Convolutional neural networks with large-margin softmax loss function for cognitive load recognition

Cognitive load recognition has been widely studied recently, but how to find the effective and robust feature representations from the electroencephalography (EEG) signals is still a challenge. In this paper we design lightweight 1D and 2D Convolutional Neural Networks (CNNs) with large-margin softmax loss functions for cognitive load recognition. First, we extract the frequency domain features from the EEG signals. Then, the extracted frequency feature vectors and matrices are used to train the 1D and 2D CNNs. Our approach approximately achieves 93% accuracy for 1D CNNs and 91% for 2D CNNs with shorter training time on the 4 classes of cognitive load recognition task. The results indicate that the proposed method with few parameters gets good performance and is more efficient than other deep learning methods. We find that the large-margin softmax loss function works well for EEG signal recognition, and we also find that the 1D CNNs with 3 χ 1 convolution kernel have good classification performance with few parameters and fast training rate to be more suitable for EEG signal classification than 2D CNNs for small EEG dataset. However, 2D CNNs are suitable for large EEG dataset because of their fast training rate.