Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection

Abstract

In the area of brain-computer interfaces (BCI), the detection of P300 is a very important technique and has a lot of applications. Although this problem has been studied for decades, it is still a tough problem in electroencephalography (EEG) signal processing owing to its high dimension features and low signal-to-noise ratio (SNR). Recently, neural networks, like conventional neural networks (CNN), has shown excellent performance on many applications. However, standard convolutional neural networks suffer from performance degradation on dealing with noisy data or data with too many redundant information. In this paper, we proposed a novel convolutional neural network with variational information bottleneck for P300 detection. Wiht the CNN architecture and information bottleneck, the proposed network termed P300-VIB-Net could remove the redundant information in data effectively. The experimental results on BCI competition data sets show that P300-VIB-Net achieves cutting-edge character recognition performance. Furthermore, the proposed model is capable of restricting the flow of irrelevant information adaptively in the network from perspective of information theory. The experimental results show that P300-VIB-Net is a promising tool for P300 detection.

MOOC Dropout Prediction Based on Multidimensional Time-Series Data

“…e model uses the variational information bottleneck to compress the dimensionality of the input data to improve generalizability. Liao et al [36] proposed a new convolutional neural network with variational information bottleneck for P300 EEG signal detection. Experiments show that this method can effectively remove redundant information from the P300 EEG data.…”