21.9.8 Brain, Cortex, Brain Waves, EEG Analysis, Electroencephalogram

Haor, D., Shavit, R., Shapiro, M., Geva, A.B.,
Back-Projection Cortical Potential Imaging: Theory and Results,
MedImg(36), No. 7, July 2017, pp. 1583-1595.
IEEE DOI 1707
Brain modeling, Electric potential, Electroencephalography, Estimation, Imaging, Scalp, Cortical potential imaging, backprojection, finite element method, head modeling, surface, Laplacian BibRef

Li, F., Zhang, G., Wang, W., Xu, R., Schnell, T., Wen, J., McKenzie, F., Li, J.,
Deep Models for Engagement Assessment With Scarce Label Information,
HMS(47), No. 4, August 2017, pp. 598-605.
IEEE DOI 1708
Brain models, Data models, Electroencephalography, Feature extraction, Machine learning, Training, Deep learning, electroencephalography (EEG), engagement assessment, scarce, label, information BibRef

Gupta, V.[Vipin], Priya, T.[Tanvi], Yadav, A.K.[Abhishek Kumar], Pachori, R.B.[Ram Bilas], Acharya, U.R.[U. Rajendra],
Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform,
PRL(94), No. 1, 2017, pp. 180-188.
Elsevier DOI 1708
EEG BibRef

Arunkumar, N., Ramkumar, K., Venkatraman, V., Abdulhay, E.[Enas], Fernandes, S.L.[Steven Lawrence], Kadry, S.[Seifedine], Segal, S.[Sophia],
Classification of focal and non focal EEG using entropies,
PRL(94), No. 1, 2017, pp. 112-117.
Elsevier DOI 1708
Classification BibRef

Muhammad Umar Saeed, S.[Sanay], Muhammad Anwar, S.[Syed], Majid, M.[Muhammad],
Quantification of Human Stress Using Commercially Available Single Channel EEG Headset,
IEICE(E100-D), No. 9, September 2017, pp. 2241-2244.
WWW Link. 1709
BibRef

Khan, M.S., Deng, H.,
Design and Prototyping a Smart Deep Brain Stimulator: An Autonomous Neuro-Sensing and Stimulating Electrode System,
IEEE_Int_Sys(32), No. 5, September 2017, pp. 14-27.
IEEE DOI 1710
Artificial intelligence, Brain models, Electrodes, Implants, Micromechanical devices, Process control, BibRef

Mu, Z.D.[Zhen-Dong], Hu, J.F.[Jian-Feng], Min, J.L.[Jian-Liang], Yin, J.H.[Jing-Hai],
Comparison of different entropies as features for person authentication based on EEG signals,
IET-Bio(6), No. 6, November 2017, pp. 409-417.
DOI Link 1711
BibRef

Hashemi, S.[Soheil], Abdolali, A.[Ali],
Three-dimensional FDTD modeling of neurons to solve EEG and MEG forward problem,
IJIST(27), No. 4, 2017, pp. 361-367.
DOI Link 1712
brain function, electroencephalogram and magnetoencephalography analysis, neurons BibRef

Jiao, Z.C.[Zhi-Cheng], Gao, X.B.[Xin-Bo], Wang, Y.[Ying], Li, J.[Jie], Xu, H.J.[Hao-Jun],
Deep Convolutional Neural Networks for mental load classification based on EEG data,
PR(76), No. 1, 2018, pp. 582-595.
Elsevier DOI 1801
Deep learning BibRef

Xu, M.Y.[Mei-Yan], Yao, J.F.[Jun-Feng], Zhang, Z.H.[Zhi-Hong], Li, R.[Rui], Yang, B.R.[Bao-Rong], Li, C.Y.[Chun-Yan], Li, J.[Jun], Zhang, J.S.[Jun-Song],
Learning EEG Topographical Representation for Classification Via Convolutional Neural Network,
PR(105), 2020, pp. 107390.
Elsevier DOI 2006
Motor imagery, Electroencephalography topographical representation, Signal pre-processing BibRef

Rutigliano, T., Rivolta, M.W., Pizzi, R., Sassi, R.,
Composition of Feature Extraction Methods Shows Interesting Performances in Discriminating Wakefulness and NREM Sleep,
SPLetters(25), No. 2, February 2018, pp. 204-208.
IEEE DOI 1802
brain, electroencephalography, feature extraction, medical signal processing, neural nets, neurophysiology, sleep, wavelet transforms (WT) BibRef

Tsumugiwa, T., Shibata, A., Yokogawa, R.,
Analysis of Upper-Extremity Motion and Muscle and Brain Activation During Machine Operation in Consideration of Mass and Friction,
HMS(48), No. 2, April 2018, pp. 161-171.
IEEE DOI 1804
Brain, Extremities, Force, Friction, Impedance, Muscles, Task analysis, Brain activity, electromyography (EMG), near-infrared spectroscopy (NIRS) BibRef

Chauhan, M., Indahlastari, A., Kasinadhuni, A.K., Schär, M., Mareci, T.H., Sadleir, R.J.,
Low-Frequency Conductivity Tensor Imaging of the Human Head In Vivo Using DT-MREIT: First Study,
MedImg(37), No. 4, April 2018, pp. 966-976.
IEEE DOI 1804
Conductivity, Electrodes, Image reconstruction, Tensile stress, Tomography, Inverse electroencephalogram (EEG), MREIT, tDCS BibRef

Hassan, M., Wendling, F.,
Electroencephalography Source Connectivity: Aiming for High Resolution of Brain Networks in Time and Space,
SPMag(35), No. 3, May 2018, pp. 81-96.
IEEE DOI 1805
Biomedical signal processing, Brain modeling, Couplings, Electrodes, Electroencephalography, Neuroimaging, Neuroscience, Time series analysis BibRef

Mheich, A., Hassan, M., Khalil, M., Gripon, V., Dufor, O., Wendling, F.,
SimiNet: A Novel Method for Quantifying Brain Network Similarity,
PAMI(40), No. 9, September 2018, pp. 2238-2249.
IEEE DOI 1808
Indexes, Algorithm design and analysis, Brain, Image edge detection, Visualization, Animals, Tools, spatial information BibRef

Wu, H.Y.[Hui-Ying], Xu, Y.K.[Yi-Kai], Xu, W.[Wenbiao],
Imaging manifestations of sepsis-associated encephalopathy,
IJIST(28), No. 3, September 2018, pp. 196-206.
WWW Link. 1808
BibRef

Bosse, S., Acqualagna, L., Samek, W., Porbadnigk, A.K., Curio, G., Blankertz, B., Müller, K., Wiegand, T.,
Assessing Perceived Image Quality Using Steady-State Visual Evoked Potentials and Spatio-Spectral Decomposition,
CirSysVideo(28), No. 8, August 2018, pp. 1694-1706.
IEEE DOI 1808
Visualization, Electroencephalography, Electric potential, Quality assessment, Correlation, Distortion, Electrodes, EEG, SSVEP, spatio-spectral decomposition BibRef

Mert, A.[Ahmet], Akan, A.[Aydin],
Seizure onset detection based on frequency domain metric of empirical mode decomposition,
SIViP(12), No. 8, November 2018, pp. 1489-1496.
WWW Link. 1809
BibRef

Ranjan, R.[Rakesh], Arya, R.[Rajeev], Fernandes, S.L.[Steven Lawrence], Sravya, E.[Erukonda], Jain, V.[Vinay],
A fuzzy neural network approach for automatic K-complex detection in sleep EEG signal,
PRL(115), 2018, pp. 74-83.
Elsevier DOI 1812
K-complex, Sleep EEG, Savitzky-Golay filter, NREM sleep, Fuzzy neural system, Artificial neural network, Back Proportion algorithm BibRef

Kim, S., Ba, D., Brown, E.N.,
A Multitaper Frequency-Domain Bootstrap Method,
SPLetters(25), No. 12, December 2018, pp. 1805-1809.
IEEE DOI 1812
bioelectric potentials, bootstrapping, electroencephalography, inference mechanisms, medical signal processing, spectra resampling BibRef

Labyt, E., Corsi, M., Fourcault, W., Palacios Laloy, A., Bertrand, F., Lenouvel, F., Cauffet, G., Le Prado, M., Berger, F., Morales, S.,
Magnetoencephalography With Optically Pumped4He Magnetometers at Ambient Temperature,
MedImg(38), No. 1, January 2019, pp. 90-98.
IEEE DOI 1901
Magnetometers, Magnetic fields, Magnetic field measurement, Phantoms, SQUIDs, Optical pumping, Temperature measurement, spontaneous activity BibRef

Arapakis, I., Barreda-Ángeles, M., Pereda-Baños, A.,
Interest as a Proxy of Engagement in News Reading: Spectral and Entropy Analyses of EEG Activity Patterns,
AffCom(10), No. 1, January 2019, pp. 100-114.
IEEE DOI 1903
Media, FAA, Electroencephalography, Psychology, Hip, Entropy, User engagement, news consumption, EEG, spectral analysis, predictive modelling BibRef

Hatipoglu, B.[Bahar], Yilmaz, C.M.[Cagatay Murat], Kose, C.[Cemal],
A signal-to-image transformation approach for EEG and MEG signal classification,
SIViP(13), No. 3, April 2019, pp. 483-490.
WWW Link. 1904
BibRef

Al-dabag, M.L.[Mohand Lokman], Ozkurt, N.[Nalan],
EEG motor movement classification based on cross-correlation with effective channel,
SIViP(13), No. 3, April 2019, pp. 567-573.
WWW Link. 1904
BibRef

Pander, T.[Tomasz],
EEG signal improvement with cascaded filter based on OWA operator,
SIViP(13), No. 6, September 2019, pp. 1165-1171.
WWW Link. 1908
BibRef

Rodrigues, J.D.[Jardel Das_C.], Filho, P.P.R.[Pedro P. Rebouças], Peixoto, E.[Eugenio], Kumar, N.A.[N. Arun], de Albuquerque, V.H.C.[Victor Hugo C.],
Classification of EEG signals to detect alcoholism using machine learning techniques,
PRL(125), 2019, pp. 140-149.
Elsevier DOI 1909
Electroencephalogram (EEG), Alcoholic signals, Wavelet packet decomposition, Support Vector Machine (SVM), Multi-layer Perceptron (MLP) BibRef

