Zijing Mao

Xiaomi (China)

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Yufei Huang

UPMC Hillman Cancer Center

Mehdi Hajinoroozi

The University of Texas at San Antonio

Jingxia Chen

Changhai Hospital

Lenis Mauricio Meriño

The University of Texas at San Antonio

Tapsya Nayak

The University of Texas Health Science Center at San Antonio

Daniel J. Pack

University of Kentucky

Tinghe Zhang

UPMC Hillman Cancer Center

Tzyy‐Ping Jung

Tianjin University

Fuquan Zhang

Minjiang University

Wan X. Yao

The University of Texas at San Antonio

Cooperative Institutions

The University of Texas at San Antonio

Shanghai Jiao Tong University

Xiaomi (China)

The University of Texas Health Science Center at San Antonio

Beijing Institute of Technology

Northwestern Polytechnical University

Shaanxi University of Science and Technology

Tsinghua University

University of Pittsburgh

Fujian Normal University

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Prediction of driver's drowsy and alert states from EEG signals with deep learning

Mehdi Hajinoroozi Zijing Mao Yufei Huang

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.

Boltzmann machine

10.1109/camsap.2015.7383844

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Citations (59)

Idea Creation of Creative Computing Based on Embedding Features and Ontology

Fuquan Zhang Zijing Mao Gangyi Ding Rui Zhang Junjiang Lai

This paper used clustering to embed features and use ontology to simulate the process of idea creation from human brain. All the technologies are applied on the frame of creative computing. We are try to display the emergence, the self-organization, the self-coordination of idea creation process of human brain. By creative computing we will try to discover how the idea coming from step by step. From this paper, you will see the process in five steps from blurry motivation to clear creative idea.

10.1145/2955129.2955191

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Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures

Brain Sciences (2018)

Tapsya Nayak Tinghe Zhang Zijing Mao Xiaojing Xu Lin Zhang

Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing indoor environments have become a highly important research topic bearing significant impact in our society. While past research studies have attempted to determine predictors for performance, they do not provide satisfactory prediction ability. Therefore, in this preliminary study, we attempt to predict performance during office-work tasks triggered by different indoor room temperatures (22.2 °C and 30 °C) from human brain signals recorded using electroencephalography (EEG). Seven participants were recruited, from whom EEG, skin temperature, heart rate and thermal survey questionnaires were collected. Regression analyses were carried out to investigate the effectiveness of using EEG power spectral densities (PSD) as predictors of performance. Our results indicate EEG PSDs as predictors provide the highest R² (> 0.70), that is 17 times higher than using other physiological signals as predictors and is more robust. Finally, the paper provides insight on the selected predictors based on brain activity patterns for low- and high-performance levels under different indoor-temperatures.

10.3390/brainsci8040074

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Citations (23)

Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks

IEEE Access (2019)

Jingxia Chen Ping Zhang Zijing Mao Yunran Huang Dongmei Jiang

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.

Binary classification

10.1109/access.2019.2908285

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Citations (207)

A Deep Learning method for classification of images RSVP events with EEG data

IEEE Global Conference on Signal and Information Processing (2013)

Shaheen Ahmed Lenis Mauricio Meriño Zijing Mao Jia Meng Kay A. Robbins

In this paper, we investigated Deep Learning (DL) for characterizing and detecting target images in an image rapid serial visual presentation (RSVP) task based on EEG data. We exploited DL technique with input feature clusters to handle high dimensional features related to time - frequency events. The method was applied to EEG recordings of a RSVP experiment with multiple sessions and subjects. For classification of target and non-target images, a deep belief net (DBN) classifier was based on the uncorrelated features, which was constructed from original correlated features using clustering method. The performance of the proposed DBN was tested for different combinations of hidden units and hidden layers on multiple subjects. The results of DBN were compared with cluster Linear Discriminant Analysis (cLDA) and Support vector machine (SVM) and DBN demonstrated better performance in all tested cases. There was an improvement of 10 – 25% for certain cases. We also demonstrated how DBN is used to characterize brain activities.

