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Classification of EEG signals using relative wavelet energy and artificial neural networks
Abstract
Electroencephalographms (EEGs) are records of brain electrical activity. It is an indispensable tool for diagnosing neurological diseases, such as epilepsy. Wavelet transform (WT) is an effective tool for analysis of non-stationary signal, such as EEGs. Relative wavelet energy (RWE) provides information about the relative energy associated with different frequency bands present in EEG signals and their corresponding degree of importance. This paper deals with a novel method of analysis of EEG signals using relative wavelet energy, and classification using Artificial Neural Networks (ANNs). The obtained classification accuracy confirms that the proposed scheme has potential in classifying EEG signals.
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Cited by
Samiee, K., Kovacs, P., & Gabbouj, M. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2360101 · Cited by 411
Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders
Raghavendra, U., Acharya, U. R., & Adeli, H. (2019) · European Neurology · doi:10.1159/000504292 · Cited by 174
Metadata
- Authors
- Guo, L., Rivero, D., Seoane, J. A., & Pazos, A.
- Venue
- Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
- Publisher
- ACM
- Published
- 2009-06-12
- Pages
- 177-184
- OpenAlex
- W2034697419
- Cited by
- 192
- References
- 0
- FWCI
- 14.89
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Classification of EEG signals using relative wavelet energy and artificial neural networks. https://app.cochat.ai/papers/10.1145/1543834.1543860
- The paper
- Guo, L., Rivero, D., Seoane, J. A., & Pazos, A. (2009). Classification of EEG signals using relative wavelet energy and artificial neural networks. Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation. https://doi.org/10.1145/1543834.1543860
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