Fraschini, M.[Matteo], Pani, S.M.[Sara Maria], Didaci, L.[Luca], Marcialis, G.L.[Gian Luca],
Robustness of functional connectivity metrics for EEG-based personal identification over task-induced intra-class and inter-class variations,
PRL(125), 2019, pp. 49-54.
Elsevier DOI 1909
BibRef

Thilagaraj, M., Rajasekaran, M.P.[M. Pallikonda],
An empirical mode decomposition (EMD)-based scheme for alcoholism identification,
PRL(125), 2019, pp. 133-139.
Elsevier DOI 1909
ElectroEncephaloGram, k-Nearest-neighbour, Independent component analysis, Empirical mode decomposition BibRef

Bhalerao, S.[Siddharth], Ansari, I.A.[Irshad Ahmad], Kumar, A.[Anil], Jain, D.K.[Deepak Kumar],
A reversible and multipurpose ECG data hiding technique for telemedicine applications,
PRL(125), 2019, pp. 463-473.
Elsevier DOI 1909
Medical data hiding, Deep neural network, Reversible data hiding, Ownership detection, Tamper detection BibRef

Sawata, R., Ogawa, T., Haseyama, M.,
Novel Audio Feature Projection Using KDLPCCA-Based Correlation with EEG Features for Favorite Music Classification,
AffCom(10), No. 3, July 2019, pp. 430-444.
IEEE DOI 1909
Electroencephalography, Music, Correlation, Feature extraction, Multiple signal classification, Support vector machines, support vector machine (SVM) BibRef

Yang, W.[Wei], Yang, D.P.[Da-Peng], Liu, Y.[Yu], Liu, H.[Hong],
Decoding Simultaneous Multi-DOF Wrist Movements From Raw EMG Signals Using a Convolutional Neural Network,
HMS(49), No. 5, October 2019, pp. 411-420.
IEEE DOI 1909
Electromyography, Wrist, Training, Decoding, Feature extraction, Force, Convolutional neural network (CNN), simultaneous control BibRef

Samadzadehaghdam, N.[Nasser], Makkiabadi, B.[Bahador], Masjoodi, S.[Sadegh], Mohammadi, M.[Mohammad], Mohagheghian, F.[Fahimeh],
A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources,
IJIST(29), No. 4, 2019, pp. 686-700.
DOI Link 1911
EEG forward problem, EEG inverse problem, L1-norm regularization, source reconstruction BibRef

Besedová, P.[Petra], Vyšata, O.[Oldrich], Mazurová, R.[Radka], Kopal, J.[Jakub], Ondráková, J.[Jana], Vališ, M.[Martin], Procházka, A.[Aleš],
Classification of brain activities during language and music perception,
SIViP(13), No. 8, November 2019, pp. 1559-1567.
Springer DOI 1911
BibRef

Monteiro, T.G., Skourup, C., Zhang, H.,
Using EEG for Mental Fatigue Assessment: A Comprehensive Look Into the Current State of the Art,
HMS(49), No. 6, December 2019, pp. 599-610.
IEEE DOI 1912
Electroencephalography, Feature extraction, Human factors, Sensor fusion, Fatigue, Risk management, sensor fusion BibRef

Jiang, J., Fares, A., Zhong, S.,
A Context-Supported Deep Learning Framework for Multimodal Brain Imaging Classification,
HMS(49), No. 6, December 2019, pp. 611-622.
IEEE DOI 1912
Electroencephalography, Brain, Deep learning, Feature extraction, Functional magnetic resonance imaging, Image classification, object classification BibRef

Munia, T.T.K., Aviyente, S.,
Comparison of Wavelet and RID-Rihaczek Based Methods for Phase-Amplitude Coupling,
SPLetters(26), No. 12, December 2019, pp. 1897-1901.
IEEE DOI 2001
band-pass filters, electroencephalography, Hilbert transforms, interference (signal), medical signal processing, EEG BibRef

Jatoi, M.A.[Munsif Ali], Kamel, N.[Nidal], López, J.D.[José D.],
Multiple sparse priors technique with optimized patches for brain source localization,
IJIST(30), No. 1, 2020, pp. 154-167.
DOI Link 2002
Bayesian framework, EEG, free energy, MSP, source localization BibRef

Zheng, X.[Xiao], Chen, W.Z.[Wan-Zhong], You, Y.[Yang], Jiang, Y.[Yun], Li, M.Y.[Ming-Yang], Zhang, T.[Tao],
Ensemble deep learning for automated visual classification using EEG signals,
PR(102), 2020, pp. 107147.
Elsevier DOI 2003
Ensemble deep learning, Bagging algorithm, EEG, Automated visual classification BibRef

Cai, C., Diwakar, M., Chen, D., Sekihara, K., Nagarajan, S.S.,
Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging,
MedImg(39), No. 3, March 2020, pp. 567-577.
IEEE DOI 2004
Bayes methods, Kernel, Brain modeling, Image reconstruction, Electroencephalography, Imaging, Electromagnetic brain mapping, electroencephalography BibRef

Cai, C.[Chang], Long, Y.S.[Yuan-Shun], Ghosh, S.[Sanjay], Hashemi, A.[Ali], Gao, Y.J.[Yi-Jing], Diwakar, M.[Mithun], Haufe, S.[Stefan], Sekihara, K.[Kensuke], Wu, W.[Wei], Nagarajan, S.S.[Srikantan S.],
Bayesian Adaptive Beamformer for Robust Electromagnetic Brain Imaging of Correlated Sources in High Spatial Resolution,
MedImg(42), No. 9, September 2023, pp. 2502-2512.
IEEE DOI 2310
BibRef

Maksymenko, K., Clerc, M., Papadopoulo, T.,
Fast Approximation of EEG Forward Problem and Application to Tissue Conductivity Estimation,
MedImg(39), No. 4, April 2020, pp. 888-897.
IEEE DOI 2004
Conductivity, Lead, Electroencephalography, Head, Brain modeling, Finite element analysis, Electric potential, EEG forward problem, lead field matrix approximation BibRef

Hatamimajoumerd, E.[Elaheh], Talebpour, A.[Alireza], Mohsenzadeh, Y.[Yalda],
Enhancing multivariate pattern analysis for magnetoencephalography through relevant sensor selection,
IJIST(30), No. 2, 2020, pp. 473-494.
DOI Link 2005
dimensionality reduction, feature selection, MEG analysis, MEG sensor selection, multivariate pattern analysis, statistical dependency BibRef

Mahmoodian, N.[Naghmeh], Haddadnia, J.[Javad], Illanes, A.[Alfredo], Boese, A.[Axel], Friebe, M.[Michael],
Seizure prediction with cross-higher-order spectral analysis of EEG signals,
SIViP(14), No. 4, June 2020, pp. 821-828.
Springer DOI 2005
BibRef

Sharma, M.[Manish], Patel, S.[Sohamkumar], Acharya, U.R.[U. Rajendra],
Automated detection of abnormal EEG signals using localized wavelet filter banks,
PRL(133), 2020, pp. 188-194.
Elsevier DOI 2005
EEG, Energy localization, Support vector machine (SVM), Wavelet-based features BibRef

Wu, E.Q., Zhu, L., Zhang, W., Deng, P., Jia, B., Chen, S., Ren, H., Zhou, G.,
Novel Nonlinear Approach for Real-Time Fatigue EEG Data: An Infinitely Warped Model of Weighted Permutation Entropy,
ITS(21), No. 6, June 2020, pp. 2437-2448.
IEEE DOI 2006
Fatigue, Electroencephalography, Entropy, Brain modeling, Sleep, Feature extraction, Shape, Pilots' fatigue, Gaussian mixture model BibRef

Kohan, M.D.[Marzieh Daneshi], Nasrabadi, A.M.[Ali Motie], Shamsollahi, M.B.[Mohammad Bagher], Sharifi, A.[Ali],
EEG/PPG effective connectivity fusion for analyzing deception in interview,
SIViP(14), No. 5, July 2020, pp. 907-914.
Springer DOI 2006
BibRef

Chacon-Murguia, M.I.[Mario I.], Olivas-Padilla, B.E.[Brenda E.], Ramirez-Quintana, J.[Juan],
A new approach for multiclass motor imagery recognition using pattern image features generated from common spatial patterns,
SIViP(14), No. 5, July 2020, pp. 915-923.
Springer DOI 2006
BibRef

Jiménez-Guarneros, M.[Magdiel], Gómez-Gil, P.[Pilar],
Custom Domain Adaptation: A New Method for Cross-Subject, EEG-Based Cognitive Load Recognition,
SPLetters(27), 2020, pp. 750-754.
IEEE DOI 2006
Unsupervised domain adaptation, EEG-based classification, cognitive load, deep learning BibRef

Jiménez-Guarneros, M.[Magdiel], Gómez-Gil, P.[Pilar],
Standardization-refinement domain adaptation method for cross-subject EEG-based classification in imagined speech recognition,
PRL(141), 2021, pp. 54-60.
Elsevier DOI 2101
Unsupervised domain adaptation, EEG, Classification, Imagined speech, Deep learning BibRef

Radüntz, T., Meffert, B.,
Cross-Modality Matching for Evaluating User Experience of Emerging Mobile EEG Technology,
HMS(50), No. 4, August 2020, pp. 298-305.
IEEE DOI 2007
Electroencephalography, Mood, Headphones, Force, User experience, Usability, Electrodes, Dry sensors, electroencephalography (EEG), wearable devices BibRef

Rong, Y.Y.[Yu-Ying], Wu, X.J.[Xiao-Jun], Zhang, Y.[Yumei],
Classification of motor imagery electroencephalography signals using continuous small convolutional neural network,
IJIST(30), No. 3, 2020, pp. 653-659.
DOI Link 2008
BCI, CSCNN, EEG classification, motor imagery EEG signals BibRef

Ramdani, S.[Sofiane], Boyer, A.[Anthony], Caron, S.[Stéphane], Bonnetblanc, F.[François], Bouchara, F.[Frédéric], Duffau, H.[Hugues], Lesne, A.[Annick],
Parametric recurrence quantification analysis of autoregressive processes for pattern recognition in multichannel electroencephalographic data,
PR(109), 2021, pp. 107572.
Elsevier DOI 2009
Recurrence plots, Recurrence quantification analysis, Autoregressive stochastic processes, EEG Data BibRef

