21.9.8.3 Brain-Computer Interface, Brain-Machine Interface, Biomimetic

Brunner, C.[Clemens], Naeem, M.[Muhammad], Leeb, R.[Robert], Graimann, B.[Bernhard], Pfurtscheller, G.[Gert],
Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis,
PRL(28), No. 8, 1 June 2007, pp. 957-964.
Elsevier DOI 0704
Spatial filtering; Independent components analysis (ICA); Common spatial patterns (CSP); Principal components analysis (PCA); Electroencephalogram (EEG); Brain-computer interface (BCI); Motor imagery BibRef

Sun, S.L.[Shi-Liang], Zhang, C.S.[Chang-Shui], Zhang, D.[Dan],
An experimental evaluation of ensemble methods for EEG signal classification,
PRL(28), No. 15, 1 November 2007, pp. 2157-2163.
Elsevier DOI 0711
Brain-computer interface (BCI); EEG signal classification; Bagging; Boosting; Random subspace BibRef

Zhong, M.J.[Ming-Jun], Lotte, F.[Fabien], Girolami, M.[Mark], Lecuyer, A.[Anatole],
Classifying EEG for brain computer interfaces using Gaussian processes,
PRL(29), No. 3, 1 February 2008, pp. 354-359.
Elsevier DOI 0801
Gaussian process; Brain computer interfaces; Support vector machine; EEG BibRef

Glyn-Davies, A.[Alex], Girolami, M.[Mark],
Anomaly detection in streaming data with gaussian process based stochastic differential equations,
PRL(153), 2022, pp. 254-260.
Elsevier DOI 2201
Anomaly detection, Stochastic differential equations, Gaussian process, Bootstrapping, Streaming data BibRef

Li, Y.Q.[Yuan-Qing], Guan, C.T.[Cun-Tai], Li, H.Q.[Hui-Qi], Chin, Z.Y.[Zheng-Yang],
A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system,
PRL(29), No. 9, 1 July 2008, pp. 1285-1294.
Elsevier DOI 0711
Semi-supervised support vector machine (SVM); Model selection; Convergence; Brain computer interface (BCI); Electroencephalogram (EEG) BibRef

Lowne, D.R., Roberts, S.J., Garnett, R.,
Sequential non-stationary dynamic classification with sparse feedback,
PR(43), No. 3, March 2010, pp. 897-905.
Elsevier DOI 1001
Non-stationary dynamic classification; Sequential Bayesian learning; Missing data; Medical signal analysis; Brain-computer interface BibRef

Li, J.[Jie], Zhang, L.Q.[Li-Qing],
Regularized tensor discriminant analysis for single trial EEG classification in BCI,
PRL(31), No. 7, 1 May 2010, pp. 619-628.
Elsevier DOI 1004
Brain computer interfacing (BCI); Channel selection; Electroencephalogram (EEG); Regularized tensor discriminant analysis (RTDA); Single trial classification; Tensor factorization BibRef

Kayikcioglu, T.[Temel], Aydemir, O.[Onder],
A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data,
PRL(31), No. 11, 1 August 2010, pp. 1207-1215.
Elsevier DOI 1008
Brain computer interface (BCI); Polynomial fitting; k-Nearest neighbor; Electroencephalogram (EEG); Feature extraction; Classification BibRef

Cecotti, H.[Hubert], Graeser, A.[Axel],
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces,
PAMI(33), No. 3, March 2011, pp. 433-445.
IEEE DOI 1102
BibRef
Earlier:
Convolutional Neural Network with embedded Fourier Transform for EEG classification,
ICPR08(1-4).
IEEE DOI 0812
To enable spelling out words. BibRef

Cecotti, H.[Hubert],
A time-frequency convolutional neural network for the offline classification of steady-state visual evoked potential responses,
PRL(32), No. 8, 1 June 2011, pp. 1145-1153.
Elsevier DOI 1101
Neural network; Convolution; Fourier transform; Spatial filters; Steady-state visual evoked potential (SSVEP); Electroencephalogram (EEG) BibRef

