Exploring Convolutional Neural Network Architectures for EEG Feature Extraction

Sensors (Basel). 2024 Jan 29;24(3):877. doi: 10.3390/s24030877.


The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.

Keywords: CNN; EEG; machine learning; signal processing.

MeSH terms

  • Benchmarking
  • Electroencephalography* / methods
  • Interior Design and Furnishings
  • Neural Networks, Computer*
  • Signal Processing, Computer-Assisted

Grants and funding

I.R., A.N. and D.M.’s work was supported in part by a USSOCOM grant EESB P85655.