Mohagheghian, F.[Fahimeh], Deevband, M.R.[Mohammad Reza], Samadzadehaghdam, N.[Nasser], Khajehpour, H.[Hassan], Makkiabadi, B.[Bahador],
An enhanced weighted greedy analysis pursuit algorithm with application to EEG signal reconstruction,
IJIST(30), No. 4, 2020, pp. 1243-1255.
DOI Link 2011
compressed sensing, cosparse analysis model, EEG signal reconstruction, Greedy Analysis Pursuit, sparsity BibRef

Li, R.[Ren], Johansen, J.S.[Jared S.], Ahmed, H.[Hamad], Ilyevsky, T.V.[Thomas V.], Wilbur, R.B.[Ronnie B.], Bharadwaj, H.M.[Hari M.], Siskind, J.M.[Jeffrey Mark],
The Perils and Pitfalls of Block Design for EEG Classification Experiments,
PAMI(43), No. 1, January 2021, pp. 316-333.
IEEE DOI 2012
Electroencephalography, Visualization, Training, Correlation, Neuroimaging, Task analysis, Manifolds, Object classification, EEG, neuroimaging BibRef

Liu, F., Wang, L., Lou, Y., Li, R.C., Purdon, P.L.,
Probabilistic Structure Learning for EEG/MEG Source Imaging With Hierarchical Graph Priors,
MedImg(40), No. 1, January 2021, pp. 321-334.
IEEE DOI 2012
Brain modeling, Probabilistic logic, Imaging, Inverse problems, Electromagnetics, Uncertainty, EEG/MEG source imaging, graph structure learning BibRef

Qi, F., Wu, W., Yu, Z.L., Gu, Z., Wen, Z., Yu, T., Li, Y.,
Spatiotemporal-Filtering-Based Channel Selection for Single-Trial EEG Classification,
Cyber(51), No. 2, February 2021, pp. 558-567.
IEEE DOI 2101
Spatiotemporal phenomena, Electroencephalography, Optimization, Covariance matrices, Cybernetics, Finance, Feature extraction, spatiotemporal filtering BibRef

Fernandes, C.M., Migotina, D., Rosa, A.C.,
Brain's Night Symphony (BraiNSy): A Methodology for EEG Sonification,
AffCom(12), No. 1, January 2021, pp. 103-112.
IEEE DOI 2103
Electroencephalography, Sleep, Multiple signal classification, Sonification, Music, Feature extraction, Frequency modulation, signal processing BibRef

Jao, P.K.[Ping-Keng], Chavarriaga, R.[Ricardo], Dell'Agnola, F.[Fabio], Arza, A.[Adriana], Atienza, D.[David], del R. Millán, J.[José],
EEG Correlates of Difficulty Levels in Dynamical Transitions of Simulated Flying and Mapping Tasks,
HMS(51), No. 2, April 2021, pp. 99-108.
IEEE DOI 2103
Task analysis, Electroencephalography, Protocols, Drones, Games, Electrooculography, Compounds, Cognitive, difficulty, workload BibRef

Zheng, Q., Wang, Y., Heng, P.A.,
Multitask Feature Learning Meets Robust Tensor Decomposition for EEG Classification,
Cyber(51), No. 4, April 2021, pp. 2242-2252.
IEEE DOI 2103
Task analysis, Electroencephalography, Feature extraction, Brain modeling, Optimization, Training, tensor classification BibRef

Jiang, J.M.[Jian-Min], Fares, A.[Ahmed], Zhong, S.H.[Sheng-Hua],
A Brain-Media Deep Framework Towards Seeing Imaginations Inside Brains,
MultMed(23), 2021, pp. 1454-1465.
IEEE DOI 2105
Electroencephalography, Visualization, Brain, Deep learning, Feature extraction, Decoding, EEG, image generation, variant LSTM BibRef

Meng, X.J.[Xian-Jia], Qiu, S.[Shi], Wan, S.H.[Shao-Hua], Cheng, K.[Keyang], Cui, L.[Lei],
A motor imagery EEG signal classification algorithm based on recurrence plot convolution neural network,
PRL(146), 2021, pp. 134-141.
Elsevier DOI 2105
EEG signal, Recurrence plot, Convolution neural network, Classification, Motor imagery BibRef

Huang, C.B.[Cheng-Bin], Chen, W.T.[Wei-Ting], Chen, M.S.[Ming-Song], Yuan, B.H.[Bin-Hang],
A Feature Fusion Framework and Its Application to Automatic Seizure Detection,
SPLetters(28), 2021, pp. 753-757.
IEEE DOI 2105
Feature extraction, Medical services, Transforms, Training, Optimization, Indexes, Deep learning, Feature fusion, seizure detection BibRef

Soundirarajan, M.[Mirra], Babini, M.H.[Mohammad Hossein], Sim, S.[Sue], Nathan, V.[Visvamba], Subasi, A.[Abdulhamit], Namazi, H.[Hamidreza],
Analysis of brain-facial muscle connection in the static fractal visual stimulation,
IJIST(31), No. 2, 2021, pp. 548-554.
DOI Link 2105
brain, complexity, EEG signals, EMG signals, facial muscles, fractal analysis, static visual stimuli BibRef

Geirnaert, S.[Simon], Vandecappelle, S.[Servaas], Alickovic, E.[Emina], de Cheveigne, A.[Alain], Lalor, E.[Edmund], Meyer, B.T.[Bernd T.], Miran, S.[Sina], Francart, T.[Tom], Bertrand, A.[Alexander],
Electroencephalography-Based Auditory Attention Decoding: Toward Neurosteered Hearing Devices,
SPMag(38), No. 4, July 2021, pp. 89-102.
IEEE DOI 2107
Signal processing algorithms, Auditory system, biomedical signal processing, Electroencephalography, Decoding, Noise measurement BibRef

Huang, S.L.[Shou-Lin], Cai, G.Q.[Guo-Qing], Wang, T.[Tong], Ma, T.[Ting],
Amplitude-Phase Information Measurement on Riemannian Manifold for Motor Imagery-Based BCI,
SPLetters(28), 2021, pp. 1310-1314.
IEEE DOI 2107
Electroencephalography, Covariance matrices, Manifolds, Training, Phase measurement, Signal processing algorithms, Synchronization, common spatial pattern (CSP) BibRef

Lan, Z.[Zhen], Yan, C.[Chao], Li, Z.X.[Zi-Xing], Tang, D.Q.[Deng-Qing], Xiang, X.J.[Xiao-Jia],
MACRO: Multi-Attention Convolutional Recurrent Model for Subject-Independent ERP Detection,
SPLetters(28), 2021, pp. 1505-1509.
IEEE DOI 2108
Electroencephalography, Integrated circuits, Feature extraction, Convolution, Kernel, Brain modeling, Training, EEG, ERP detection, deep learning BibRef

Thomas, K.P.[Kavitha P.], Robinson, N.[Neethu], Prasad, V.A.[Vinod A.],
Separability of Motor Imagery Directions Using Subject-Specific Discriminative EEG Features,
HMS(51), No. 5, October 2021, pp. 544-553.
IEEE DOI 2109
Electroencephalography, Feature extraction, Kinematics, Task analysis, Man-machine systems, Data mining, Trajectory, phase locking values (PLVs) BibRef

Behrouzi, T.[Tina], Hatzinakos, D.[Dimitrios],
Graph variational auto-encoder for deriving EEG-based graph embedding,
PR(121), 2022, pp. 108202.
Elsevier DOI 2109
Biometrics, Functional connectivity, Electroencephalogram (EEG), Graph deep learning BibRef

Palazzo, S.[Simone], Spampinato, C.[Concetto], Kavasidis, I.[Isaak], Giordano, D.[Daniela], Schmidt, J.[Joseph], Shah, M.[Mubarak],
Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features,
PAMI(43), No. 11, November 2021, pp. 3833-3849.
IEEE DOI 2110
BibRef
And:
Rebuttal to Comments,
PAMI(46), No. 12, December 2024, pp. 11540-11542.
IEEE DOI 2411
Visualization, Brain modeling, Electroencephalography, Computational modeling, Neural activity, Machine learning, unsupervised learning
See also Confounds in the Data: Comments on Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features. Comments:
See also Still an Ineffective Method With Supertrials/ERPs: Comments on Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features. BibRef

Gao, Z.K.[Zhong-Ke], Dang, W.D.[Wei-Dong], Liu, M.X.[Ming-Xu], Guo, W.[Wei], Ma, K.[Kai], Chen, G.R.[Guan-Rong],
Classification of EEG Signals on VEP-Based BCI Systems With Broad Learning,
SMCS(51), No. 11, November 2021, pp. 7143-7151.
IEEE DOI 2110
Electroencephalography, Visualization, Feature extraction, Steady-state, Time series analysis, Bars, Complex networks, steady-state VEP (SSVEP) BibRef

Bore, J.C.[Joyce Chelangat], Li, P.Y.[Pei-Yang], Jiang, L.[Lin], Ayedh, W.M.A.[Walid M. A.], Chen, C.L.[Chun-Li], Harmah, D.J.[Dennis Joe], Yao, D.Z.[De-Zhong], Cao, Z.[Zehong], Xu, P.[Peng],
A Long Short-Term Memory Network for Sparse Spatiotemporal EEG Source Imaging,
MedImg(40), No. 12, December 2021, pp. 3787-3800.
IEEE DOI 2112
Electroencephalography, Imaging, Scalp, Lead, Deep learning, Spatiotemporal phenomena, Inverse problems, spatiotemporal EEG sources BibRef

Brusini, L.[Lorenza], Stival, F.[Francesca], Setti, F.[Francesco], Menegatti, E.[Emanuele], Menegaz, G.[Gloria], Storti, S.F.[Silvia Francesca],
A Systematic Review on Motor-Imagery Brain-Connectivity-Based Computer Interfaces,
HMS(51), No. 6, December 2021, pp. 725-733.
IEEE DOI 2112
Electroencephalography, Feature extraction, Brain modeling, Deep learning, Machine learning, Real-time systems, motor imagery (MI) BibRef

Türk, Ö.[Ömer],
Classification of electroencephalogram records related to cursor movements with a hybrid method based on deep learning,
IJIST(31), No. 4, 2021, pp. 2322-2333.
DOI Link 2112
classification, CNN, cursor movement, k-NN, raw EEG, SVM BibRef

Reddy, T.K.[Tharun Kumar], Arora, V.[Vipul], Gupta, V.[Vinay], Biswas, R.[Rupam], Behera, L.[Laxmidhar],
EEG-Based Drowsiness Detection With Fuzzy Independent Phase-Locking Value Representations Using Lagrangian-Based Deep Neural Networks,
SMCS(52), No. 1, January 2022, pp. 101-111.
IEEE DOI 2112
Electroencephalography, Artificial neural networks, Optimization, Feature extraction, Signal processing algorithms, reaction time (RT) BibRef

