Brain-Computer Interface: Advancement and Challenges
暂无分享,去创建一个
Yutaka Watanobe | Muhammad F. Mridha | Md. Rashedul Islam | Sujoy Chandra Das | Muhammad Mohsin Kabir | Aklima Akter Lima | Md. Rashedul Islam | M. Mridha | Y. Watanobe | S. Das
[1] P. Choyke,et al. Positron emission tomography (PET) radiotracers for prostate cancer imaging , 2020, Abdominal Radiology.
[2] Fabien Lotte,et al. Defining and quantifying users’ mental imagery-based BCI skills: a first step , 2018, Journal of neural engineering.
[3] Joseph Tabrikian,et al. Classification of multichannel EEG patterns using parallel hidden Markov models , 2012, Medical & Biological Engineering & Computing.
[4] Md. Rashedul Islam,et al. EEG Motor Signal Analysis-Based Enhanced Motor Activity Recognition Using Optimal De-noising Algorithm , 2019, IJCCI.
[5] Dennis J. McFarland,et al. Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.
[6] O. Sourina,et al. STEW: Simultaneous Task EEG Workload Data Set , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[7] Michael Erb,et al. Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data , 2003, NeuroImage.
[8] Mubarak Shah,et al. Brain2Image: Converting Brain Signals into Images , 2017, ACM Multimedia.
[9] Wonzoo Chung,et al. Frequency-Optimized Local Region Common Spatial Pattern Approach for Motor Imagery Classification , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[10] Bo Hong,et al. A practical VEP-based brain-computer interface , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[11] Gordon Cheng,et al. Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals , 2018, Sensors.
[12] Abeer Al-Nafjan,et al. Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network , 2017 .
[13] J. Dalling,et al. Long‐Term Persistence of Pioneer Species in Tropical Rain Forest Soil Seed Banks , 2009, The American Naturalist.
[14] Krzysztof A. Cyran,et al. A Machine Learning Approach to the Detection of Pilot's Reaction to Unexpected Events Based on EEG Signals , 2018, Comput. Intell. Neurosci..
[15] Yuan-Pin Lin,et al. Challenge for Affective Brain-Computer Interfaces: Non-stationary Spatio-spectral EEG Oscillations of Emotional Responses , 2019, Front. Hum. Neurosci..
[16] Zhaohui Yuan,et al. Identification of Motor and Mental Imagery EEG in Two and Multiclass Subject-Dependent Tasks Using Successive Decomposition Index , 2020, Sensors.
[17] Sangtae Ahn,et al. Achieving a hybrid brain–computer interface with tactile selective attention and motor imagery , 2014, Journal of neural engineering.
[18] G. Deco,et al. International Journal of Biological & Medical Research Detection of Primary Brain Tumor Present in Eeg Signal Using Wavelet Transform and Neural Network Keywords: International Journal of Biological and Medical Research Www.biomedscidirect.com Int J Biol Med Res , 2022 .
[19] Andrzej Cichocki,et al. Bimodal BCI Using Simultaneously NIRS and EEG , 2014, IEEE Transactions on Biomedical Engineering.
[20] Shiv K Mudgal,et al. Brain computer interface advancement in neurosciences: Applications and issues , 2020, Interdisciplinary Neurosurgery.
[21] Kazuhiko Takahashi. Remarks on Emotion Recognition from Bio-Potential Signals , 2004 .
[22] Fabien Lotte,et al. Online Classification accuracy is a Poor Metric to Study Mental imagery-based BCI User Learning: an Experimental Demonstration and New Metrics , 2017, GBCIC.
[23] Shuicheng Yan,et al. Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[24] F. Cincotti,et al. Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis , 2013, Front. Hum. Neurosci..
[25] Marios Poulos,et al. Is it possible to extract a fingerprint for early breast cancer via EEG analysis? , 2012, Medical hypotheses.
[26] Rajdeep Ghosh,et al. A Survey on Feature Extraction Methods for EEG Based Emotion Recognition , 2019, Learning and Analytics in Intelligent Systems.
[27] Xingyu Wang,et al. Cluster decomposing and multi-objective optimization based-ensemble learning framework for motor imagery-based brain–computer interfaces , 2021, Journal of neural engineering.
[28] Klaus-Robert Müller,et al. Descriptor : Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset , 2018 .
[29] Swati Vaid,et al. EEG Signal Analysis for BCI Interface: A Review , 2015, 2015 Fifth International Conference on Advanced Computing & Communication Technologies.
[30] C Grozea,et al. On the feasibility of using motor imagery EEG-based brain–computer interface in chronic tetraplegics for assistive robotic arm control: a clinical test and long-term post-trial follow-up , 2012, Spinal Cord.
[31] Yu Zhang,et al. Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces , 2018, Expert Syst. Appl..
[32] R Amirfattahi,et al. Enhancing P300 Wave of BCI Systems Via Negentropy in Adaptive Wavelet Denoising , 2011, Journal of medical signals and sensors.
[33] Christian Barillot,et al. A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback , 2020, Frontiers in Neuroscience.
[34] Eltaf Mohamed,et al. Enhancing EEG Signals in Brain Computer Interface Using Wavelet Transform , 2014 .
[35] Slim Essid,et al. Assessment of new spectral features for eeg-based emotion recognition , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[36] Ricardo Chavarriaga,et al. Applying Transfer Learning To Deep Learned Models For EEG Analysis , 2019, ArXiv.
[37] G. Schalk,et al. ECoG factors underlying multimodal control of a brain-computer interface , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[38] Rashmi Agrawal,et al. A Comparative Study of Linear and Non-Linear Classifiers in Sensory Motor Imagery Based Brain Computer Interface , 2019 .
[39] Julie A. E. Christensen,et al. Classification of iRBD and Parkinson's disease patients based on eye movements during sleep , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[40] Michael T. Johnson,et al. Speech signal enhancement through adaptive wavelet thresholding , 2007, Speech Commun..