Zhang, S.M.[Shao-Min], Jiang, B.[Bo], Zhu, J.M.[Jun-Ming], Zhang, Q.S.[Qiao-Sheng], Chen, W.D.[Wei-Dong], Zheng, X.X.[Xiao-Xiang], Zhao, T.[Ting],
A study on combining local field potential and single unit activity for better neural decoding,
IJIST(21), No. 2, June 2011, pp. 165-172.
DOI Link 1101
brain-machine interface; local field potential; single-unit activity; neural decoding; time-frequency domain BibRef

Gouy-Pailler, C.[Cédric], Sebag, M.[Michèle], Larue, A.[Anthony], Souloumiac, A.[Antoine],
Single trial variability in brain-computer interfaces based on motor imagery: Learning in the presence of labeling noise,
IJIST(21), No. 2, June 2011, pp. 148-157.
DOI Link 1101
BibRef

Zhang, S.M.[Shao-Min], Liao, Y.X.[Yu-Xi], Zheng, X.X.[Xiao-Xiang], Chen, W.D.[Wei-Dong], Wang, Y.W.[Yi-Wen],
Decoding the nonstationary neural activity in motor cortex for brain machine interfaces,
IJIST(21), No. 2, June 2011, pp. 158-164.
DOI Link 1101
BibRef

Suk, H.I.[Heung-Il], Lee, S.W.[Seong-Whan],
Subject and class specific frequency bands selection for multiclass motor imagery classification,
IJIST(21), No. 2, June 2011, pp. 123-130.
DOI Link 1101
brain-computer interface; frequency bands selection; motor imagery classification; ERD/ERS; electroencephalography BibRef

Kroeker, K.L.[Kirk L.],
Improving Brain-Computer Interfaces,
CACM(54), No. 10, October 2011, pp. 11-14.
DOI Link 1110
Researchers are demonstrating advances in restorative BCI systems that are giving paralyzed individuals more effective ways to communicate, move, and interact with their environment. BibRef

Sasayama, T.[Teruyoshi], Kobayashi, T.[Tetsuo],
Movement-Imagery Brain-Computer Interface: EEG Classification of Beta Rhythm Synchronization Based on Cumulative Distribution Function,
IEICE(E94-D), No. 12, December 2011, pp. 2479-2486.
WWW Link. 1112
BibRef

Lee, P.L., Chang, H.C., Hsieh, T.Y., Deng, H.T., Sun, C.W.,
A Brain-Wave-Actuated Small Robot Car Using Ensemble Empirical Mode Decomposition-Based Approach,
SMC-A(42), No. 5, September 2012, pp. 1053-1064.
IEEE DOI 1208
BibRef

van de Ville, D.[Dimitri], Lee, S.W.[Seong-Whan],
Brain decoding: Opportunities and challenges for pattern recognition,
PR(45), No. 6, June 2012, pp. 2033-2034.
Elsevier DOI 1202
Brain decoding; Neuroimaging; Functional magnetic resonance imaging; Electroencephalography; Brain-computer interface. Special issue intro. BibRef

Daly, I.[Ian], Nasuto, S.J.[Slawomir J.], Warwick, K.[Kevin],
Brain computer interface control via functional connectivity dynamics,
PR(45), No. 6, June 2012, pp. 2123-2136.
Elsevier DOI 1202
BCI; Phase synchronization; Functional connectivity; Complex networks; Finger tapping; HMM BibRef

Becedas, J.,
Brain-Machine Interfaces: Basis and Advances,
SMC-C(42), No. 6, November 2012, pp. 825-836.
IEEE DOI 1210
BibRef

Ang, K.K.[Kai Keng], Chin, Z.Y.[Zheng Yang], Zhang, H.H.[Hai-Hong], Guan, C.T.[Cun-Tai],
Mutual information-based selection of optimal spatial-temporal patterns for single-trial EEG-based BCIs,
PR(45), No. 6, June 2012, pp. 2137-2144.
Elsevier DOI 1202
Brain-computer interface (BCI); Electroencephalogram (EEG); Mutual information; Feature selection; Bayesian classification BibRef

van Erp, J.[Jan], Lotte, F.[Fabien], Tangermann, M.[Michael],
Brain-Computer Interfaces: Beyond Medical Applications,
Computer(45), No. 4, April 2012, pp. 26-34.
IEEE DOI 1204
BibRef