Wu, E.Q.[Edmond Q.], Xiong, P.W.[Peng-Wen], Tang, Z.R.[Zhi-Ri], Li, G.J.[Gui-Jiang], Song, A.[Aiguo], Zhu, L.M.[Li-Min],
Detecting Dynamic Behavior of Brain Fatigue Through 3-D-CNN-LSTM,
SMCS(52), No. 1, January 2022, pp. 90-100.
IEEE DOI 2112
Fatigue, Electroencephalography, Rhythm, Brain modeling, Deep learning, Feature extraction, Time-frequency analysis, RNN BibRef

Kumari, N.[Nandini], Anwar, S.[Shamama], Bhattacharjee, V.[Vandana],
Automated visual stimuli evoked multi-channel EEG signal classification using EEGCapsNet,
PRL(153), 2022, pp. 29-35.
Elsevier DOI 2201
Visual stimuli, Electroencephalogram signal, Deep learning, Capsule Network, Dynamic routing, Primary capsule, EEGCapsNet BibRef

Dai, C.L.[Cheng-Long], Wu, J.[Jia], Pi, D.C.[De-Chang], Becker, S.I.[Stefanie I.], Cui, L.[Lin], Zhang, Q.[Qin], Johnson, B.[Blake],
Brain EEG Time-Series Clustering Using Maximum-Weight Clique,
Cyber(52), No. 1, January 2022, pp. 357-371.
IEEE DOI 2201
Electroencephalography, Time series analysis, Electrodes, Feature extraction, Correlation, Clustering algorithms, Australia, weighted EEG graph BibRef

Bao, T.Z.[Tian-Zhe], Zaidi, S.A.R.[Syed Ali Raza], Xie, S.Q.[Sheng Quan], Yang, P.F.[Peng-Fei], Zhang, Z.Q.[Zhi-Qiang],
CNN Confidence Estimation for Rejection-Based Hand Gesture Classification in Myoelectric Control,
HMS(52), No. 1, February 2022, pp. 99-109.
IEEE DOI 2201
Convolutional neural networks, Estimation, Neural networks, Entropy, Linear programming, Task analysis, Optimization, surface electromyography (sEMG) BibRef

Li, P.W.[Pei-Wen], Cai, S.Q.[Si-Qi], Su, E.[Enze], Xie, L.H.[Long-Han],
A Biologically Inspired Attention Network for EEG-Based Auditory Attention Detection,
SPLetters(29), 2022, pp. 284-288.
IEEE DOI 2202
Electroencephalography, Decoding, Brain modeling, Frequency modulation, Speech processing, Training, electroencephalography BibRef

Cai, S.Q.[Si-Qi], Li, P.W.[Pei-Wen], Su, E.[Enze], Liu, Q.[Qi], Xie, L.H.[Long-Han],
A Neural-Inspired Architecture for EEG-Based Auditory Attention Detection,
HMS(52), No. 4, August 2022, pp. 668-676.
IEEE DOI 2208
Electroencephalography, Brain modeling, Neurons, Feature extraction, Training, Computational modeling, Decoding, neural-inspired architecture BibRef

Cai, S.Q.[Si-Qi], Su, E.[Enze], Xie, L.H.[Long-Han], Li, H.Z.[Hai-Zhou],
EEG-Based Auditory Attention Detection via Frequency and Channel Neural Attention,
HMS(52), No. 2, April 2022, pp. 256-266.
IEEE DOI 2203
Electroencephalography, Brain modeling, Frequency modulation, Convolution, Decoding, Correlation, Representation learning, frequency attention BibRef

Ye, J.A.[Jian-An], Xiao, Q.F.[Qin-Feng], Wang, J.[Jing], Zhang, H.J.[Hong-Jun], Deng, J.X.[Jiao-Xue], Lin, Y.F.[You-Fang],
CoSleep: A Multi-View Representation Learning Framework for Self-Supervised Learning of Sleep Stage Classification,
SPLetters(29), 2022, pp. 189-193.
IEEE DOI 2202
Sleep, Task analysis, Physiology, Electroencephalography, Training, Annotations, Time-frequency analysis, Self-supervised learning, representation learning BibRef

Li, J.[Jie], Wang, Z.L.[Zhe-Long], Qiu, S.[Sen], Zhao, H.Y.[Hong-Yu], Wang, J.X.[Jia-Xin], Shi, X.[Xin], Liu, L.[Long], Yang, N.[Ning],
Study on Horse-Rider Interaction Based on Body Sensor Network in Competitive Equitation,
AffCom(13), No. 1, January 2022, pp. 553-567.
IEEE DOI 2203
Magnetometers, Electroencephalography, Gyroscopes, Legged locomotion, Accelerometers, Training, Hardware, extend kalman filter BibRef

Chen, X.[Xun], Li, C.[Chang], Liu, A.[Aiping], McKeown, M.J.[Martin J.], Qian, R.[Ruobing], Wang, Z.J.[Z. Jane],
Toward Open-World Electroencephalogram Decoding Via Deep Learning: A comprehensive survey,
SPMag(39), No. 2, March 2022, pp. 117-134.
IEEE DOI 2203
Survey, EEG. Deep learning, Brain models, Systematics, Semantics, Tutorials,
WWW Link. ction, Electroencephalography, Decoding BibRef

Yi, C.Z.[Chun-Zhi], Jiang, F.[Feng], Zhang, S.P.[Sheng-Ping], Guo, H.[Hao], Yang, C.[Chifu], Ding, Z.[Zhen], Wei, B.[Baichun], Lan, X.Y.[Xiang-Yuan], Zhou, H.Y.[Hui-Yu],
Continuous Prediction of Lower-Limb Kinematics From Multi-Modal Biomedical Signals,
CirSysVideo(32), No. 5, May 2022, pp. 2592-2602.
IEEE DOI 2205
Kinematics, Electromyography, Feature extraction, Delays, Prediction algorithms, Exoskeletons, Predictive models, long short-term memory BibRef

Bagchi, S.[Subhranil], Bathula, D.R.[Deepti R.],
EEG-ConvTransformer for single-trial EEG-based visual stimulus classification,
PR(129), 2022, pp. 108757.
Elsevier DOI 2206
EEG, Visual stimulus classification, Deep learning, Transformer, Multi-head attention, Inter-region similarity, Head representations BibRef

Liu, J.B.[Jin-Biao], Tan, G.S.[Gan-Sheng], Wang, J.X.[Ji-Xian], Wei, Y.[Yina], Sheng, Y.X.[Yi-Xuan], Chang, H.[Hui], Xie, Q.[Qing], Liu, H.H.[Hong-Hai],
Closed-Loop Construction and Analysis of Cortico-Muscular-Cortical Functional Network After Stroke,
MedImg(41), No. 6, June 2022, pp. 1575-1586.
IEEE DOI 2206
Muscles, Brain modeling, Frequency-domain analysis, Electroencephalography, Couplings, Electromyography, Hospitals, repetitive transcranial magnetic stimulation BibRef

Jindal, K.[Komal], Upadhyay, R.[Rahul], Singh, H.S.[Hari Shankar],
A novel EEG channel selection and classification methodology for multi-class motor imagery-based BCI system design,
IJIST(32), No. 4, 2022, pp. 1318-1337.
DOI Link 2207
brain computer interface, channel selection, electroencephalogram, machine learning, motor imagery, wavelet transform BibRef

Dovedi, T.[Tanvi], Upadhyay, R.[Rahul], Kumar, V.[Vinay],
Hybrid time-reassigned multisynchrosqueezing transform-Picard-based automated electroencephalography artifact correction methodology for brain-computer interface applications,
IJIST(32), No. 4, 2022, pp. 1338-1356.
DOI Link 2207
artifact removal, electroencephalogram, independent component analysis, sparse entropy, time reassigned multisynchrosqueezing transform BibRef

Samanta, K.[Kaniska], Chatterjee, S.[Soumya], Bose, R.[Rohit],
Neuromuscular disease detection based on feature extraction from time-frequency images of EMG signals employing robust hyperbolic Stockwell transform,
IJIST(32), No. 4, 2022, pp. 1251-1262.
DOI Link 2207
classification, EMG signals, feature selection and genetic algorithm, Stockwell transform BibRef

Chen, P.Y.[Pei-Yin], Gao, Z.K.[Zhong-Ke], Yin, M.M.[Miao-Miao], Wu, J.L.[Jia-Ling], Ma, K.[Kai], Grebogi, C.[Celso],
Multiattention Adaptation Network for Motor Imagery Recognition,
SMCS(52), No. 8, August 2022, pp. 5127-5139.
IEEE DOI 2208
Electroencephalography, Feature extraction, Training, Task analysis, Transfer learning, Signal resolution, Deep learning, transfer learning BibRef

Kotas, M.P.[Marian P.], Piela, M.[Michal], Contreras-Ortiz, S.H.[Sonia H.],
Modified Spatio-Temporal Matched Filtering for Brain Responses Classification,
HMS(52), No. 4, August 2022, pp. 677-686.
IEEE DOI 2208
Ash, Visualization, Electroencephalography, Man-machine systems, Estimation, Testing, Linear programming, visual evoked potentials (EPs) BibRef

Choo, S.[Sanghyun], Nam, C.S.[Chang S.],
Detecting Human Trust Calibration in Automation: A Convolutional Neural Network Approach,
HMS(52), No. 4, August 2022, pp. 774-783.
IEEE DOI 2208
Automation, Electroencephalography, Calibration, Feature extraction, Convolutional neural networks, trust estimation BibRef

Li, Y.[Yang], Liu, Y.[Yu], Guo, Y.Z.[Yu-Zhu], Liao, X.F.[Xiao-Feng], Hu, B.[Bin], Yu, T.[Tao],
Spatio-Temporal-Spectral Hierarchical Graph Convolutional Network with Semisupervised Active Learning for Patient-Specific Seizure Prediction,
Cyber(52), No. 11, November 2022, pp. 12189-12204.
IEEE DOI 2211
Electroencephalography, Spatiotemporal phenomena, Integrated circuits, Convolution, Scalp, Training, Logic gates, spatio-temporal-spectral dependencies BibRef