[41] Sebastian Möller,et al. Neurophysiological experimental facility for Quality of Experience (QoE) assessment , 2013, 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013).
[42] A. Al-Ani,et al. Brain-Computer Interface Analysis using Continuous Wavelet Transform and Adaptive Neuro-Fuzzy Classifier , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[43] D. Cohen. Magnetoencephalography: Evidence of Magnetic Fields Produced by Alpha-Rhythm Currents , 1968, Science.
[44] Ali P. Yunus,et al. Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan , 2019, Landslides.
[45] Jinchang Ren,et al. EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges , 2019, Sensors.
[46] Azim Eskandarian,et al. EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification , 2021, Journal of neural engineering.
[47] S. Ali Etemad,et al. RFNet: Riemannian Fusion Network for EEG-based Brain-Computer Interfaces , 2020, ArXiv.
[48] Ana Loboda,et al. Discrimination of EEG-Based Motor Imagery Tasks by Means of a Simple Phase Information Method , 2014 .
[49] Howida A. Shedeed,et al. A CSP\AM-BA-SVM Approach for Motor Imagery BCI System , 2018, IEEE Access.
[50] Mehrnaz Fahimirad,et al. A Review on Application of Artificial Intelligence in Teaching and Learning in Educational Contexts , 2018, International Journal of Learning and Development.
[51] Steven Lemm,et al. BCI competition 2003-data set III: probabilistic modeling of sensorimotor /spl mu/ rhythms for classification of imaginary hand movements , 2004, IEEE Transactions on Biomedical Engineering.
[52] Tom Chau,et al. Partially supervised P300 speller adaptation for eventual stimulus timing optimization: target confidence is superior to error-related potential score as an uncertain label. , 2016, Journal of neural engineering.
[53] Edward Cutrell,et al. BCI for passive input in HCI , 2007 .
[54] John Williamson,et al. EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy , 2019, GigaScience.
[55] Stephen P. Hubbell,et al. Stochastically driven adult–recruit associations of tree species on Barro Colorado Island , 2013, Proceedings of the Royal Society B: Biological Sciences.
[56] Toby P. Breckon,et al. Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).
[57] Yuanqing Li,et al. Target Selection With Hybrid Feature for BCI-Based 2-D Cursor Control , 2012, IEEE Transactions on Biomedical Engineering.
[58] Tiago H. Falk,et al. Using deep neural networks for natural saccade classification from electroencephalograms , 2016, 2016 IEEE EMBS International Student Conference (ISC).
[59] Jesús González,et al. A new multi-objective wrapper method for feature selection - Accuracy and stability analysis for BCI , 2019, Neurocomputing.
[60] Toby P. Breckon,et al. On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-Based Bio-Signal Decoding in BCI Speller Applications , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[61] Damodar Reddy Edla,et al. Survey on Brain-Computer Interface , 2019, ACM Comput. Surv..
[62] Thierry Pun,et al. DEAP: A Database for Emotion Analysis ;Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.
[63] Martin Spüler,et al. Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity , 2015, Front. Hum. Neurosci..
[64] Anton Andreev,et al. Engineering study on the use of Head-Mounted display for Brain- Computer Interface , 2019, ArXiv.
[65] Yijun Wang,et al. A high-speed BCI based on code modulation VEP , 2011, Journal of neural engineering.
[66] Y. Hara. Brain plasticity and rehabilitation in stroke patients. , 2015, Journal of Nippon Medical School = Nippon Ika Daigaku zasshi.
[67] K. M. Ravi Kumar,et al. Analysis of EEG Based Emotion Detection of DEAP and SEED-IV Databases Using SVM , 2019, SSRN Electronic Journal.
[68] D. Lindsley. Psychological phenomena and the electroencephalogram. , 1952, Electroencephalography and clinical neurophysiology.
[69] Mahfuzah Mustafa,et al. Auditory Evoked Potential (AEP) Based Brain-Computer Interface (BCI) Technology: A Short Review , 2021 .
[70] Dat Tran,et al. Human identification with electroencephalogram (EEG) signal processing , 2012, 2012 International Symposium on Communications and Information Technologies (ISCIT).
[71] J J Vidal,et al. Toward direct brain-computer communication. , 1973, Annual review of biophysics and bioengineering.
[72] Muhammad Ghulam,et al. Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion , 2019, Future Gener. Comput. Syst..
[73] Min Hong,et al. Deep Learning in Physiological Signal Data: A Survey , 2020, Sensors.
[74] G. Pfurtscheller,et al. The BCI competition III: validating alternative approaches to actual BCI problems , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[75] Reza Fazel-Rezai,et al. A Review of Hybrid Brain-Computer Interface Systems , 2013, Adv. Hum. Comput. Interact..
[76] Fuchun Sun,et al. Deep Transfer Learning for EEG-Based Brain Computer Interface , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[77] Wolfgang Rosenstiel,et al. Online Adaptation of a c-VEP Brain-Computer Interface(BCI) Based on Error-Related Potentials and Unsupervised Learning , 2012, PloS one.
[78] S. Palazzo,et al. Deep Learning Human Mind for Automated Visual Classification , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[79] A. Frolov,et al. Brain-Computer Interface Based on Generation of Visual Images , 2011, PloS one.
[80] M. Z. Soroush,et al. A Review on EEG Signals Based Emotion Recognition , 2017 .
[81] Gérard Dreyfus,et al. A cognitive brain–computer interface monitoring sustained attentional variations during a continuous task , 2019, Cognitive Neurodynamics.
[82] Yan Liang,et al. Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection , 2020, Sensors.
[83] Y. F. Huang,et al. Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks , 2019, IEEE Access.
