Decoding of visual-related information from the human EEG using an end-to-end deep learning approach

TL;DR

This paper introduces EEG_CNNLSTMNet, an end-to-end deep learning model combining CNN and LSTM layers for improved EEG classification, outperforming traditional methods and existing neural network architectures, especially in inter-subject scenarios.

Contribution

The study proposes a novel CNNLSTM-based neural network architecture for EEG classification, demonstrating superior performance over traditional and existing neural network methods, and highlights the effectiveness of fine-tuning for inter-subject transfer learning.

Findings

EEG_CNNLSTMNet outperforms SVM and EEGNet in EEG classification.

The model performs well in intra-subject classification.

Fine-tuning enhances inter-subject classification accuracy.

Abstract

There is increasing interest in using deep learning approach for EEG analysis as there are still rooms for the improvement of EEG analysis in its accuracy. Convolutional long short-term (CNNLSTM) has been successfully applied in time series data with spatial structure through end-to-end learning. Here, we proposed a CNNLSTM based neural network architecture termed EEG_CNNLSTMNet for the classification of EEG signals in response to grating stimuli with different spatial frequencies. EEG_CNNLSTMNet comprises two convolutional layers and one bidirectional long short-term memory (LSTM) layer. The convolutional layers capture local temporal characteristics of the EEG signal at each channel as well as global spatial characteristics across channels, while the LSTM layer extracts long-term temporal dependency of EEG signals. Our experiment showed that EEG_CNNLSTMNet performed much better at EEG classification than a traditional machine learning approach, i.e. a support vector machine (SVM) with features. Additionally, EEG_CNNLSTMNet outperformed EEGNet, a state-of-art neural network architecture for the intra-subject case. We infer that the underperformance when using an LSTM layer in the inter-subject case is due to long-term dependency characteristics in the EEG signal that vary greatly across subjects. Moreover, the inter-subject fine-tuned classification model using very little data of the new subject achieved much higher accuracy than that trained only on the data from the other subjects. Our study suggests that the fine-tuned inter-subject model can be a potential end-to-end EEG analysis method considering both the accuracy and the required training data of the new subject.