Chang, S.[Shuohua], Yang, Z.H.[Zhi-Hong], You, Y.Y.[Yu-Yang], Guo, X.Y.[Xiao-Yu],
DSSNet: A Deep Sequential Sleep Network for Self-Supervised Representation Learning Based on Single-Channel EEG,
SPLetters(29), 2022, pp. 2143-2147.
IEEE DOI 2211
Sleep, Electroencephalography, Brain modeling, Task analysis, Convolution, Self-supervised learning, Representation learning, sleep staging BibRef

Hu, S.[Shuzhan], Duan, Y.P.[Yi-Ping], Tao, X.M.[Xiao-Ming], Li, G.Y.[Geoffrey Ye], Lu, J.H.[Jian-Hua],
Human Perception Measurement by Electroencephalography for Facial Image Compression,
SPLetters(29), 2022, pp. 2148-2152.
IEEE DOI 2211
Electroencephalography, Distortion, Image coding, Distortion measurement, Resource management, Videoconferences, data rate allocation BibRef

Ahmed, H.[Hamad], Wilbur, R.B.[Ronnie B.], Bharadwaj, H.M.[Hari M.], Siskind, J.M.[Jeffrey Mark],
Confounds in the Data: Comments on 'Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features',
PAMI(44), No. 12, December 2022, pp. 9217-9220.
IEEE DOI 2212
Electroencephalography, Support vector machines, Training, Neuroscience, Codes, Auditory system, Welding, Object classification, brain-computer interface
See also Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features. BibRef

Yi, C.[Chanlin], Yao, R.W.[Ru-Wei], Song, L.[Liuyi], Jiang, L.[Lin], Si, Y.J.[Ya-Jing], Li, P.Y.[Pei-Yang], Li, F.[Fali], Yao, D.Z.[De-Zhong], Zhang, Y.[Yu], Xu, P.[Peng],
A Novel Method for Constructing EEG Large-Scale Cortical Dynamical Functional Network Connectivity (dFNC): WTCS,
Cyber(52), No. 12, December 2022, pp. 12869-12881.
IEEE DOI 2212
Electroencephalography, Brain, Correlation, Couplings, Spatial resolution, Coherence, Cognition, wavelet coherence-S estimator (WTCS) BibRef

Cho, J.H.[Jeong-Hyun], Jeong, J.H.[Ji-Hoon], Lee, S.W.[Seong-Whan],
NeuroGrasp: Real-Time EEG Classification of High-Level Motor Imagery Tasks Using a Dual-Stage Deep Learning Framework,
Cyber(52), No. 12, December 2022, pp. 13279-13292.
IEEE DOI 2212
Electroencephalography, Brain-computer interfaces, Deep learning, Brain-computer interface (BCI), deep learning, real-time classification BibRef

Gohel, B.[Bakul], Khare, M.[Mahish],
EEG/MEG source imaging in the absence of subject's brain MRI scan: Perspective on co-registration and MRI selection approach,
IJIST(33), No. 1, 2023, pp. 287-298.
DOI Link 2301
EEG, forward model, ICP, inverse modelling, MEG, MRI co-registration BibRef

Günel, S.[Semih], Aymanns, F.[Florian], Honari, S.[Sina], Ramdya, P.[Pavan], Fua, P.[Pascal],
Overcoming the Domain Gap in Neural Action Representations,
IJCV(131), No. 3, March 2023, pp. 813-833.
Springer DOI 2302
Electrocorticography. Relating behavior to brain activity. BibRef

Liu, Y.X.[Ying-Xin], Yu, Y.[Yang], Ye, Z.[Zeqi], Li, M.[Ming], Zhang, Y.F.[Yi-Fan], Zhou, Z.T.[Zong-Tan], Hu, D.[Dewen], Zeng, L.L.[Ling-Li],
Fusion of Spatial, Temporal, and Spectral EEG Signatures Improves Multilevel Cognitive Load Prediction,
HMS(53), No. 2, April 2023, pp. 357-366.
IEEE DOI 2303
Task analysis, Cognitive load, Electroencephalography, Games, Man-machine systems, Indexes, Feature extraction, Classification, power spectral density (PSD) BibRef

Lerogeron, H.[Hugo], Picot-Clémente, R.[Romain], Rakotomamonjy, A.[Alain], Heutte, L.[Laurent],
Approximating dynamic time warping with a convolutional neural network on EEG data,
PRL(171), 2023, pp. 162-169.
Elsevier DOI 2306
DTW, EEG, Similarity learning BibRef

Tang, Y.[Yunbo], Chen, D.[Dan], Liu, H.H.[Hong-Hai], Cai, C.[Chang], Li, X.L.[Xiao-Li],
Deep EEG Superresolution via Correlating Brain Structural and Functional Connectivities,
Cyber(53), No. 7, July 2023, pp. 4410-4422.
IEEE DOI 2307
Electroencephalography, Convolution, Electrodes, Image reconstruction, Brain modeling, Interpolation, Scalp, spatial localization BibRef

Pancholi, S.[Sidharth], Giri, A.[Amita], Jain, A.[Anant], Kumar, L.[Lalan], Roy, S.[Sitikantha],
Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal,
Cyber(53), No. 7, July 2023, pp. 4094-4106.
IEEE DOI 2307
Electroencephalography, Brain modeling, Kinematics, Trajectory, Deep learning, Convolutional neural networks, Location awareness, source localization BibRef

Karimi, S.[Sajjad], Shamsollahi, M.B.[Mohammad Bagher],
Tractable Maximum Likelihood Estimation for Latent Structure Influence Models With Applications to EEG and ECoG Processing,
PAMI(45), No. 8, August 2023, pp. 10466-10477.
IEEE DOI 2307
Hidden Markov models, Brain modeling, Inference algorithms, Approximation algorithms, Convergence, Electroencephalography, learning problem BibRef

Du, J.Z.[Jun-Zhen], Tai, Y.H.[Yong-Hang], Li, F.[Fei], Chen, Z.Q.[Zai-Qing], Ren, X.Q.[Xu-Qing], Li, C.L.[Chang-Le],
Using Beta Rhythm From EEG to Assess Physicians' Operative Skills in Virtual Surgical Training,
HMS(53), No. 4, August 2023, pp. 688-696.
IEEE DOI 2308
Electroencephalography, Training, Rhythm, Medical services, Indexes, Electrodes, Task analysis, Electroencephalograph (EEG), virtual reality BibRef

Bharadwaj, H.M.[Hari M.], Wilbur, R.B.[Ronnie B.], Siskind, J.M.[Jeffrey Mark],
Still an Ineffective Method With Supertrials/ERPs: Comments on 'Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features',
PAMI(45), No. 11, November 2023, pp. 14052-14054.
IEEE DOI 2310

See also Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features. BibRef

Huang, W.Q.[Wen-Qie], Yan, G.H.[Guang-Hui], Chang, W.W.[Wen-Wen], Zhang, Y.[Yuchan], Yuan, Y.T.[Yue-Ting],
EEG-based classification combining Bayesian convolutional neural networks with recurrence plot for motor movement/imagery,
PR(144), 2023, pp. 109838.
Elsevier DOI 2310
Electroencephalogram (EEG), Real execution, Motor imagery (MI), Deep learning (DL), Classification BibRef

Kim, B.H.[Byung Hyung], Choi, J.W.[Jin Woo], Lee, H.G.[Hong-Gu], Jo, S.[Sungho],
A discriminative SPD feature learning approach on Riemannian manifolds for EEG classification,
PR(143), 2023, pp. 109751.
Elsevier DOI 2310
Discriminative, EEG, Non-stationary, SPD Matrix, Riemannian, Barycenter BibRef

Wang, W.L.[Wen-Long], Qi, F.F.[Fei-Fei], Wipf, D.P.[David Paul], Cai, C.[Chang], Yu, T.Y.[Tian-You], Li, Y.Q.[Yuan-Qing], Zhang, Y.[Yu], Yu, Z.L.[Zhu-Liang], Wu, W.[Wei],
Sparse Bayesian Learning for End-to-End EEG Decoding,
PAMI(45), No. 12, December 2023, pp. 15632-15649.
IEEE DOI 2311
BibRef

Seraphim, M.[Mathieu], Dequidt, P.[Paul], Lechervy, A.[Alexis], Yger, F.[Florian], Brun, L.[Luc], Etard, O.[Olivier],
Temporal Sequences of EEG Covariance Matrices for Automated Sleep Stage Scoring with Attention Mechanisms,
CAIP23(II:67-76).
Springer DOI 2312
BibRef

Dolzhikova, I.[Irina], Abibullaev, B.[Berdakh], Zollanvari, A.[Amin],
A Jackknife-Inspired Deep Learning Approach to Subject-Independent Classification of EEG,
PRL(176), 2023, pp. 28-33.
Elsevier DOI 2312
Convolutional neural network, Delete-a-Subject-Jackknife, Deep learning, Subject-independent BibRef

Li, C.F.[Cheng-Fang], Fang, G.Y.[Gao-Yun], Liu, Y.[Yang], Liu, J.[Jing], Song, L.[Liang],
Decoding Silent Reading EEG Signals Using Adaptive Feature Graph Convolutional Network,
SPLetters(31), 2024, pp. 1-5.
IEEE DOI 2401
BibRef

Liu, Z.Y.[Ze-Yi], Xiao, F.Y.[Fu-Yuan], Lin, C.T.[Chin-Teng], Cao, Z.[Zehong],
A Robust Evidential Multisource Data Fusion Approach Based on Cooperative Game Theory and Its Application in EEG,
SMCS(54), No. 2, February 2024, pp. 729-740.
IEEE DOI 2402
Data integration, Evidence theory, Electroencephalography, Time complexity, Resource management, Measurement uncertainty, Shapley function BibRef

Hou, K.[Kechen], Zhang, X.W.[Xiao-Wei], Yang, Y.K.[Yi-Kun], Zhao, Q.Q.[Qi-Qi], Yuan, W.J.[Wen-Jie], Zhou, Z.Y.[Zhong-Yi], Zhang, S.[Sipo], Li, C.[Chen], Shen, J.[Jian], Hu, B.[Bin],
Emotion Recognition From Multimodal Physiological Signals via Discriminative Correlation Fusion With a Temporal Alignment Mechanism,
Cyber(54), No. 5, May 2024, pp. 3079-3092.
IEEE DOI 2405
Physiology, Correlation, Emotion recognition, Nervous system, Electroencephalography, Feature extraction, Brain modeling, temporal alignment BibRef