[84] P. Brown,et al. Adaptive deep brain stimulation in Parkinson's disease , 2016, Parkinsonism & related disorders.
[85] Marco Congedo,et al. EEG Alpha Waves Dataset , 2018 .
[86] Amjed S. Al-Fahoum,et al. Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains , 2014, ISRN neuroscience.
[87] John P. Cunningham,et al. Single-Neuron Stability during Repeated Reaching in Macaque Premotor Cortex , 2007, The Journal of Neuroscience.
[88] Jin-Woo Jeong,et al. Motor Imagery EEG Classification Using Capsule Networks† , 2019, Sensors.
[89] Ivan Volosyak,et al. Brain–computer interface using water-based electrodes , 2010, Journal of neural engineering.
[90] Heung-Il Suk,et al. Deep recurrent spatio-temporal neural network for motor imagery based BCI , 2018, 2018 6th International Conference on Brain-Computer Interface (BCI).
[91] Minkyu Ahn,et al. Journal of Neuroscience Methods , 2015 .
[92] Gernot R. Müller-Putz,et al. Single Versus Multiple Events Error Potential Detection in a BCI-Controlled Car Game With Continuous and Discrete Feedback , 2016, IEEE Transactions on Biomedical Engineering.
[93] Yuanqing Li,et al. An asynchronous wheelchair control by hybrid EEG–EOG brain–computer interface , 2014, Cognitive Neurodynamics.
[94] P. Mallikarjuna Rao,et al. A Neural Network Approach for EEG Classification in BCI , 2012 .
[95] Smith,et al. Mathematics of the Discrete Fourier Transform (DFT) with Audio Applications , 2007 .
[96] Stephan Waldert,et al. Invasive vs. Non-Invasive Neuronal Signals for Brain-Machine Interfaces: Will One Prevail? , 2016, Front. Neurosci..
[97] Chin-Teng Lin,et al. EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[98] Jianjun Wang,et al. A review of the commercial brain-computer interface technology from perspective of industrial robotics , 2010, 2010 IEEE International Conference on Automation and Logistics.
[99] Yang Li,et al. A Channel-Projection Mixed-Scale Convolutional Neural Network for Motor Imagery EEG Decoding , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[100] 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).
[101] Damodar Reddy Edla,et al. Classification of EEG Data using k-Nearest Neighbor approach for Concealed Information Test , 2018 .
[102] Mihaly Benda,et al. Asynchronous c-VEP communication tools—efficiency comparison of low-target, multi-target and dictionary-assisted BCI spellers , 2020, Scientific Reports.
[103] Xiaobo Sharon Hu,et al. Using EEG to Improve Massive Open Online Courses Feedback Interaction , 2013, AIED Workshops.
[104] Francisco J. Perales,et al. Evaluation of a VR system for Pain Management using binaural acoustic stimulation , 2019, Multimedia Tools and Applications.
[105] B. Hjorth. EEG analysis based on time domain properties. , 1970, Electroencephalography and clinical neurophysiology.
[106] Kouhyar Tavakolian,et al. Different classification techniques considering brain computer interface applications. , 2006, Journal of neural engineering.
[107] Joshua B Plotkin,et al. Seed Dispersal and Spatial Pattern in Tropical Trees , 2006, PLoS biology.
[108] Chee Peng Lim,et al. Classification of Multi-Class BCI Data by Common Spatial Pattern and Fuzzy System , 2018, IEEE Access.
[109] Anthony J. Ries,et al. The effect of target and non-target similarity on neural classification performance: a boost from confidence , 2015, Front. Neurosci..
[110] Priyanka S. Ghare,et al. Human emotion recognition using non linear and non stationary EEG signal , 2016, 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT).
[111] J. Polich. Updating P300: An integrative theory of P3a and P3b , 2007, Clinical Neurophysiology.
[112] Yijun Wang,et al. A Novel c-VEP BCI Paradigm for Increasing the Number of Stimulus Targets Based on Grouping Modulation With Different Codes , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[113] Akshaya R. Mane,et al. Review paper on Feature Extraction Methods for EEG Signal Analysis , 2015 .
[114] E. Donchin,et al. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.
[115] Evangelos Triantaphyllou,et al. A systematic survey of computer-aided diagnosis in medicine: Past and present developments , 2019, Expert Syst. Appl..
[116] A. Stevens,et al. Brain functional magnetic resonance imaging response to glucose and fructose infusions in humans , 2011, Diabetes, obesity & metabolism.
[117] F. Mohagheghian,et al. Computer-Aided Tinnitus Detection based on Brain Network Analysis of EEG Functional Connectivity , 2019, Journal of biomedical physics & engineering.
[118] M Congedo,et al. A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.
[119] C. Y. Lee,et al. Computing Intelligence Approach for an Eye State Classification with EEG Signal in BCI , 2016, International Conference on Software Engineering.
[120] Sandra Cancino,et al. Electrocorticographic signals classification for brain computer interfaces using stacked-autoencoders , 2020, Optical Engineering + Applications.
[121] Girijesh Prasad,et al. A Covariate Shift Minimisation Method to Alleviate Non-stationarity Effects for an Adaptive Brain-Computer Interface , 2010, 2010 20th International Conference on Pattern Recognition.
[122] Anton Nijholt,et al. Towards Practical Brain-Computer Interfaces: Bridging the Gap from Research to Real-World Applications , 2012 .
[123] Saeid Nahavandi,et al. Multiclass Informative Instance Transfer Learning Framework for Motor Imagery-Based Brain-Computer Interface , 2018, Comput. Intell. Neurosci..
[124] Robert H. Riffenburgh,et al. Linear Discriminant Analysis , 1960 .
[125] Guy Shani,et al. EEG-triggered dynamic difficulty adjustment for multiplayer games , 2018, Entertain. Comput..