Escolano, C., Antelis, J.M., Minguez, J.,
A Telepresence Mobile Robot Controlled With a Noninvasive Brain-Computer Interface,
SMC-B(42), No. 3, June 2012, pp. 793-804.
IEEE DOI 1202
BibRef

Suk, H.I.[Heung-Il], Lee, S.W.[Seong-Whan],
A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces,
PAMI(35), No. 2, February 2013, pp. 286-299.
IEEE DOI 1301
EEG based interface. Learning. BibRef

Bi, L.Z.[Lu-Zheng], Fan, X.A.[Xin-An], Liu, Y.[Yili],
EEG-Based Brain-Controlled Mobile Robots: A Survey,
HMS(43), No. 2, March 2013, pp. 161-176.
IEEE DOI 1303
Survey, BCI. BibRef

Li, H.Q.[Hong-Qi], Bi, L.Z.[Lu-Zheng], Li, X.Y.[Xiao-Ya], Gan, H.P.[Hong-Ping],
Robust Predictive Control for EEG-Based Brain-Robot Teleoperation,
ITS(25), No. 8, August 2024, pp. 9130-9140.
IEEE DOI 2408
Robots, Electroencephalography, Safety, Robot kinematics, Navigation, Mobile robots, Robustness, Neurorobotics, robustness, safety BibRef

Lee, K.Y.[Kyeong-Yeon], Kim, S.[Sun],
Designing discriminative spatial filter vectors in motor imagery brain-computer interface,
IJIST(23), No. 2, 2013, pp. 147-151.
DOI Link 1307
brain-computer interface, common spatial pattern, feature selection, motor imagery classification, electroencephalography BibRef

Wang, H.X.[Hai-Xian],
Discriminant and adaptive extensions to local temporal common spatial patterns,
PRL(34), No. 10, 15 July 2013, pp. 1125-1129.
Elsevier DOI 1306
Common spatial patterns; Brain-computer interfaces (BCI); Fisher criterion; Sparse representation BibRef

Bi, L., Fan, X.A., Luo, N., Jie, K., Li, Y., Liu, Y.,
A Head-Up Display-Based P300 Brain-Computer Interface for Destination Selection,
ITS(14), No. 4, 2013, pp. 1996-2001.
IEEE DOI 1312
Brain-computer interfaces BibRef

Fan, X., Bi, L., Teng, T., Ding, H., Liu, Y.,
A Brain-Computer Interface-Based Vehicle Destination Selection System Using P300 and SSVEP Signals,
ITS(16), No. 1, February 2015, pp. 274-283.
IEEE DOI 1502
Accuracy BibRef

Dura-Bernal, S.[Salvador], Chadderdon, G.L.[George L.], Neymotin, S.A.[Samuel A.], Francis, J.T.[Joseph T.], Lytton, W.W.[William W.],
Towards a real-time interface between a biomimetic model of sensorimotor cortex and a robotic arm,
PRL(36), No. 1, 2014, pp. 204-212.
Elsevier DOI 1312
Real-time interface BibRef

Gandhi, V., Prasad, G., Coyle, D., Behera, L., McGinnity, T.M.,
EEG-Based Mobile Robot Control Through an Adaptive Brain-Robot Interface,
SMCS(44), No. 9, September 2014, pp. 1278-1285.
IEEE DOI 1410
adaptive control BibRef

Kakoty, N.M.[Nayan M.], Hazarika, S.M.[Shyamanta M.],
Development of an electromyographic controlled biomimetic prosthetic hand,
IJCVR(4), No. 1-2, 2014, pp. 115-133.
DOI Link 1403
BibRef

Kao, J.C., Stavisky, S.D., Sussillo, D., Nuyujukian, P., Shenoy, K.V.,
Information Systems Opportunities in Brain-Machine Interface Decoders,
PIEEE(102), No. 5, May 2014, pp. 666-682.
IEEE DOI 1405
Algorithm design and analysis BibRef