Shin, D.H.[Dong-Hee], Son, Y.H.[Young-Han], Kim, J.M.[Jun-Mo], Ahn, H.J.[Hee-Jun], Seo, J.H.[Jun-Ho], Ji, C.H.[Chang-Hoon], Han, J.W.[Ji-Wung], Lee, B.J.[Byung-Jun], Won, D.O.[Dong-Ok], Kam, T.E.[Tae-Eui],
MARS: Multiagent Reinforcement Learning for Spatial: Spectral and Temporal Feature Selection in EEG-Based BCI,
SMCS(54), No. 5, May 2024, pp. 3084-3096.
IEEE DOI 2405
Feature extraction, Electroencephalography, Task analysis, Robots, Reinforcement learning, Mars, Data mining, multiagent reinforcement learning (RL) BibRef

Sun, K.[Ke], Peng, C.[Ciyuan], Yu, S.[Shuo], Han, Z.Y.[Zhuo-Yang], Xia, F.[Feng],
From Electroencephalogram Data to Brain Networks: Graph-Learning-Based Brain Disease Diagnosis,
IEEE_Int_Sys(39), No. 2, March 2024, pp. 21-29.
IEEE DOI 2405
Electroencephalography, Brain modeling, Adaptation models, Convolution, Electrodes, Topology, Medical diagnosis, Classification algorithms BibRef

Zhang, Z.[Zirui], Wu, W.M.[Wei-Ming], Sun, C.[Chen], Wang, C.[Cong],
Seizure detection via deterministic learning feature extraction,
PR(153), 2024, pp. 110466.
Elsevier DOI 2405
Seizure detection, Feature extraction, Nonlinear system dynamics, Deterministic learning BibRef

Dang, W.D.[Wei-Dong], Lv, D.M.[Dong-Mei], Tang, M.X.[Meng-Xiao], Sun, X.L.[Xin-Lin], Liu, Y.[Yong], Grebogi, C.[Celso], Gao, Z.K.[Zhong-Ke],
Flashlight-Net: A Modular Convolutional Neural Network for Motor Imagery EEG Classification,
SMCS(54), No. 7, July 2024, pp. 4507-4516.
IEEE DOI 2406
Brain modeling, Feature extraction, Electroencephalography, Convolutional neural networks, Convolution, Transfer learning, transfer learning BibRef

Ji, Y.[Youshuo], Li, F.[Fu], Fu, B.[Boxun], Zhou, Y.J.[Yi-Jin], Wu, H.[Hao], Li, Y.[Yang], Li, X.L.[Xiao-Li], Shi, G.M.[Guang-Ming],
A novel hybrid decoding neural network for EEG signal representation,
PR(155), 2024, pp. 110726.
Elsevier DOI Code:
WWW Link. 2408
EEG representation, Hybrid decoding network, Depthwise separable convolution, Multi-head attention, Spatial-temporal information BibRef

Bhalerao, S.V.[Shailesh Vitthalrao], Pachori, R.B.[Ram Bilas],
Automated Classification of Cognitive Visual Objects Using Multivariate Swarm Sparse Decomposition From Multichannel EEG-MEG Signals,
HMS(54), No. 4, August 2024, pp. 455-464.
IEEE DOI 2408
Visualization, Electroencephalography, Vectors, Object recognition, Support vector machines, Optimization, Neural activity, visual object recognition BibRef

Fang, Z., Albera, L., Kachenoura, A., Shu, H., Kang, Y., Jeannès, R.L.B.[R. Le Bouquin],
High-Dimensional MVAR Model Identification Based on Structured Sparsity Penalization,
SPLetters(31), 2024, pp. 1975-1979.
IEEE DOI 2408
Brain modeling, Numerical models, Estimation, Linear programming, Electroencephalography, Convergence, Computational modeling, sparsity BibRef

Tabejamaat, M.[Mohsen], Mohammadzade, H.[Hoda], Negin, F.[Farhood], Bremond, F.[Francois],
EEG classification with limited data: A deep clustering approach,
PR(157), 2025, pp. 110934.
Elsevier DOI 2409
Deep neural network, Overfitting issue, Electroencephalography, Data scarcity BibRef

Singh, P.[Prajwal], Dalal, D.[Dwip], Vashishtha, G.[Gautam], Miyapuram, K.[Krishna], Raman, S.[Shanmuganathan],
Learning Robust Deep Visual Representations from EEG Brain Recordings,
WACV24(7538-7547)
IEEE DOI 2404
Representation learning, Visualization, Image synthesis, Transforms, Self-supervised learning, Feature extraction, Biomedical / healthcare / medicine BibRef

Takagi, Y.[Yu], Nishimoto, S.[Shinji],
High-resolution image reconstruction with latent diffusion models from human brain activity,
CVPR23(14453-14463)
IEEE DOI 2309
BibRef

Zhang, M.J.[Mei-Juan], Liu, J.[Jun], Liu, C.[Chuang], Wu, T.[Ting], Peng, X.P.[Xue-Ping],
An Efficient CADNet for Classification of High-frequency Oscillations in Magnetoencephalography,
ICRVC22(25-30)
IEEE DOI 2301
Training, Time-frequency analysis, Magnetoencephalography, Epilepsy, Hafnium oxide, Feature extraction, Dendrites (neurons), Dendrite Net BibRef

Delvigne, V.[Victor], Wannous, H.[Hazem], Vandeborre, J.P.[Jean-Philippe], Ris, L.[Laurence], Dutoit, T.[Thierry],
Spatio-Temporal Analysis of Transformer based Architecture for Attention Estimation from EEG,
ICPR22(1076-1082)
IEEE DOI 2212
Time-frequency analysis, Machine learning, Information representation, Brain modeling, Transformers, Feature extraction BibRef

Lotey, T.[Taveena], Keserwani, P.[Prateek], Wasnik, G.[Gaurav], Roy, P.P.[Partha Pratim],
Cross-Session Motor Imagery EEG Classification using Self-Supervised Contrastive Learning,
ICPR22(975-981)
IEEE DOI 2212
Training, Representation learning, Supervised learning, Neural networks, Self-supervised learning, Recording BibRef

Tamekue, C.[Cyprien], Prandi, D.[Dario], Chitour, Y.[Yacine],
Reproducing Sensory Induced Hallucinations via Neural Fields,
ICIP22(3326-3330)
IEEE DOI 2211
Visualization, Pattern formation, Numerical simulation, Presence network agents, Physiology, Mathematical models, neural field equation BibRef

Shah, D.[Darshil], Govind, M.[Meghna], Gopan, K.G.[K. Gopika], Sinha, N.[Neelam],
Deep Learning Based EEG Analysis Using Video Analytics,
ICIP22(536-540)
IEEE DOI 2211
Deep learning, Interpolation, Visual analytics, Electroencephalography, Arithmetic, EEG, Deep Learning, Classification BibRef

Avola, D.[Danilo], Cascio, M.[Marco], Cinque, L.[Luigi], Fagioli, A.[Alessio], Foresti, G.L.[Gian Luca], Marini, M.R.[Marco Raoul], Pannone, D.[Daniele],
Analyzing EEG Data with Machine and Deep Learning: A Benchmark,
CIAP22(I:335-345).
Springer DOI 2205
BibRef

Ahmed, H.[Hamad], Wilbur, R.B.[Ronnie B.], Bharadwaj, H.M.[Hari M.], Siskind, J.M.[Jeffrey Mark],
Object classification from randomized EEG trials,
CVPR21(3844-3853)
IEEE DOI 2111
Support vector machines, Training, Recurrent neural networks, Training data, Electroencephalography, Convolutional neural networks BibRef

Lemkhenter, A.[Abdelhak], Favaro, P.[Paolo],
Boosting Generalization in Bio-signal Classification by Learning the Phase-Amplitude Coupling,
GCPR20(72-85).
Springer DOI 2110
BibRef

Solórzano-Espíndola, C.E.[Carlos Emiliano], Sossa, H.[Humberto], Zamora, E.[Erik],
A Comparison Study of EEG Signals Classifiers for Inter-subject Generalization,
MCPR21(305-315).
Springer DOI 2108
BibRef

Kim, B.H.[Byung Hyung], Suh, Y.J.[Yoon-Je], Lee, H.[Honggu], Jo, S.H.[Sung-Ho],
Nonlinear Ranking Loss on Riemannian Potato Embedding,
ICPR21(4348-4355)
IEEE DOI 2105
Manifolds, Training, Learning systems, Loss measurement, Electroencephalography, Sensors, Data mining BibRef

Chakladar, D.D.[Debashis Das], Dey, S.[Shubhashis], Roy, P.P.[Partha Pratim], Iwamura, M.[Masakazu],
EEG-Based Cognitive State Assessment Using Deep Ensemble Model and Filter Bank Common Spatial Pattern,
ICPR21(4107-4114)
IEEE DOI 2105
Solid modeling, Computational modeling, Filter banks, Predictive models, Brain modeling, Feature extraction, Electroencephalography BibRef

Bria, A.[Alessandro], Marrocco, C.[Claudio], Tortorella, F.[Francesco],
Sinc-based Convolutional Neural Networks for EEG-BCI-based Motor Imagery Classification,
AIHA20(526-535).
Springer DOI 2103
BibRef

Awrangjeb, M., Rodrigues, J.d.C., Stantic, B., Estivill-Castro, V.,
A fair comparison of the EEG signal classification methods for alcoholic subject identification,
IVCNZ20(1-6)
IEEE DOI 2012
Training, Pattern classification, Feature extraction, Electroencephalography, Wavelet packets, classification BibRef

Pilz, C.S., Ben Makhlouf, I., Habel, U., Leonhardt, S.,
Predicting Brainwaves from Face Videos,
CVPM20(1139-1147)
IEEE DOI 2008
Electroencephalography, Entropy, Organisms, Physiology, Sleep, Videos, Sensors BibRef

Kim, J., Kim, W., Oh, H., Lee, S., Lee, S.,
A Deep Cybersickness Predictor Based on Brain Signal Analysis for Virtual Reality Contents,
ICCV19(10579-10588)
IEEE DOI 2004
cognition, convolutional neural nets, electroencephalography, image sequences, medical image processing, recurrent neural nets, Feature extraction BibRef

