels on EEGNet to improve classification accuracy
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Published In
May 2024
349 pages
ISBN:9798400716874
DOI:10.1145/3673971
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Published: 09 September 2024
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ICMHI 2024
ICMHI 2024: 2024 8th International Conference on Medical and Health Informatics
May 17 - 19, 2024
Yokohama, Japan
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