Zijing Mao

In order to improve the accuracy of emotional recognition by end-to-end automatic learning of emotional features in spatial and temporal dimensions of electroencephalogram (EEG), an EEG emotional feature learning and classification method using deep convolution neural network (CNN) was proposed based on temporal features, frequential features, and their combinations of EEG signals in DEAP dataset. The shallow machine learning models including bagging tree (BT), support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian linear discriminant analysis (BLDA) models and deep CNN models were used to make emotional binary classification experiments on DEAP datasets in valence and arousal dimensions. The experimental results showed that the deep CNN models which require no feature engineering achieved the best recognition performance on temporal and frequency combined features in both valence and arousal dimensions, which is 3.58% higher than the performance of the best traditional BT classifier in valence dimension and 3.29% higher than that of BT classifier in arousal dimension.

Despite the recent increasing interest in biometric identification using electroencephalogram (EEG) signals, the state of the art still lacks a simple and robust model that is useful in real applications. This work proposes a new approach based on convolutional neural network CNN. The proposed CNN works directly on raw EEG data, thus alleviating the need for engineering features. We investigate the performance of the CNN on datasets of 100 subjects collected from one driving fatigue experiment. Our results show that the CNN model is fast highly efficient in training (<;0.5h on >100K training epochs) and highly robust, achieving 97% accuracy in identifying ~14K testing epochs from 100 subjects with non-time-locked natural driving fatigue data and much higher than from randomly sampled epochs (90%). Overall, this work demonstrates the potential of deep learning solutions for real-life EEG-based biometric identification.

We investigate in this paper deep learning (DL) solutions for prediction of driver's cognitive states (drowsy or alert) using EEG data. We discussed the novel channel-wise convolutional neural network (CCNN) and CCNN-R which is a CCNN variation that uses Restricted Boltzmann Machine in order to replace the convolutional filter. We also consider bagging classifiers based on DL hidden units as an alternative to the conventional DL solutions. To test the performance of the proposed methods, a large EEG dataset from 3 studies of driver's fatigue that includes 70 sessions from 37 subjects is assembled. All proposed methods are tested on both raw EEG and Independent Component Analysis (ICA)-transformed data for cross-session predictions. The results show that CCNN and CCNN-R outperform deep neural networks (DNN) and convolutional neural networks (CNN) as well as other non-DL algorithms and DL with raw EEG inputs achieves better performance than ICA features.