10.1109/globalsip.2013.6736804

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Citations (30)

BIMMER: a novel algorithm for detecting differential DNA methylation regions from MBDCap-seq data

BMC Bioinformatics (2014)

Zijing Mao Chifeng Ma Tim Hui-Ming Huang Yidong Chen Yufei Huang

DNA methylation is a common epigenetic marker that regulates gene expression. A robust and cost-effective way for measuring whole genome methylation is Methyl-CpG binding domain-based capture followed by sequencing (MBDCap-seq). In this study, we proposed BIMMER, a Hidden Markov Model (HMM) for differential Methylation Regions (DMRs) identification, where HMMs were proposed to model the methylation status in normal and cancer samples in the first layer and another HMM was introduced to model the relationship between differential methylation and methylation statuses in normal and cancer samples. To carry out the prediction for BIMMER, an Expectation-Maximization algorithm was derived. BIMMER was validated on the simulated data and applied to real MBDCap-seq data of normal and cancer samples. BIMMER revealed that 8.83% of the breast cancer genome are differentially methylated and the majority are hypo-methylated in breast cancer.

Differentially methylated regions

CpG site

10.1186/1471-2105-15-s12-s6

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EEG-based biometric identification with convolutional neural network

Multimedia Tools and Applications (2019)

Jingxia Chen Zijing Mao Wanxiang Yao Yufei Huang

Feature (linguistics)

Identification

10.1007/s11042-019-7258-4

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Citations (56)

Asynchronous control of unmanned aerial vehicles using a steady-state visual evoked potential-based brain computer interface

Brain-Computer Interfaces (2017)

Lenis Mauricio Meriño Tapsya Nayak Prasanna Kolar Garrett Hall Zijing Mao

The goal of this study is to design an asynchronous steady-state visual evoked potential (SSVEP) BCI system to enable control of an unmanned aerial vehicle (UAV) with multiple commands. An SSVEP-based BCI system with six different flickering frequencies was constructed to realize six actuation commands for UAV control. In addition, asynchronous control was achieved by including a detection of the 'idle' brain state using a novel likelihood ratio test and the hover command was implemented for the idle state. Offline recording was conducted to evaluate the detection accuracies and a game-like online experiment was also conducted to assess the online performance of the proposed system. Forty-two subjects participated in offline recordings to evaluate the detection accuracy of commands as well as detection of the 'idle' state. An average error rate of 15% was obtained for detecting the six commands, whereas an average error rate of 23.06% was obtained for differentiating commands from idle brain states. For the online test, 11 subjects were recruited and all except two subjects successfully demonstrated control of the UAV by maneuvering the drone using all six commands and hover to acquire targets. Given our system design with a higher number of commands and online task difficulty, an average ITR of 0.98 bits/min was obtained. The developed SSVEP-based drone control system can execute a lot more commands than an imaginary motion-based drone control system and the asynchronous design significantly improves navigation. In addition, the proposed system can achieve good detection performance and ITR without any training.

Idle

Interface (matter)

10.1080/2326263x.2017.1292721

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Citations (23)

Design of Chinese Natural Language in Fuzzy Boundary Determination Algorithm Based on Big Data.

J. Inf. Hiding Multim. Signal Process. (2017)

Fuquan Zhang Zijing Mao Gangyi Ding Lin Xu

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Classification of non-time-locked rapid serial visual presentation events for brain-computer interaction using deep learning

Zijing Mao Vernon J. Lawhern Lenis Mauricio Meriño Kenneth R. Ball Li Deng

Deep learning solutions based on deep neural networks (DNN) and deep stack networks (DSN) were investigated for classifying target images in a non-time-locked rapid serial visual presentation (RSVP) image target identification task using EEG. Several feature extraction methods associated with this task were implemented and tested for deep learning, where a sliding window method using the trained classifier was used to predict the occurrence of target events in a non-time-locked fashion‥ The deep learning algorithms explored based on deep stacking networks were able to improve the error rate by about 5% over existing algorithms such as linear discriminant analysis (LDA) for this task. Initial test results also showed that this method based on deep stacking networks for non-time-locked classification can produce an error rate close to that achieved for time-locked classification, thus illustrating the power of deep learning for complex feature spaces.

Word error rate

Deep Neural Networks

10.1109/chinasip.2014.6889297

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Citations (9)