Shi, Z.Z.[Zhong-Zhi], Zhang, J.H.[Jian-Hua], Yang, X.[Xi], Ma, G.[Gang], Qi, B.Y.[Bao-Yuan], Yue, J.P.[Jin-Peng],
Computational Cognitive Models for Brain-Machine Collaborations,
IEEE_Int_Sys(29), No. 6, November 2014, pp. 24-31.
IEEE DOI 1502
brain-computer interfaces BibRef

Lee, M.H.[Min-Ho], Fazli, S.[Siamac], Mehnert, J.[Jan], Lee, S.W.[Seong-Whan],
Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI,
PR(48), No. 8, 2015, pp. 2725-2737.
Elsevier DOI 1505
Hybrid brain-computer interfacing BibRef

Muller-Putz, G., del R Millan, J., Schalk, G., Muller, K.,
The Plurality of Human Brain-Computer Interfacing,
PIEEE(103), No. 6, June 2015, pp. 868-870.
IEEE DOI 1506
[Scanning the Issue]. Biomedical monitoring BibRef

Lotte, F.,
Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain-Computer Interfaces,
PIEEE(103), No. 6, June 2015, pp. 871-890.
IEEE DOI 1506
brain-computer interfaces BibRef

Fazli, S., Dahne, S., Samek, W., Bieszmann, F., Muller, K.R.,
Learning From More Than One Data Source: Data Fusion Techniques for Sensorimotor Rhythm-Based Brain-Computer Interfaces,
PIEEE(103), No. 6, June 2015, pp. 891-906.
IEEE DOI 1506
brain-computer interfaces BibRef

He, B.[Bin], Baxter, B., Edelman, B.J., Cline, C.C., Ye, W.W.,
Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms,
PIEEE(103), No. 6, June 2015, pp. 907-925.
IEEE DOI 1506
brain-computer interfaces BibRef

Muller-Putz, G., Leeb, R., Tangermann, M., Hohne, J., Kubler, A., Cincotti, F., Mattia, D., Rupp, R., Muller, K.R., Millan, J.D.[J. Del_R],
Towards Noninvasive Hybrid Brain-Computer Interfaces: Framework, Practice, Clinical Application, and Beyond,
PIEEE(103), No. 6, June 2015, pp. 926-943.
IEEE DOI 1506
assisted living BibRef

Leeb, R., Tonin, L., Rohm, M., Desideri, L., Carlson, T., del R Millan, J.,
Towards Independence: A BCI Telepresence Robot for People With Severe Motor Disabilities,
PIEEE(103), No. 6, June 2015, pp. 969-982.
IEEE DOI 1506
brain-computer interfaces BibRef

Peck, E.M.[Evan M.], Carlin, E.[Emily], Jacob, R.[Robert],
Designing Brain-Computer Interfaces for Attention-Aware Systems,
Computer(48), No. 10, October 2015, pp. 34-42.
IEEE DOI 1511
BCIs BibRef

Cecotti, H.[Hubert],
Toward shift invariant detection of event-related potentials in non-invasive brain-computer interface,
PRL(66), No. 1, 2015, pp. 127-134.
Elsevier DOI 1511
Event-related potentials (ERP) BibRef

Lu, J.[Jie], Mamun, K.A.[Khondaker A.], Chau, T.[Tom],
Pattern classification to optimize the performance of Transcranial Doppler Ultrasonography-based brain machine interface,
PRL(66), No. 1, 2015, pp. 135-143.
Elsevier DOI 1511
Transcranial Doppler (TCD) Ultrasonography BibRef

Obeidat, Q.T., Campbell, T.A., Kong, J.,
Introducing the Edges Paradigm: A P300 Brain-Computer Interface for Spelling Written Words,
HMS(45), No. 6, December 2015, pp. 727-738.
IEEE DOI 1512
Accuracy. BibRef

Higger, M., Akcakaya, M., Nezamfar, H., La Mountain, G., Orhan, U., Erdogmus, D.,
A Bayesian Framework for Intent Detection and Stimulation Selection in SSVEP BCIs,
SPLetters(22), No. 6, June 2015, pp. 743-747.
IEEE DOI 1411
Brain Computer Interfaces (BCI). BibRef

