Modeling EEG-based motor imagery with session to session online adaptation by Zhang, Zhuo, Foong, Ruyi, Phua, Kok Soon, Wang, Chuanchu, Ang, Kai Keng

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EEG-based emotion recognition using regularized graph neural networks by Zhong, Peixiang, Wang, Di, Miao, Chunyan

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LGGNet: learning from local-global-graph representations for brain-computer interface by Ding, Yi, Robinson, Neethu, Tong, Chengxuan, Zeng, Qiuhao, Guan, Cuntai

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Neurophysiological predictors and spectro-spatial discriminative features for enhancing SMR-BCI by Robinson, Neethu, Thomas, Kavitha Perumpadappil, Vinod, A P

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Adaptive transfer learning for EEG motor imagery classification with deep convolutional neural network by Zhang, Kaishuo, Robinson, Neethu, Lee, Seong-Whan, Guan, Cuntai

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A novel method of emergency situation detection for a brain-controlled vehicle by combining EEG signals with surrounding information by Bi, Luzheng, Wang, Huikang, Teng, Teng, Guan, Cuntai

“…In this paper, to address the safety of brain-controlled vehicles under emergency situations, we propose a novel method of emergency situation detection by fusing driver electroencephalography (EEG) signals with surrounding information. …”
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EEG-based biometric authentication using gamma band power during rest state by Thomas, Kavitha P., Vinod, Achutavarrier Prasad

“…Electroencephalography (EEG), one of the most effective noninvasive methods for recording brain’s electrical activity, has widely been employed in the diagnosis of brain diseases for a few decades. …”
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Parallel spatial-temporal self-attention CNN-based motor imagery classification for BCI by Liu, Xiuling, Shen, Yonglong, Liu, Jing, Yang, Jianli, Xiong, Peng, Lin, Feng

“…Motor imagery (MI) electroencephalography (EEG) classification is an important part of the brain-computer interface (BCI), allowing people with mobility problems to communicate with the outside world via assistive devices. …”
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MIN2Net: end-to-end multi-task learning for subject-independent motor imagery EEG classification by Autthasan, Phairot, Chaisaen, Rattanaphon, Sudhawiyangkul, Thapanun, Rangpong, Phurin, Kiatthaveephong, Suktipol, Dilokthanakul, Nat, Bhakdisongkhram, Gun, Phan, Huy, Guan, Cuntai, Wilaiprasitporn, Theerawit

“…Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. …”
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Tensor-CSPNet: a novel geometric deep learning framework for motor imagery classification by Ju, Ce, Guan, Cuntai

“…Deep learning (DL) has been widely investigated in a vast majority of applications in electroencephalography (EEG)-based brain-computer interfaces (BCIs), especially for motor imagery (MI) classification in the past five years. …”
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Improving SSVEP-BCI performance through repetitive anodal tDCS-based neuromodulation: insights from fractal EEG and brain functional connectivity by Zhang, Shangen, Cui, Hongyan, Li, Yong, Chen, Xiaogang, Gao, Xiaorong, Guan, Cuntai

“…This study embarks on a comprehensive investigation of the effectiveness of repetitive transcranial direct current stimulation (tDCS)-based neuromodulation in augmenting steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs), alongside exploring pertinent electroencephalography (EEG) biomarkers for assessing brain states and evaluating tDCS efficacy. …”
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μ-STAR: a novel framework for spatio-temporal M/EEG source imaging optimized by microstates by Feng, Zhao, Wang, Sujie, Qian, Linze, Xu, Mengru, Wu, Kuijun, Kakkos, Ioannis, Guan, Cuntai, Sun, Yu

“…Source imaging of Electroencephalography (EEG) and Magnetoencephalography (MEG) provides a noninvasive way of monitoring brain activities with high spatial and temporal resolution. …”
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Cross-modal credibility modelling for EEG-based multimodal emotion recognition by Zhang, Yuzhe, Liu, Huan, Wang, Di, Zhang, Dalin, Lou, Tianyu, Zheng, Qinghua, Quek, Chai

“…The study of emotion recognition through electroencephalography (EEG) has garnered significant attention recently. …”
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