Healy, G.[Graham], Wang, Z.W.[Zheng-Wei], Ward, T.[Tomas], Smeaton, A.[Alan], Gurrin, C.[Cathal],
Experiences and Insights from the Collection of a Novel Multimedia EEG Dataset,
MMMod20(II:475-486).
Springer DOI 2003
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Murashov, D., Obukhov, Y., Kershner, I., Sinkin, M.,
Detecting Events in Video Sequence of Video-EEG Monitoring,
PTVSBB19(155-159).
DOI Link 1912
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Mukherjee, P., Das, A., Bhunia, A.K., Roy, P.P.,
Cogni-Net: Cognitive Feature Learning Through Deep Visual Perception,
ICIP19(4539-4543)
IEEE DOI 1910
Knowledge-distillation, Teacher-Student network, EEG Signal, Knowledge Transfer BibRef

Kanwal, S., Uzair, M., Ullah, H., Khan, S.D., Ullah, M., Cheikh, F.A.,
An Image Based Prediction Model for Sleep Stage Identification,
ICIP19(1366-1370)
IEEE DOI 1910
Sleep stage identification, Electroencephalography, Dense convolutional neural networks BibRef

Orrú, G.[Giulia], Garau, M.[Marco], Fraschini, M.[Matteo], Acedo, J.[Javier], Didaci, L.[Luca], Ibáñez, D.[David], Soria-Frish, A.[Aureli], Marcialis, G.L.[Gian Luca],
Personal Identity Verification by EEG-Based Network Representation on a Portable Device,
CAIP19(II:164-171).
Springer DOI 1909
BibRef

Nguyen, B., Ma, W., Tran, D.,
Impact of lossy data compression techniques on EEG-based pattern recognition systems,
ICPR18(2308-2313)
IEEE DOI 1812
Electroencephalography, Discrete wavelet transforms, Identification of persons, Image coding, Person recognition BibRef

Mutasim, A.K., RaihanulBashar, M., SardarTipu, R., KafiulIslam, M., AshrafulAmin, M.,
Effect of Artefact Removal Techniques on EEG Signals for Video Category Classification,
ICPR18(3513-3518)
IEEE DOI 1812
Feature extraction, Electroencephalography, Classification algorithms, Signal processing algorithms, SWTSD BibRef

Liu, X., Tao, X., Zhan, Y.,
Calibrating Human Perception Threshold of Video Distortion Using EEG,
ICIP18(3543-3547)
IEEE DOI 1809
Distortion, Electroencephalography, Distortion measurement, Covariance matrices, Visualization, Quality assessment, LDA BibRef

Hernández, L.G.[Luis G.], Antelis, J.M.[Javier M.],
A Comparison of Deep Neural Network Algorithms for Recognition of EEG Motor Imagery Signals,
MCPR18(126-134).
Springer DOI 1807
BibRef

Gharbali, A.A.[Ali Abdollahi], Najdi, S.[Shirin], Fonseca, J.M.[José Manuel],
Transfer Learning of Spectrogram Image for Automatic Sleep Stage Classification,
ICIAR18(522-528).
Springer DOI 1807
BibRef

Qin, J., Liu, F., Wang, S., Rosenberger, J.,
EEG source imaging based on spatial and temporal graph structures,
IPTA17(1-6)
IEEE DOI 1804
electroencephalography, graph theory, inverse problems, medical signal processing, neurophysiology, signal resolution, Graph Regularization BibRef

Toyoda, A., Ogawa, T., Haseyama, M.,
MvLFDA-based video preference estimation using complementary properties of features,
ICIP17(635-639)
IEEE DOI 1803
Correlation, Electroencephalography, Estimation, Feature extraction, Music, Transforms, Visualization, individual preference BibRef

Orjuela-Cañón, A.D.[Alvaro D.], Renteria-Meza, O.[Osvaldo], Hernández, L.G.[Luis G.], Ruíz-Olaya, A.F.[Andrés F.], Cerquera, A.[Alexander], Antelis, J.M.[Javier M.],
Self-organizing Maps for Motor Tasks Recognition from Electrical Brain Signals,
CIARP17(458-465).
Springer DOI 1802
BibRef

Murabito, F.[Francesca], Palazzo, S.[Simone], Spampinato, C.[Concetto], Giordano, D.[Daniela],
Generating Knowledge-Enriched Image Annotations for Fine-Grained Visual Classification,
CIAP17(I:332-344).
Springer DOI 1711
BibRef

Spampinato, C.[Concetto], Palazzo, S.[Simone], Kavasidis, I., Giordano, D.[Daniela], Souly, N., Shah, M.,
Deep Learning Human Mind for Automated Visual Classification,
CVPR17(4503-4511)
IEEE DOI 1711
Brain modeling, Electroencephalography, Feature extraction, Machine learning, Manifolds, Visualization BibRef

Cirett-Galán, F.[Federico], Torres-Peralta, R.[Raquel], Beal, C.R.[Carole R.],
Language Proficiency Classification During Computer-Based Test with EEG Pattern Recognition Methods,
MCPR17(288-296).
Springer DOI 1706
BibRef

Sarker, T., Paul, S., Rayhan, A., Zabir, I., Shahnaz, C.,
Bi-spectral higher order statistics and time-frequency domain features for arithmetic task classification from EEG signals,
IVPR17(1-4)
IEEE DOI 1704
Data mining BibRef

Torres-Valencia, C., Hernandez-Muriel, J., Gonzalez-Vanegas, W., Alvarez-Meza, A., Orozco, A.A., Alvarez, M.A.,
Non-parametric Source Reconstruction via Kernel Temporal Enhancement for EEG Data,
CIARP16(443-450).
Springer DOI 1703
BibRef

Davis, P., Creusere, C.D., Kroger, J.,
The effect of perceptual video quality on EEG power distribution,
ICIP16(2420-2424)
IEEE DOI 1610
Degradation BibRef

Betül Yüce, A., Yaslan, Y.,
A disagreement based co-active learning method for sleep stage classification,
WSSIP16(1-4)
IEEE DOI 1608
frequency-domain analysis BibRef

Khouaja, I., Nouira, I., Bedoui, M.H., Akil, M.,
Enhancing EEG Surface Resolution by Using a Combination of Kalman Filter and Interpolation Method,
CGiV16(353-357)
IEEE DOI 1608
Kalman filters BibRef

Pinto, D.[David], Vilariño, D.[Darnes], Morales, I.[Illiana], Aguilar, C.[Cristina], Tovar, M.[Mireya],
Using N-Grams of Quantized EEG Values for Happiness Detection,
MCPR16(270-279).
Springer DOI 1608
BibRef

Avilés-Cruz, C.[Carlos], Villegas-Cortez, J.[Juan], Ferreyra-Ramírez, A.[Andrés], López, A.Z.[Arturo Zúñiga],
EEG Pattern Recognition: An Efficient Improvement Combination of ERD/ERS/Laterality Features to Create a Self-paced BCI System,
MCPR16(231-240).
Springer DOI 1608
BibRef

Meloni, P., Tuveri, G., Pani, D., Raffo, L., Palumbo, F.,
Exploring custom heterogeneous MPSoCs for real-time neural signal decoding,
DASIP15(1-8)
IEEE DOI 1605
biomedical electronics BibRef

Hassen, A.E., Histace, A., Terosiet, M., Romain, O.,
FPGA-based detection of QRS complexes in ECG signal,
DASIP15(1-7)
IEEE DOI 1605
electrocardiography BibRef

Davis, P., Creusere, C.D., Kroger, J.,
Assessing cross frequency coupling in EEG collected from subjects viewing video using a modified metric,
Southwest16(17-20)
IEEE DOI 1605
Band-pass filters BibRef

Davis, P.[Philip], Creusere, C.D.[Charles D.], Kroger, J.[Jim],
Subject identification based on EEG responses to video stimuli,
ICIP15(1523-1527)
IEEE DOI 1512
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Ghaemmaghami, P.[Pouya], Abadi, M.K.[Mojtaba Khomami], Kia, S.M.[Seyed Mostafa], Avesani, P.[Paolo], Sebe, N.[Nicu],
Movie Genre Classification by Exploiting MEG Brain Signals,
CIAP15(I:683-693).
Springer DOI 1511
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Patrone, M.[Martín], Lecumberry, F.[Federico], Martín, Á.[Álvaro], Ramirez, I.[Ignacio], Seroussi, G.[Gadiel],
EEG Signal Pre-Processing for the P300 Speller,
CIARP15(559-566).
Springer DOI 1511
BibRef

Valadez, S.[Sergio], Sossa, H.[Humberto], Santiago-Montero, R.[Raúl], Guevara, E.[Elizabeth],
Encoding Polysomnographic Signals into Spike Firing Rate for Sleep Staging,
MCPR15(282-291).
Springer DOI 1506
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Gudiño-Mendoza, B.[Berenice], Sossa, H.[Humberto], Sanchez-Ante, G.[Gildardo], Antelis, J.M.[Javier M.],
Classification of Motor States from Brain Rhythms Using Lattice Neural Networks,
MCPR16(303-312).
Springer DOI 1608
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Ojeda, L.[Leonardo], Vega, R.[Roberto], Falcon, L.E.[Luis Eduardo], Sanchez-Ante, G.[Gildardo], Sossa, H.[Humberto], Antelis, J.M.[Javier M.],
Classification of Hand Movements from Non-invasive Brain Signals Using Lattice Neural Networks with Dendritic Processing,
MCPR15(23-32).
Springer DOI 1506
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Bozhkov, L.[Lachezar], Georgieva, P.[Petia],
Brain Neural Data Analysis with Feature Space Defined by Descriptive Statistics,
IbPRIA15(415-422).
Springer DOI 1506
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Zammouri, A., Aitmoussa, A., Chevallier, S., Monacelli, E.,
Intelligentocular artifacts removal in a noninvasive singlechannel EEG recording,
ISCV15(1-5)
IEEE DOI 1506
brain BibRef

Moon, J.Y.[Jin-Young], Kwon, Y.J.[Yong-Jin], Kang, K.[Kyuchang], Bae, C.[Changseok], Yoon, W.C.[Wan Chul],
Recognition of Meaningful Human Actions for Video Annotation Using EEG Based User Responses,
MMMod15(II: 447-457).
Springer DOI 1501
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Zhao, R.[Rui], Schalk, G.[Gerwin], Ji, Q.A.[Qi-Ang],
Coupled Hidden Markov Model for Electrocorticographic Signal Classification,
ICPR14(1858-1862)
IEEE DOI 1412
Accuracy BibRef