Zhao, X., Chu, Y., Han, J., Zhang, Z.,
SSVEP-Based Brain-Computer Interface Controlled Functional Electrical Stimulation System for Upper Extremity Rehabilitation,
SMCS(46), No. 7, July 2016, pp. 947-956.
IEEE DOI 1606
Electroencephalography BibRef

Bhattacharyya, S., Shimoda, S., Hayashibe, M.,
A Synergetic Brain-Machine Interfacing Paradigm for Multi-DOF Robot Control,
SMCS(46), No. 7, July 2016, pp. 957-968.
IEEE DOI 1606
Decoding BibRef

Waytowich, N.R., Krusienski, D.J.,
Multiclass Steady-State Visual Evoked Potential Frequency Evaluation Using Chirp-Modulated Stimuli,
HMS(46), No. 4, August 2016, pp. 593-600.
IEEE DOI 1608
brain-computer interfaces BibRef

Handiru, V.S., Prasad, V.A.,
Optimized Bi-Objective EEG Channel Selection and Cross-Subject Generalization With Brain-Computer Interfaces,
HMS(46), No. 6, December 2016, pp. 777-786.
IEEE DOI 1612
brain-computer interfaces BibRef

Balducci, F.[Fabrizio], Grana, C.[Costantino], Cucchiara, R.[Rita],
Affective level design for a role-playing videogame evaluated by a brain-computer interface and machine learning methods,
VC(33), No. 4, April 2017, pp. 413-427.
WWW Link. 1704
BibRef

Vourvopoulos, A.[Athanasios], Bermudez i Badia, S.[Sergi], Liarokapis, F.[Fotis],
EEG correlates of video game experience and user profile in motor-imagery-based brain-computer interaction,
VC(33), No. 4, April 2017, pp. 533-546.
WWW Link. 1704
BibRef

Lee, C.C., Chuang, C.C., Yeng, C.H., Chen, Y.J., Lin, B.S.,
Noise Suppression by Minima Controlled Recursive Averaging for SSVEP-Based BCIs With Single Channel,
SPLetters(24), No. 12, December 2017, pp. 1783-1787.
IEEE DOI 1712
amplitude estimation, brain-computer interfaces, diseases, electroencephalography, medical signal processing, steady-state visual evoked potentials BibRef

Alt, M.T., Fiedler, E., Rudmann, L., Ordonez, J.S., Ruther, P., Stieglitz, T.,
Let There Be Light: Optoprobes for Neural Implants,
PIEEE(105), No. 1, January 2017, pp. 101-138.
IEEE DOI 1612
Biological tissues BibRef

Maharbiz, M.M., Muller, R., Alon, E., Rabaey, J.M., Carmena, J.M.,
Reliable Next-Generation Cortical Interfaces for Chronic Brain-Machine Interfaces and Neuroscience,
PIEEE(105), No. 1, January 2017, pp. 73-82.
IEEE DOI 1612
Electrodes BibRef

Muraskin, J., Sherwin, J., Lieberman, G., Garcia, J.O., Verstynen, T., Vettel, J.M., Sajda, P.,
Fusing Multiple Neuroimaging Modalities to Assess Group Differences in Perception-Action Coupling,
PIEEE(105), No. 1, January 2017, pp. 83-100.
IEEE DOI 1612
Couplings BibRef

Ha, S., Akinin, A., Park, J., Kim, C., Wang, H., Maier, C., Mercier, P.P., Cauwenberghs, G.,
Silicon-Integrated High-Density Electrocortical Interfaces,
PIEEE(105), No. 1, January 2017, pp. 11-33.
IEEE DOI 1612
Biomedical monitoring BibRef

Akay, M., Sajda, P., Micera, S., Carmena, J.M.,
Advanced Technologies for Brain Research,
PIEEE(105), No. 1, January 2017, pp. 8-10.
IEEE DOI 1612
BibRef

Dweiri, Y.M., Eggers, T.E., Gonzalez-Reyes, L.E., Drain, J., McCallum, G.A., Durand, D.M.,
Stable Detection of Movement Intent From Peripheral Nerves: Chronic Study in Dogs,
PIEEE(105), No. 1, January 2017, pp. 50-65.
IEEE DOI 1612
Dogs BibRef