[126] Zhen Yang,et al. A Voting Optimized Strategy Based on ELM for Improving Classification of Motor Imagery BCI Data , 2014, Cognitive Computation.
[127] K. Lafleur,et al. Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface , 2013, Journal of neural engineering.
[128] Barbara Kitchenham,et al. Procedures for Performing Systematic Reviews , 2004 .
[129] Jungpil Shin,et al. Optimal IMF Selection of EMD for Sleep Disorder Diagnosis using EEG Signals , 2018 .
[130] Shervin Shahryari,et al. Eye State Prediction from EEG Data Using Boosted Rotational Forests , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).
[131] Brent Lance,et al. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces , 2016, Journal of neural engineering.
[132] Cheolsoo Park,et al. Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[133] Franck Tarpin-Bernard,et al. Towards Brain Computer Interfaces for Recreational Activities: Piloting a Drone , 2015, INTERACT.
[134] Christa Neuper,et al. Error potential detection during continuous movement of an artificial arm controlled by brain–computer interface , 2012, Medical & Biological Engineering & Computing.
[135] Sadasivan Puthusserypady,et al. Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application , 2020, Comput. Biol. Medicine.
[136] Abbas Erfanian,et al. Improving the performance of brain-computer interface through meditation practicing , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[137] 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.
[138] Konstantinos N. Plataniotis,et al. Subject independent affective states classification using EEG signals , 2015, 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP).
[139] Mohammed Yeasin,et al. Spectrotemporal dynamics of the EEG during working memory encoding and maintenance predicts individual behavioral capacity , 2014, The European journal of neuroscience.
[140] Xiaowei Chen,et al. Continuous Convolutional Neural Network with 3D Input for EEG-Based Emotion Recognition , 2018, ICONIP.
[141] Toshihisa Tanaka,et al. Two-Stage Frequency Recognition Method Based on Correlated Component Analysis for SSVEP-Based BCI , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[142] O. Aasland,et al. Personality traits and drinking to cope as predictors of hazardous drinking among medical students. , 2004, Journal of studies on alcohol.
[143] P. Gomez-Gil,et al. A motor imagery BCI experiment using wavelet analysis and spatial patterns feature extraction , 2012, 2012 Workshop on Engineering Applications.
[144] Bao-Liang Lu,et al. A multimodal approach to estimating vigilance using EEG and forehead EOG , 2016, Journal of neural engineering.
[145] Mufti Mahmud,et al. Deep Learning in Mining Biological Data , 2020, Cognitive Computation.
[146] Bin He,et al. EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks , 2016, IEEE Transactions on Biomedical Engineering.
[147] Antonio Fernández-Caballero,et al. Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface , 2018, Electronics.
[148] T. Martin McGinnity,et al. Evaluating Quantum Neural Network filtered motor imagery brain-computer interface using multiple classification techniques , 2015, Neurocomputing.
[149] Stefan Haufe,et al. Optimizing event-related potential based brain-computer interfaces: a systematic evaluation of dynamic stopping methods. , 2013, Journal of neural engineering.
[150] Andrzej Cichocki,et al. Correlation-based channel selection and regularized feature optimization for MI-based BCI , 2019, Neural Networks.
[151] Jiali Li,et al. Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG , 2017, Sensors.
[152] Luigi Cinque,et al. An Affective BCI Driven by Self-induced Emotions for People with Severe Neurological Disorders , 2017, ICIAP Workshops.
[153] Stavros M. Panas,et al. Brain-Computer Interface (BCI): Types, Processing Perspectives and Applications , 2010 .
[154] Laxmidhar Behera,et al. Online Eye state recognition from EEG data using Deep architectures , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
[155] Bao-Guo Xu,et al. Pattern Recognition of Motor Imagery EEG using Wavelet Transform , 2008 .
[156] Sungho Jo,et al. BCI based hybrid interface for 3D object control in virtual reality , 2016, 2016 4th International Winter Conference on Brain-Computer Interface (BCI).
[157] Eros Gian Alessandro Pasero,et al. EEG Based Eye State Classification using Deep Belief Network and Stacked AutoEncoder , 2016 .
[158] Bin He,et al. Three-Dimensional Brain–Computer Interface Control Through Simultaneous Overt Spatial Attentional and Motor Imagery Tasks , 2018, IEEE Transactions on Biomedical Engineering.
[159] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[160] Grzegorz M. Wójcik,et al. Most Popular Signal Processing Methods in Motor-Imagery BCI: A Review and Meta-Analysis , 2018, Front. Neuroinform..
[161] Ricardo Soto,et al. Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition , 2018, Comput. Intell. Neurosci..
[162] Stefan Stenfelt,et al. A novel bone conduction implant (BCI): Engineering aspects and pre-clinical studies , 2010, International journal of audiology.
[163] John P. Cunningham,et al. A High-Performance Neural Prosthesis Enabled by Control Algorithm Design , 2012, Nature Neuroscience.
[164] Sriram Subramanian,et al. Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns , 2015, PloS one.
[165] Siew Wen Chin,et al. A mobile driver safety system: Analysis of single-channel EEG on drowsiness detection , 2014, 2014 International Conference on Computational Science and Technology (ICCST).
[166] Rajesh P. N. Rao,et al. Spontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Change , 2016, PLoS Comput. Biol..
[167] Fan-Gang Zeng,et al. Cochlear Implants: System Design, Integration, and Evaluation , 2008, IEEE Reviews in Biomedical Engineering.
[168] Rabab K Ward,et al. A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals , 2007, Journal of neural engineering.
[169] Mourad Ykhlef,et al. Deep Learning for EEG-Based Preference Classification in Neuromarketing , 2020, Applied Sciences.
[170] Andrzej Cichocki,et al. EmotionMeter: A Multimodal Framework for Recognizing Human Emotions , 2019, IEEE Transactions on Cybernetics.
