Subject Adaptive EEG-based Visual Recognition

[1]  Mohammad Soleymani,et al.  A Survey on Neuromarketing Using EEG Signals , 2021, IEEE Transactions on Cognitive and Developmental Systems.

[2]  Hyeran Byun,et al.  Feature Stylization and Domain-aware Contrastive Learning for Domain Generalization , 2021, ACM Multimedia.

[3]  M. Zhuravlev,et al.  Changes in EEG Alpha Activity during Attention Control in Patients: Association with Sleep Disorders , 2021, Journal of personalized medicine.

[4]  Hyeran Byun,et al.  Mitigating Inter-Subject Brain Signal Variability FOR EEG-Based Driver Fatigue State Classification , 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[5]  Andrew D. Wilson,et al.  Decoding Music Attention from “EEG Headphones”: A User-Friendly Auditory Brain-Computer Interface , 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  Fei Wang,et al.  The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning , 2021, Sensors.

[7]  Learning Subject-independent Representation for EEG-based Drowsy Driving Detection , 2021, 2021 9th International Winter Conference on Brain-Computer Interface (BCI).

[8]  Jiahui Pan,et al.  An EEG-Based Brain Computer Interface for Emotion Recognition and Its Application in Patients with Disorder of Consciousness , 2019, IEEE Transactions on Affective Computing.

[9]  Seong-Whan Lee,et al.  Neural Decoding of Imagined Speech and Visual Imagery as Intuitive Paradigms for BCI Communication , 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Andrea Kübler,et al.  Wheelchair Control in a Virtual Environment by Healthy Participants Using a P300-BCI Based on Tactile Stimulation: Training Effects and Usability , 2020, Frontiers in Human Neuroscience.

[11]  Hyeran Byun,et al.  Subject-Independent EEG-based Emotion Recognition using Adversarial Learning , 2020, 2020 8th International Winter Conference on Brain-Computer Interface (BCI).

[12]  Heung-Il Suk,et al.  VIGNet: A Deep Convolutional Neural Network for EEG-based Driver Vigilance Estimation , 2020, 2020 8th International Winter Conference on Brain-Computer Interface (BCI).

[13]  O. Osman,et al.  Effect of brightness of visual stimuli on EEG signals , 2019, Behavioural Brain Research.

[14]  Minkyu Ahn,et al.  CNN With Large Data Achieves True Zero-Training in Online P300 Brain-Computer Interface , 2020, IEEE Access.

[15]  Hyeran Byun,et al.  Learning CNN features from DE features for EEG-based emotion recognition , 2019, Pattern Analysis and Applications.

[16]  Dilip Singh Sisodia,et al.  Alcohol use disorder detection using EEG Signal features and flexible analytical wavelet transform , 2019, Biomed. Signal Process. Control..

[17]  Tomasz M. Rutkowski,et al.  Brain Correlates of Task–Load and Dementia Elucidation with Tensor Machine Learning Using Oddball BCI Paradigm , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[18]  Lina Yao,et al.  A Convolutional Recurrent Attention Model for Subject-Independent EEG Signal Analysis , 2019, IEEE Signal Processing Letters.

[19]  Hyeran Byun,et al.  EZSL-GAN: EEG-based Zero-Shot Learning approach using a Generative Adversarial Network , 2019, 2019 7th International Winter Conference on Brain-Computer Interface (BCI).

[20]  Shuai Wang,et al.  EEG Classification of Motor Imagery Using a Novel Deep Learning Framework , 2019, Sensors.

[21]  Kebin Jia,et al.  A Multi-View Deep Learning Framework for EEG Seizure Detection , 2019, IEEE Journal of Biomedical and Health Informatics.

[22]  Rui Cao,et al.  Epileptic Seizure Detection Based on EEG Signals and CNN , 2018, Front. Neuroinform..

[23]  Yu Zhang,et al.  EEG classification using sparse Bayesian extreme learning machine for brain–computer interface , 2018, Neural Computing and Applications.

[24]  Sanchita Paul,et al.  Detection of major depressive disorder using linear and non-linear features from EEG signals , 2018, Microsystem Technologies.

[25]  Debi Prosad Dogra,et al.  Envisioned speech recognition using EEG sensors , 2018, Personal and Ubiquitous Computing.

[26]  Mubarak Shah,et al.  Brain2Image: Converting Brain Signals into Images , 2017, ACM Multimedia.

[27]  Wolfram Burgard,et al.  Deep learning with convolutional neural networks for EEG decoding and visualization , 2017, Human brain mapping.

[28]  S. Palazzo,et al.  Deep Learning Human Mind for Automated Visual Classification , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Andrea Petracca,et al.  Classification of Emotional Signals from the DEAP dataset , 2016, NEUROTECHNIX.

[30]  Mengjie Zhang,et al.  Domain Generalization for Object Recognition with Multi-task Autoencoders , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[31]  Michael I. Jordan,et al.  Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.

[32]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[33]  Pierre-Yves Oudeyer,et al.  Calibration-Free BCI Based Control , 2014, AAAI.

[34]  Guido Sanguinetti,et al.  Single-trial classification of EEG in a visual object task using ICA and machine learning , 2014, Journal of Neuroscience Methods.

[35]  Chunshui Yu,et al.  Neural Pathways Conveying Novisual Information to the Visual Cortex , 2013, Neural plasticity.

[36]  Rajesh Singla,et al.  Virtual keyboard BCI using Eye blinks in EEG , 2010, 2010 IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications.

[37]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  Vera Kaiser,et al.  BCI Applications for People with Disabilities: Defining User Needs and User Requirements , 2009 .

[39]  Boris Reuderink,et al.  BrainBasher: a BCI Game , 2008 .

[40]  N. Ramsey,et al.  Towards human BCI applications based on cognitive brain systems: an investigation of neural signals recorded from the dorsolateral prefrontal cortex , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[41]  Bernhard Schölkopf,et al.  A Primer on Kernel Methods , 2004 .

[42]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[43]  Seppo P. Ahlfors,et al.  Parieto‐occipital ∼1 0Hz activity reflects anticipatory state of visual attention mechanisms , 1998 .

[44]  S. Kosslyn,et al.  Reactivity of magnetic parieto-occipital alpha rhythm during visual imagery. , 1995, Electroencephalography and clinical neurophysiology.