Ohta, J., Ohta, Y., Takehara, H., Noda, T., Sasagawa, K., Tokuda, T., Haruta, M., Kobayashi, T., Akay, Y.M., Akay, M.,
Implantable Microimaging Device for Observing Brain Activities of Rodents,
PIEEE(105), No. 1, January 2017, pp. 158-166.
IEEE DOI 1612
Brain BibRef

Abdalsalam, E.[Eltaf], Yusoff, M.Z.[Mohd Zuki], Malik, A.[Aamir], Kamel, N.S.[Nidal S.], Mahmoud, D.[Dalia],
Modulation of sensorimotor rhythms for brain-computer interface using motor imagery with online feedback,
SIViP(12), No. 3, March 2018, pp. 557-564.
Springer DOI 1804
BibRef

Mishuhina, V., Jiang, X.,
Feature Weighting and Regularization of Common Spatial Patterns in EEG-Based Motor Imagery BCI,
SPLetters(25), No. 6, June 2018, pp. 783-787.
IEEE DOI 1806
brain-computer interfaces, electroencephalography, feature extraction, feature selection, medical signal processing, motor imagery (MI) BibRef

Teng, T., Bi, L., Liu, Y.,
EEG-Based Detection of Driver Emergency Braking Intention for Brain-Controlled Vehicles,
ITS(19), No. 6, June 2018, pp. 1766-1773.
IEEE DOI 1806
Brain modeling, Electroencephalography, Feature extraction, Sensors, Testing, Training, Vehicles, Brain-controlled vehicle, emergency braking intention BibRef

Nourmohammadi, A., Jafari, M., Zander, T.O.,
A Survey on Unmanned Aerial Vehicle Remote Control Using Brain-Computer Interface,
HMS(48), No. 4, August 2018, pp. 337-348.
IEEE DOI 1808
aircraft control, autonomous aerial vehicles, brain-computer interfaces, feature extraction, unmanned aerial vehicle (UAV) BibRef

Hou, H.R., Meng, Q.H., Zeng, M., Sun, B.,
Improving Classification of Slow Cortical Potential Signals for BCI Systems With Polynomial Fitting and Voting Support Vector Machine,
SPLetters(25), No. 2, February 2018, pp. 283-287.
IEEE DOI 1802
brain-computer interfaces, electroencephalography, medical signal processing, signal classification, wavelet decomposition (WD) BibRef

Qu, J., Wang, F., Xia, Z., Yu, T., Xiao, J., Yu, Z., Gu, Z., Li, Y.,
A Novel Three-Dimensional P300 Speller Based on Stereo Visual Stimuli,
HMS(48), No. 4, August 2018, pp. 392-399.
IEEE DOI 1808
bioelectric potentials, brain-computer interfaces, electroencephalography, medical signal processing, three-dimensional (3-D) BibRef

Özbeyaz, A.[Abdurrahman], Arica, S.[Sami],
Familiar/unfamiliar face classification from EEG signals by utilizing pairwise distant channels and distinctive time interval,
SIViP(12), No. 6, September 2018, pp. 1181-1188.
WWW Link. 1808
BibRef

Bablani, A.[Annushree], Edla, D.R.[Damodar Reddy], Tripathi, D.[Diwakar], Cheruku, R.[Ramalingaswamy],
Survey on Brain-Computer Interface: An Emerging Computational Intelligence Paradigm,
Surveys(51), No. 1, February 2019, pp. Article No 20.
DOI Link 1906
Survey, Brain-Computer Interface. BibRef

Scudellari, M.,
Wanted: HI-RES, surgery-free brain interfaces: DARPA aims to develop wearable devices that let soldiers communicate directly with machines,
Spectrum(56), No. 7, July 2019, pp. 9-10.
IEEE DOI 1907
BibRef

Bablani, A.[Annushree], Edla, D.R.[Damodar Reddy], Tripathi, D.[Diwakar], Kuppili, V.[Venkatanareshbabu],
An efficient Concealed Information Test: EEG feature extraction and ensemble classification for lie identification,
MVA(30), No. 5, July 2019, pp. 813-832.
Springer DOI 1907
BibRef