[171] Yanhui Xu,et al. Classification Based on Multilayer Extreme Learning Machine for Motor Imagery Task from EEG Signals , 2016, BICA.
[172] Erhan Ekmekcioglu,et al. Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition , 2020, Sensors.
[173] Jong-Myon Kim,et al. Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors , 2016, J. Sensors.
[174] Monica-Claudia Dobrea,et al. The selection of proper discriminative cognitive tasks — A necessary prerequisite in high-quality BCI applications , 2009, 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies.
[175] Arne Robben,et al. Towards the detection of error-related potentials and its integration in the context of a P300 speller brain-computer interface , 2012, Neurocomputing.
[176] Gernot R. Müller-Putz,et al. A Single-Switch BCI Based on Passive and imagined movements: toward Restoring Communication in Minimally Conscious patients , 2013, Int. J. Neural Syst..
[177] Michael J. Black,et al. Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array , 2011 .
[178] Su Yang,et al. EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder , 2020, Frontiers in Systems Neuroscience.
[179] Esmeralda C. Djamal,et al. Brain Computer Interface Game Controlling Using Fast Fourier Transform and Learning Vector Quantization , 2017 .
[180] Leonardo Cunha de Miranda,et al. Brain–Computer Interface Games Based on Consumer-Grade EEG Devices: A Systematic Literature Review , 2019, Int. J. Hum. Comput. Interact..
[181] Wei Gao,et al. Multi-ganglion ANN based feature learning with application to P300-BCI signal classification , 2015, Biomed. Signal Process. Control..
[182] R. Flink,et al. Intraoperative electrocorticography in epilepsy surgery: useful or not? , 2003, Seizure.
[183] Jing Peng,et al. Comparing Linear Discriminant Analysis and Support Vector Machines , 2002, ADVIS.
[184] Anna M. Beres,et al. Time is of the Essence: A Review of Electroencephalography (EEG) and Event-Related Brain Potentials (ERPs) in Language Research , 2017, Applied Psychophysiology and Biofeedback.
[185] He Li,et al. Cross-Subject Emotion Recognition Using Deep Adaptation Networks , 2018, ICONIP.
[186] Jacob Cohen. A Coefficient of Agreement for Nominal Scales , 1960 .
[187] Andrzej Cichocki,et al. Task-Independent EEG Identification via Low-Rank Matrix Decomposition , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[188] Prasant Kumar Pattnaik,et al. Brain Computer Interface issues on hand movement , 2018, J. King Saud Univ. Comput. Inf. Sci..
[189] Adam G Rouse,et al. Differentiating closed-loop cortical intention from rest: building an asynchronous electrocorticographic BCI , 2013, Journal of neural engineering.
[190] Ad Aertsen,et al. Review of the BCI Competition IV , 2012, Front. Neurosci..
[191] T. Ward,et al. Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke. , 2015, Annals of physical and rehabilitation medicine.
[192] Chin-Teng Lin,et al. Driving fatigue prediction with pre-event electroencephalography (EEG) via a recurrent fuzzy neural network , 2016, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
[193] Ronald M. Aarts,et al. A Survey of Stimulation Methods Used in SSVEP-Based BCIs , 2010, Comput. Intell. Neurosci..
[194] R. Homan,et al. Cerebral location of international 10-20 system electrode placement. , 1987, Electroencephalography and clinical neurophysiology.
[195] Ricardo A. Ramirez-Mendoza,et al. Emotion recognition for semi-autonomous vehicles framework , 2018 .
[196] Dariusz Mikołajewski,et al. The prospects of brain — computer interface applications in children , 2014 .
[197] Fotis Liarokapis,et al. EEG-based BCI and video games: a progress report , 2018, Virtual Reality.
[198] Eric W. Sellers,et al. The effects of working memory on brain–computer interface performance , 2016, Clinical Neurophysiology.
[199] B. Caputo,et al. Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..
[200] Suguru Kanoga,et al. A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI , 2018, 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP).
[201] Horst Bischof,et al. Mahalanobis Distance Learning for Person Re-identification , 2014, Person Re-Identification.
[202] Cuntai Guan,et al. Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI , 2019, Journal of neural engineering.
[203] Tobias Kaufmann,et al. Long-term independent brain-computer interface home use improves quality of life of a patient in the locked-in state: a case study. , 2015, Archives of physical medicine and rehabilitation.
[204] Pablo Varona,et al. A Low-Cost Computational Method for Characterizing Event-Related Potentials for BCI Applications and Beyond , 2020, IEEE Access.
[205] Swati Aggarwal,et al. Signal processing techniques for motor imagery brain computer interface: A review , 2019, Array.
[206] Gunnar Blohm,et al. Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces , 2012, Front. Neurosci..
[207] Niels Birbaumer,et al. Brain–Machine Interfaces in Stroke Neurorehabilitation , 2015 .
[208] Chunyan Miao,et al. EEG-Based Emotion Recognition Using Regularized Graph Neural Networks , 2019, IEEE Transactions on Affective Computing.
[209] Abdulhamit Subasi,et al. EEG signal classification using PCA, ICA, LDA and support vector machines , 2010, Expert Syst. Appl..
[210] Anton Nijholt. The future of brain-computer interfacing (keynote paper) , 2016, 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV).
[211] Tom Chau,et al. Improving bit rate in an auditory BCI: Exploiting error-related potentials , 2016 .
[212] Yuanqing Li,et al. A comparison study of two P300 speller paradigms for brain–computer interface , 2013, Cognitive Neurodynamics.
[213] Chang-Hwan Im,et al. Classification of binary intentions for individuals with impaired oculomotor function: 'eyes-closed' SSVEP-based brain-computer interface (BCI). , 2013, Journal of neural engineering.
[214] A. Zabidi,et al. Short-time Fourier Transform analysis of EEG signal generated during imagined writing , 2012, 2012 International Conference on System Engineering and Technology (ICSET).