García-Vega, S., Álvarez-Meza, A.M., Castellanos-Domínguez, G.[Germán],
Neural Decoding Using Kernel-Based Functional Representation of ECoG Recordings,
CIARP14(247-254).
Springer DOI 1411
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Kunze, K., Shiga, Y., Ishimaru, S., Kise, K.,
Reading Activity Recognition Using an Off-the-Shelf EEG: Detecting Reading Activities and Distinguishing Genres of Documents,
ICDAR13(96-100)
IEEE DOI 1312
electroencephalography BibRef

Relvas, V.[Vânia], Sanches, J.M.[J. Miguel], Figueiredo, P.[Patrícia],
Scalp EEG Continuous Space ERD/ERS Quantification,
IbPRIA13(616-623).
Springer DOI 1307
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Rothlübbers, S.[Sven], Relvas, V.[Vânia], Leal, A.[Alberto], Figueiredo, P.[Patrícia],
Characterization and Reduction of MR-Environment-Related EEG Artefacts,
IbPRIA13(808-818).
Springer DOI 1307
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Sivarajah, Y., Holden, E.J., Kovesi, P., Togneri, R., Tan, T., Price, G.,
Studying the Impact of Feature Saliency for Geoscientific Target Detection Using Electroencephalography (EEG),
DICTA12(1-6).
IEEE DOI 1303
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Yang, H.J.[Hui-Juan], Guan, C.T.[Cun-Tai], Ang, K.K.[Kai Keng], Zhang, H.[Haihong], Wang, C.C.[Chuan Chu],
Iterative clustering and support vectors-based high-confidence query selection for motor imagery EEG signals classification,
ICPR12(2169-2172).
WWW Link. 1302
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Lotte, F.[Fabien],
A new feature and associated optimal spatial filter for EEG signal classification: Waveform Length,
ICPR12(1302-1305).
WWW Link. 1302
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Khushaba, R.N.[Rami N.], Elliott, R.[Rosalind], Al Sukker, A.[Akram], Al-Ani, A.[Ahmed], McKinley, S.[Sharon],
Orthogonal Locality Sensitive Fuzzy Discriminant Analysis in Sleep-Stage Scoring,
ICPR10(165-168).
IEEE DOI 1008
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Iturralde, P.A.[Pablo A.], Patrone, M.[Martín], Lecumberry, F.[Federico], Fernández, A.[Alicia],
Motor Intention Recognition in EEG: In Pursuit of a Relevant Feature Set,
CIARP12(551-558).
Springer DOI 1209
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Fernandez, A., Penedo, M.G., Ortega, M., Cancela, B., Vazquez, C., Gigirey, L.M.,
Automatic Analysis of the Patient's Conscious Responses to the Emission of Auditory Stimuli during the Performance of an Audiometry,
DICTA11(291-296).
IEEE DOI 1205
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Bhattacharyya, S., Roy, A., Dogra, D.P., Biswas, A., Mukherjee, J., Majumdar, A.K., Majumdar, B., Mukherjee, S., Singh, A.K.,
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NCVPRIPG11(134-137).
IEEE DOI 1205
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Ito, S., Mitsukura, Y., Fukumi, M.,
A basic method for classifying humans based on an EEG analysis,
ICARCV08(1783-1786).
IEEE DOI 1109
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Metzen, J.H.[Jan Hendrik], Kim, S.K.[Su Kyoung], Kirchner, E.A.[Elsa Andrea],
Minimizing Calibration Time for Brain Reading,
DAGM11(366-375).
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direct brain interface BibRef

Li, M.Z.[Ming-Zhao], Pan, J.[Jing],
An Effective Classification Approach for EEG-based BCI System,
ICIG11(897-901).
IEEE DOI 1109
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Kim, S.P.[Sung-Phil], Kim, M.K.[Min-Ki], Park, G.T.[Gwi-Tae],
A Simulation Study on the Generative Neural Ensemble Decoding Algorithms,
ICPR10(3797-3800).
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cortical activity decoding. BibRef

Mehta, N.A.[Nishant Ajay], Starner, T.E.[Thad E.], Jackson, M.M.[Melody Moore], Babalola, K.O.[Karolyn O.], James, G.A.[George Andrew],
Recognizing Sign Language from Brain Imaging,
ICPR10(3842-3845).
IEEE DOI 1008
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Lotte, F.[Fabien], Guan, C.T.[Cun-Tai],
Spatially Regularized Common Spatial Patterns for EEG Classification,
ICPR10(3712-3715).
IEEE DOI 1008
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Su, F.[Fei], Xia, L.W.[Li-Wen], Cai, A.N.[An-Ni], Wu, Y.B.[Yi-Bing], Ma, J.S.[Jun-Shui],
EEG-based Personal Identification: from Proof-of-Concept to A Practical System,
ICPR10(3728-3731).
IEEE DOI 1008
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Wang, Z.[Zuoguan], Ji, Q.A.[Qi-Ang], Miller, K.J.[Kai J.], Schalk, G.[Gerwin],
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ICPR10(3756-3759).
IEEE DOI 1008
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Al-ani, T.[Tarik], Drouot, X.[Xavier], Huynh, T.T.T.[Thi Thuy Trang],
Non-linear EEG Analysis of Idiopathic Hypersomnia,
ICISP10(297-306).
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Bokehi, J.R., Vasconcellos, N.C.M., Conci, A.,
Use of Coherence Measurements between EEG and EMG on Identification of the Myoclonus Locus,
WSSIP09(1-4).
IEEE DOI 0906
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Koroutchev, K.[Kostadin], Korutcheva, E.[Elka], Kanev, K.[Kamen], Albariño, A.R.[Apolinar Rodríguez], Gutierrez, J.L.M.[Jose Luis Muñiz], Balseiro, F.F.[Fernando Fariñaz],
Detection of Unusual Objects and Temporal Patterns in EEG Video Recordings,
ISVC09(I: 965-974).
Springer DOI 0911
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Li, X.X.[Xiao-Xia], Xu, G.Z.[Gui-Zhi], Shuo, Y.[Yang], Shang, X.K.[Xiu-Kui],
Effect of EEG with the Acupuncture of Neiguan and Shenmen,
CISP09(1-5).
IEEE DOI 0910
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Mu, Z.D.[Zhen-Dong], Xiao, D.[Dan], Hu, J.F.[Jian-Feng],
Classification of Motor Imagery EEG Signals Based on STFTs,
CISP09(1-4).
IEEE DOI 0910
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Lv, C.Z.[Chang-Zhi], Fan, D.[Di], Wang, M.[Min], Gao, G.H.[Guang-Heng],
A Wireless Collector for EEG Signal of Freely Behaving White Mice,
CISP09(1-4).
IEEE DOI 0910
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Sourina, O.[Olga], Sourin, A.[Alexei], Kulish, V.[Vladimir],
EEG Data Driven Animation and Its Application,
MIRAGE09(380-388).
Springer DOI 0905
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Pires, P.[Pedro], Paiva, T.[Teresa], Sanches, J.M.[João M.],
Sleep/Wakefulness State from Actigraphy,
IbPRIA09(362-369).
Springer DOI 0906
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Rafik, K.[Khemakhem], Ahmed, B.H.[Ben Hamida], Imed, F.[Feki], Abdelmalik, T.A.[Taleb-Ahmed],
Recursive sLORETA-FOCUSS Algorithm for EEG Dipoles Localization,
IPTA08(1-5).
IEEE DOI 0811
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Alba, A.[Alfonso], Arce-Santana, E.[Edgar],
Visualization of Dynamic Connectivity in High Electrode-Density EEG,
ISVC08(I: 1040-1050).
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Chacón, M.[Max], Severino, M.[Mariela], Panerai, R.[Ronney],
Gray Box Model with an SVM to Represent the Influence of PaCO2 on the Cerebral Blood Flow Autoregulation,
CIARP11(630-637).
Springer DOI 1111
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Chacón, M.[Max], Diaz, D.[Darwin], Ríos, L.[Luis], Evans, D.[David], Panerai, R.[Ronney],
Support Vector Machine with External Recurrences for Modeling Dynamic Cerebral Autoregulation,
CIARP06(954-963).
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Breun, P.[Peter], Grosse-Wentrup, M.[Moritz], Utschick, W.[Wolfgang], Buss, M.[Martin],
Robust MEG Source Localization of Event Related Potentials: Identifying Relevant Sources by Non-Gaussianity,
DAGM06(394-403).
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Tomioka, R.[Ryota], Dornhege, G.[Guido], Nolte, G.[Guido], Aihara, K.[Kazuyuki], Müller, K.R.[Klaus-Robert],
Optimizing Spectral Filters for Single Trial EEG Classification,
DAGM06(414-423).
Springer DOI 0610
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Yun, M.H.[Myung Hwan], Lee, J.H.[Joo Hwan], Lee, H.J.[Hyoung-Joo], Cho, S.Z.[Sung-Zoon],
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ICB06(706-712).
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Xu, W.J.[Wen-Jie], Guan, C.T.[Cun-Tai], Siong, C.E.[Chng Eng], Ranganatha, S., Thulasidas, M., Wu, J.K.[Jian-Kang],
High accuracy classification of EEG signal,
ICPR04(II: 391-394).
IEEE DOI 0409
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Mattout, J., Garnero, L., Pelegrini-Issac, M., Benali, H.,
Statistical Method for Source Localization in MEG/EEG Tomographic Reconstruction Problem,
ICIP01(I: 714-717).
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Singh, S.,
EEG Data Classification with Localised Structural Information,
ICPR00(Vol II: 271-274).
IEEE DOI 0009
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Li, T.H., Klemm, W.R.,
Detection of Cognitive Binding During Ambiguous Figure Tasks by Wavelet Coherence Analysis of EEG Signals,
ICPR00(Vol III: 94-97).
IEEE DOI 0009
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Chitti, Y.[Yasmina],
Application of image processing in neurobiology: Detection of low signals with high spatial resolution and a non-uniform variance,
CIAP95(253-257).
Springer DOI 9509
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Cong, Z.X.[Zhang-Xin],
The study of human brain electrical activity: A topographic mapping approach,
ICPR88(II: 649-651).
IEEE DOI 8811
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Zhong, C.X.[Chun Xiang], Ping, X.[Xiao], Wu, X.M.[Xiao Ming], Wu, L.Y.[Lei Yi],
A contextual method for electroencephalogram recognition,
ICPR88(II: 911-913).
IEEE DOI 8811
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Brain Waves, EEG Analysis, Electroencephalogram for Biometrics .

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