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Journal Article 5

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2022 1

2021 1

2019 1

2018 1

2015 1

Keywords

Electroencephalogram (EEG) 3

Brain-computer interface (BCI) 2

Attention mechanism 1

Chinese speller 1

Classification 1

Electroencephalography (EEG) 1

Electrooculogram (EOG) 1

Emotion recognition 1

English speller 1

Feature extraction 1

Improved common spatial pattern (B-CSP) 1

Motor imagery (MI) 1

Ocular artifacts 1

Personality traits 1

Spatial constraint independent component analysis based recursive least squares (SCICA-RLS) 1

brain–computer interface 1

chronic stroke 1

electroencephalogram 1

functional electrical stimulation 1

laterality coefficient 1

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Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent

Bang-hua YANG,Liang-fei HE,Lin LIN,Qian WANG

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 6,   Pages 486-496 doi: 10.1631/FITEE.1400299

Abstract: Ocular artifacts cause the main interfering signals within electroencephalogram (EEG) signal measurements

Keywords： Ocular artifacts     Electroencephalogram (EEG)     Electrooculogram (EOG)     Brain-computer interface (BCI)    

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EEG-controlled functional electrical stimulation rehabilitation for chronic stroke: system design and clinical application

Frontiers of Medicine 2021, Volume 15, Issue 5,   Pages 740-749 doi: 10.1007/s11684-020-0794-5

Abstract: Stroke is one of the most serious diseases that threaten human life and health. It is a major cause of death and disability in the clinic. New strategies for motor rehabilitation after stroke are undergoing exploration. We aimed to develop a novel artificial neural rehabilitation system, which integrates brain--computer interface (BCI) and functional electrical stimulation (FES) technologies, for limb motor function recovery after stroke. We conducted clinical trials (including controlled trials) in 32 patients with chronic stroke. Patients were randomly divided into the BCI-FES group and the neuromuscular electrical stimulation (NMES) group. The changes in outcome measures during intervention were compared between groups, and the trends of ERD values based on EEG were analyzed for BCI-FES group. Results showed that the increase in Fugl Meyer Assessment of the Upper Extremity (FMA-UE) and Kendall Manual Muscle Testing (Kendall MMT) scores of the BCI-FES group was significantly higher than that in the sham group, which indicated the practicality and superiority of the BCI-FES system in clinical practice. The change in the laterality coefficient (LC) values based on μ-ERD (ΔLCm-ERD) had high significant positive correlation with the change in FMA-UE(r= 0.6093, P=0.012), which provides theoretical basis for exploring novel objective evaluation methods.

Keywords： brain–computer interface     functional electrical stimulation     electroencephalogram     laterality coefficient    

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A personality-guided affective brain–computer interface for implementation of emotional intelligence in machines Research Article

Shaojie LI, Wei LI, Zejian XING, Wenjie YUAN, Xiangyu WEI, Xiaowei ZHANG, Bin HU,zhangxw@lzu.edu.cn,bh@lzu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8,   Pages 1158-1173 doi: 10.1631/FITEE.2100489

Abstract: Affective brain–computer interfaces have become an increasingly important topic to achieve emotional intelligence in human–machine collaboration. However, due to the complexity of signals and the individual differences in emotional response, it is still a great challenge to design a reliable and effective model. Considering the influence of on emotional response, it would be helpful to integrate personality information and EEG signals for . This study proposes a personality-guided attention neural network that can use personality information to learn effective EEG representations for . Specifically, we first use a convolutional neural network to extract rich temporal and regional representations of EEG signals, and a special convolution kernel is designed to learn inter- and intra-regional correlations simultaneously. Second, inspired by the fact that electrodes within distinct brain scalp regions play different roles in , a personality-guided regional- is proposed to further explore the contributions of electrodes within a region and between regions. Finally, attention-based long short-term memory is designed to explore the temporal dynamics of EEG signals. Experiments on the AMIGOS dataset, which is a dataset for multimodal research for affect, , and mood on individuals and groups, show that the proposed method can significantly improve the performance of subject-independent and outperform state-of-the-art methods.

Keywords： Electroencephalogram (EEG)     Emotion recognition     Attention mechanism     Personality traits    

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Classification of EEG-based single-trial motor imagery tasks using aB-CSP method forBCI Research Articles

Zhi-chuan TANG, Chao LI, Jian-feng WU, Peng-cheng LIU, Shi-wei CHENG

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 8,   Pages 1087-1098 doi: 10.1631/FITEE.1800083

Abstract: Classifying single-trial electroencephalogram (EEG) based motor imagery (MI) tasks is extensively used

Keywords： Electroencephalogram (EEG)     Motor imagery (MI)     Improved common spatial pattern (B-CSP)     Feature extraction    

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Electroencephalogram-based brain-computer interface for the Chinese spelling system: a survey None

Ming-hui SHI, Chang-le ZHOU, Jun XIE, Shao-zi LI, Qing-yang HONG, Min JIANG, Fei CHAO, Wei-feng REN, Xiang-qian LIU, Da-jun ZHOU, Tian-yu YANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 3,   Pages 423-436 doi: 10.1631/FITEE.1601509

Abstract: Electroencephalogram (EEG) based brain-computer interfaces allow users to communicate with the external

Keywords： Brain-computer interface (BCI)     Electroencephalography (EEG)     Chinese speller     English speller    

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Title Author Date Type Operation

Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent

Bang-hua YANG,Liang-fei HE,Lin LIN,Qian WANG

Journal Article

EEG-controlled functional electrical stimulation rehabilitation for chronic stroke: system design and clinical application

Journal Article

A personality-guided affective brain–computer interface for implementation of emotional intelligence in machines

Shaojie LI, Wei LI, Zejian XING, Wenjie YUAN, Xiangyu WEI, Xiaowei ZHANG, Bin HU,zhangxw@lzu.edu.cn,bh@lzu.edu.cn

Journal Article

Classification of EEG-based single-trial motor imagery tasks using aB-CSP method forBCI

Zhi-chuan TANG, Chao LI, Jian-feng WU, Peng-cheng LIU, Shi-wei CHENG

Journal Article

Electroencephalogram-based brain-computer interface for the Chinese spelling system: a survey

Ming-hui SHI, Chang-le ZHOU, Jun XIE, Shao-zi LI, Qing-yang HONG, Min JIANG, Fei CHAO, Wei-feng REN, Xiang-qian LIU, Da-jun ZHOU, Tian-yu YANG

Journal Article