[215] Fakhreddine Ghaffari,et al. An embedded implementation based on adaptive filter bank for brain–computer interface systems , 2018, Journal of Neuroscience Methods.
[216] Antonio Fernández-Caballero,et al. Human-Avatar Symbiosis for the Treatment of Auditory Verbal Hallucinations in Schizophrenia through Virtual/Augmented Reality and Brain-Computer Interfaces , 2017, Front. Neuroinform..
[217] Luca Benini,et al. An Accurate EEGNet-based Motor-Imagery Brain–Computer Interface for Low-Power Edge Computing , 2020, 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA).
[218] Mubarak Shah,et al. ThoughtViz: Visualizing Human Thoughts Using Generative Adversarial Network , 2018, ACM Multimedia.
[219] Milos Ajcevic,et al. Effect of power feature covariance shift on BCI spatial-filtering techniques: A comparative study , 2020, Comput. Methods Programs Biomed..
[220] Saeid Nahavandi,et al. Active transfer learning and selective instance transfer with active learning for motor imagery based BCI , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[221] Yu Sun,et al. Functional Connectivity for Motor Imaginary Recognition in Brain-computer Interface , 2020, 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
[222] Lev Stankevich,et al. Development of electroencephalographic pattern classifiers for real and imaginary thumb and index finger movements of one hand , 2015, Artif. Intell. Medicine.
[223] Siyi Deng,et al. EEG Surface Laplacian using realistic head geometry , 2011 .
[224] Roman Moucek,et al. Event-related potential datasets based on a three-stimulus paradigm , 2014, GigaScience.
[225] Honghao Gao,et al. A Multi-Scale Activity Transition Network for Data Translation in EEG Signals Decoding , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[226] Minoru Ohyama,et al. Feature parameters of eye blinks when the sampling rate is changed , 2014, TENCON 2014 - 2014 IEEE Region 10 Conference.
[227] Sung Chan Jun,et al. High Theta and Low Alpha Powers May Be Indicative of BCI-Illiteracy in Motor Imagery , 2013, PloS one.
[228] Cuntai Guan,et al. BCI for stroke rehabilitation: motor and beyond , 2020, Journal of neural engineering.
[229] Urbano J. Nunes,et al. Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[230] Wolfram Burgard,et al. Deep learning with convolutional neural networks for EEG decoding and visualization , 2017, Human brain mapping.
[231] Luca Mainardi,et al. Performance measurement for brain–computer or brain–machine interfaces: a tutorial , 2014, Journal of neural engineering.
[232] Yangsong Zhang,et al. Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index , 2016, Cognitive Neurodynamics.
[233] Xianzhi Wang,et al. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers , 2019, Journal of neural engineering.
[234] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[235] Mohammad Soleymani,et al. Multimedia implicit tagging using EEG signals , 2013, 2013 IEEE International Conference on Multimedia and Expo (ICME).
[236] Hubert Cecotti,et al. Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[237] Francisco Velasco-Álvarez,et al. Audio-cued motor imagery-based brain-computer interface: Navigation through virtual and real environments , 2013, Neurocomputing.
[238] Girijesh Prasad,et al. Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface , 2015, Soft Computing.
[239] Dewen Hu,et al. Towards BCI-actuated smart wheelchair system , 2018, BioMedical Engineering OnLine.
[240] Yi-Hung Liu,et al. A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector , 2016 .
[241] V. Sinha,et al. Event-related potential: An overview , 2009, Industrial psychiatry journal.
[242] Yu Zhang,et al. EEG classification using sparse Bayesian extreme learning machine for brain–computer interface , 2018, Neural Computing and Applications.
[243] Byron M. Yu,et al. Brain–computer interfaces for dissecting cognitive processes underlying sensorimotor control , 2016, Current Opinion in Neurobiology.
[244] Young Min Jhon,et al. All-Optical AND Gate Using Cross-Gain Modulation in Semiconductor Optical Amplifiers , 2004 .
[245] Yifan Xu,et al. Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 , 2020, IEEE Transactions on Cognitive and Developmental Systems.
[246] Wolfgang Rosenstiel,et al. One Class SVM and Canonical Correlation Analysis increase performance in a c-VEP based Brain-Computer Interface (BCI) , 2012, ESANN.
[247] Wei Liu,et al. Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis , 2019, ArXiv.
[248] Daniel Charnay. Centre pour la Communication Scientifique Directe , 2003 .
[249] Onder Aydemir,et al. A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data , 2010, Pattern Recognit. Lett..
[250] Yijun Wang,et al. Control of a 7-DOF Robotic Arm System With an SSVEP-Based BCI , 2018, Int. J. Neural Syst..
[251] PAUL J. WERBOS,et al. Generalization of backpropagation with application to a recurrent gas market model , 1988, Neural Networks.
[252] Sang Guun Yoo,et al. EEG-Based BCI Emotion Recognition: A Survey , 2020, Sensors.
[253] M J Stokes,et al. EEG-based communication: a pattern recognition approach. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[254] Gaye Lightbody,et al. Investigating the use of brain-computer interaction to facilitate creativity , 2012, AH '12.
[255] Areej Al-Wabil,et al. Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review , 2017 .
[256] Jaime Gómez Gil,et al. Brain Computer Interfaces, a Review , 2012, Sensors.
[257] M Congedo,et al. sw-SVM: sensor weighting support vector machines for EEG-based brain–computer interfaces , 2011, Journal of neural engineering.
[258] P. Sajda,et al. Regulation of arousal via online neurofeedback improves human performance in a demanding sensory-motor task , 2018, Proceedings of the National Academy of Sciences.
[259] J. Zimmerman,et al. Design and Operation of Stable rf‐Biased Superconducting Point‐Contact Quantum Devices, and a Note on the Properties of Perfectly Clean Metal Contacts , 1970 .
[260] Brendan Z. Allison,et al. Comparison of Dry and Gel Based Electrodes for P300 Brain–Computer Interfaces , 2012, Front. Neurosci..
[261] Gustavo P. Sudre,et al. Decoding semantic information from human electrocorticographic (ECoG) signals , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[262] Gordon Cheng,et al. A closed-loop brain-computer music interface for continuous affective interaction , 2017, 2017 International Conference on Orange Technologies (ICOT).
[263] G. Prasad,et al. Single-trial effective brain connectivity patterns enhance discriminability of mental imagery tasks , 2017, Journal of neural engineering.
[264] J. H. Hong,et al. Gamma band activity associated with BCI performance: simultaneous MEG/EEG study , 2013, Front. Hum. Neurosci..
[265] J. Wolpaw,et al. An exploration of BCI performance variations in people with amyotrophic lateral sclerosis using longitudinal EEG data , 2019, Journal of neural engineering.
[266] Tushar Kanti Bera,et al. Noninvasive Electromagnetic Methods for Brain Monitoring: A Technical Review , 2015, Brain-Computer Interfaces.
[267] T. Huisman. Diffusion-weighted and diffusion tensor imaging of the brain, made easy , 2010, Cancer imaging : the official publication of the International Cancer Imaging Society.
[268] Nicholas L Opie,et al. Sensor Modalities for Brain-Computer Interface Technology: A Comprehensive Literature Review. , 2019, Neurosurgery.
[269] Reza Abiri,et al. A comprehensive review of EEG-based brain–computer interface paradigms , 2019, Journal of neural engineering.
[270] Norizam Sulaiman,et al. Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review , 2020, Frontiers in Neurorobotics.
[271] Yijun Wang,et al. VEP-based brain-computer interfaces: time, frequency, and code modulations [Research Frontier] , 2009, IEEE Computational Intelligence Magazine.
[272] A OjoJ.,et al. Comparative Analysis of Textural Features Derived from GLCM for Ultrasound Liver Image Classification , 2014 .
[273] A. Krzyzak,et al. Analysis and correction of errors in DTI-based tractography due to diffusion gradient inhomogeneity. , 2018, Journal of magnetic resonance.
[274] José del R. Millán,et al. Brain-Computer Interfaces , 2020, Handbook of Clinical Neurology.
[275] Gernot R Müller-Putz,et al. Masked and unmasked error-related potentials during continuous control and feedback , 2018, Journal of neural engineering.
[276] Siobhán Harty,et al. Towards error categorisation in BCI: single-trial EEG classification between different errors , 2019, Journal of neural engineering.
[277] David Suendermann,et al. A First Step towards Eye State Prediction Using EEG , 2013 .
[278] Sung Chan Jun,et al. EEG datasets for motor imagery brain–computer interface , 2017, GigaScience.
[279] G. Hajcak,et al. Event-Related Potentials, Emotion, and Emotion Regulation: An Integrative Review , 2010, Developmental neuropsychology.
[280] Xingyu Wang,et al. Optimized stimulus presentation patterns for an event-related potential EEG-based brain–computer interface , 2011, Medical & Biological Engineering & Computing.
[281] Lei Sun,et al. A contralateral channel guided model for EEG based motor imagery classification , 2018, Biomed. Signal Process. Control..
[282] Stephen J. Roberts,et al. A self-paced brain–computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training , 2009, Medical & Biological Engineering & Computing.
[283] Jiankun Hu,et al. Deep Gaussian Mixture-Hidden Markov Model for Classification of EEG Signals , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.
[284] Christa Neuper,et al. Motor imagery and EEG-based control of spelling devices and neuroprostheses. , 2006, Progress in brain research.
[285] Yangsong Zhang,et al. Prediction of SSVEP-based BCI performance by the resting-state EEG network , 2013, Journal of neural engineering.
[286] Inés María Galván,et al. Evolving spatial and frequency selection filters for Brain-Computer Interfaces , 2010, IEEE Congress on Evolutionary Computation.
[287] Guillaume Gibert,et al. xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface , 2009, IEEE Transactions on Biomedical Engineering.
[288] Pearl Brereton,et al. Performing systematic literature reviews in software engineering , 2006, ICSE.
[289] Fabien Lotte,et al. Brain-Computer Interfaces: Beyond Medical Applications , 2012, Computer.
[290] J. Wolpaw,et al. Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis , 2014, Amyotrophic lateral sclerosis & frontotemporal degeneration.
[291] Dezhong Yao,et al. Separated channel convolutional neural network to realize the training free motor imagery BCI systems , 2019, Biomed. Signal Process. Control..
[292] Raveendran Paramesran,et al. VEP optimal channel selection using genetic algorithm for neural network classification of alcoholics , 2002, IEEE Trans. Neural Networks.
[293] Amit Konar,et al. A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection , 2017, Front. Neurosci..
[294] Shaowen Yao,et al. Deep Fusion Feature Learning Network for MI-EEG Classification , 2018, IEEE Access.
[295] Min Liu,et al. A Deep Transfer Convolutional Neural Network Framework for EEG Signal Classification , 2019, IEEE Access.
[296] Ning Ye,et al. EEG Analysis of Alcoholics and Controls Based on Feature Extraction , 2006, 2006 8th international Conference on Signal Processing.
[297] Mahmut Tokmakçi,et al. Optimization of preprocessing stage in EEG based BCI systems in terms of accuracy and timing cost , 2021, Biomed. Signal Process. Control..
[298] Miseon Shim,et al. Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features , 2016, Schizophrenia Research.
[299] Ram Bilas Pachori,et al. A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI , 2021, IEEE Transactions on Instrumentation and Measurement.
[300] J. Blumberg,et al. Adaptive Classification for Brain Computer Interfaces , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[301] Luca Benini,et al. Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).
[302] Christoph Guger,et al. A BCI using VEP for continuous control of a mobile robot , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[303] Christa Neuper,et al. Hidden Markov models for online classification of single trial EEG data , 2001, Pattern Recognit. Lett..
[304] Debi Prosad Dogra,et al. Analysis of EEG signals and its application to neuromarketing , 2017, Multimedia Tools and Applications.
[305] He Li,et al. Reducing the Subject Variability of EEG Signals with Adversarial Domain Generalization , 2019, ICONIP.
[306] Gernot R. Müller-Putz,et al. Domain Adaptation Techniques for EEG-Based Emotion Recognition: A Comparative Study on Two Public Datasets , 2019, IEEE Transactions on Cognitive and Developmental Systems.
[307] M Congedo,et al. A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update , 2018, Journal of neural engineering.
[308] Shuicheng Yan,et al. Parallel convolutional-linear neural network for motor imagery classification , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).
[309] Orhan Arikan,et al. Short-time Fourier transform: two fundamental properties and an optimal implementation , 2003, IEEE Trans. Signal Process..
[310] Scott Makeig,et al. High-frequency Broadband Modulations of Electroencephalographic Spectra , 2009, Front. Hum. Neurosci..
[311] Feng Duan,et al. Design of a Multimodal EEG-based Hybrid BCI System with Visual Servo Module , 2015, IEEE Transactions on Autonomous Mental Development.
[312] Bao-Liang Lu,et al. EEG-Based Emotion Recognition Using Frequency Domain Features and Support Vector Machines , 2011, ICONIP.
[313] P. Piccini,et al. Applications of positron emission tomography (PET) in neurology , 2004, Journal of Neurology, Neurosurgery & Psychiatry.
[314] Damodar Reddy Edla,et al. Brain computer interface: A comprehensive survey , 2018, Biologically Inspired Cognitive Architectures.
[315] Aditya Inamdar,et al. A review of recent trends in EEG based Brain-Computer Interface , 2019, 2019 International Conference on Computational Intelligence in Data Science (ICCIDS).
[316] John Atkinson,et al. Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers , 2016, Expert Syst. Appl..
[317] Ayman AbuBaker,et al. EEG Mouse:A Machine Learning-Based Brain Computer Interface , 2014 .
[318] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[319] R. Ward,et al. EMG and EOG artifacts in brain computer interface systems: A survey , 2007, Clinical Neurophysiology.
[320] L. H. Viet,et al. Emotion Detection in the Loop from Brain Signals and Facial Images , 2006 .
[321] Masaki Nakanishi,et al. Assessing the effects of voluntary and involuntary eyeblinks in independent components of electroencephalogram , 2016, Neurocomputing.
[322] R. Veit,et al. Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI) , 2004, Journal of Physiology-Paris.
[323] Yijun Wang,et al. Visual and Auditory Brain–Computer Interfaces , 2014, IEEE Transactions on Biomedical Engineering.
[324] Pegah Sarkheil,et al. Targeting Treatment-Resistant Auditory Verbal Hallucinations in Schizophrenia with fMRI-Based Neurofeedback – Exploring Different Cases of Schizophrenia , 2016, Front. Psychiatry.
[325] J. Wolpaw,et al. P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls , 2015, Clinical Neurophysiology.
[326] Murat Kaya,et al. A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces , 2018, Scientific Data.
[327] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[328] Raghupathy Sivakumar,et al. Charge for a whole day: Extending Battery Life for BCI Wearables using a Lightweight Wake-Up Command , 2020, CHI.
[329] Christian Jutten,et al. Multiclass Brain–Computer Interface Classification by Riemannian Geometry , 2012, IEEE Transactions on Biomedical Engineering.
[330] Rex B. Kline,et al. Usability measurement and metrics: A consolidated model , 2006, Software Quality Journal.
[331] Xudong Jiang,et al. Feature Weighting and Regularization of Common Spatial Patterns in EEG-Based Motor Imagery BCI , 2018, IEEE Signal Processing Letters.
[332] Athanasios V. Vasilakos,et al. Brain computer interface: control signals review , 2017, Neurocomputing.
[333] Rihab Bousseta,et al. EEG Based Brain Computer Interface for Controlling a Robot Arm Movement Through Thought , 2018 .
[334] Tzyy-Ping Jung,et al. Spatial Filtering for EEG-Based Regression Problems in Brain–Computer Interface (BCI) , 2017, IEEE Transactions on Fuzzy Systems.
[335] Joydeep Ghosh,et al. HMMs and Coupled HMMs for multi-channel EEG classification , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).
[336] Joseph T. Coyne,et al. Applying Real Time Physiological Measures of Cognitive Load to Improve Training , 2009, HCI.
[337] K. V. Suma,et al. EEG based emotion recognition using SVM and PSO , 2017, 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT).
[338] Anton Nijholt,et al. BrainBrush, a Multimodal Application for Creative Expressivity , 2013, ACHI 2013.
[339] Xiaogang Chen,et al. A Benchmark Dataset for SSVEP-Based Brain–Computer Interfaces , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[340] Dharmendra Sharma,et al. A Proposed Feature Extraction Method for EEG-based Person Identification , 2012 .
[341] Raghupathy Sivakumar,et al. Blink: A Fully Automated Unsupervised Algorithm for Eye-Blink Detection in EEG Signals , 2019, 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[342] Aurobinda Routray,et al. Statistical features extraction for multivariate pattern analysis in meditation EEG using PCA , 2016, 2016 IEEE EMBS International Student Conference (ISC).
[343] Chong Liu,et al. EEG classification for multiclass motor imagery BCI , 2013, 2013 25th Chinese Control and Decision Conference (CCDC).
[344] Sang Joon Kim,et al. A Mathematical Theory of Communication , 2006 .