Zhang, D., Yao, L., Chen, K., Monaghan, J.,
A Convolutional Recurrent Attention Model for Subject-Independent EEG Signal Analysis,
SPLetters(26), No. 5, May 2019, pp. 715-719.
IEEE DOI 1905
brain-computer interfaces, convolutional neural nets, electroencephalography, medical signal processing, deep learning BibRef

van de Laar, B., Bos, D.P.O., Reuderink, B., Poel, M., Nijholt, A.,
How Much Control Is Enough? Influence of Unreliable Input on User Experience,
Cyber(43), No. 6, 2013, pp. 1584-1592.
IEEE DOI 1312
brain-computer interfaces BibRef

Zhang, S.L.[Shuai-Lei], Wang, S.[Shuai], Zheng, D.Z.[De-Zhi], Zhu, K.[Kai], Dai, M.X.[Meng-Xi],
A novel pattern with high-level commands for encoding motor imagery-based brain computer interface,
PRL(125), 2019, pp. 28-34.
Elsevier DOI 1909
Brain-computer interface, Motor imagery, High-level commands, Distinctiveness, Stability BibRef

Ma, Z.[Zheng], Cheng, J.[Jun], Tao, D.P.[Da-Peng],
Online learning using projections onto shrinkage closed balls for adaptive brain-computer interface,
PR(97), 2020, pp. 107017.
Elsevier DOI 1910
Online learning, Projections, Wearable/portable brain computer interface, Biometrics BibRef

Stegman, P., Crawford, C.S., Andujar, M., Nijholt, A., Gilbert, J.E.,
Brain-Computer Interface Software: A Review and Discussion,
HMS(50), No. 2, April 2020, pp. 101-115.
IEEE DOI 2004
Bibliometric, brain-computer interface (BCI), platforms, signal processing, software BibRef

Liu, X., Tao, X., Xu, M., Zhan, Y., Lu, J.,
An EEG-Based Study on Perception of Video Distortion Under Various Content Motion Conditions,
MultMed(22), No. 4, April 2020, pp. 949-960.
IEEE DOI 2004
Distortion, Electroencephalography, Quality assessment, Distortion measurement, Sensitivity, Visualization, AUC BibRef

Kwak, N., Lee, S.,
Error Correction Regression Framework for Enhancing the Decoding Accuracies of Ear-EEG Brain-Computer Interfaces,
Cyber(50), No. 8, August 2020, pp. 3654-3667.
IEEE DOI 2007
Electroencephalography, Decoding, Visualization, Electrodes, Ear, Estimation, Brain modeling, Brain-computer interface (BCI), steady-state visual evoked potential (SSVEP) BibRef

Abiri, R., Borhani, S., Kilmarx, J., Esterwood, C., Jiang, Y., Zhao, X.,
A Usability Study of Low-Cost Wireless Brain-Computer Interface for Cursor Control Using Online Linear Model,
HMS(50), No. 4, August 2020, pp. 287-297.
IEEE DOI 2007
Electroencephalography, Brain modeling, Training, Usability, Kinematics, Controllability, Headphones, usability BibRef

King, J.T., Prasad, M., Tsai, T., Ming, Y.R., Lin, C.T.,
Influence of Time Pressure on Inhibitory Brain Control During Emergency Driving,
SMCS(50), No. 11, November 2020, pp. 4408-4414.
IEEE DOI 1806
Electroencephalography, Task analysis, Stress, Automobiles, Brakes, Brain modeling, Matlab, Electroencephalography (EEG), time pressure BibRef

Grissmann, S., Spüler, M., Faller, J., Krumpe, T., Zander, T.O., Kelava, A., Scharinger, C., Gerjets, P.,
Context Sensitivity of EEG-Based Workload Classification Under Different Affective Valence,
AffCom(11), No. 2, April 2020, pp. 327-334.
IEEE DOI 2006
Electroencephalography, Brain-computer interfaces, Adaptive systems, Brain-computer interface (BCI), emoback BibRef

Noorzadeh, S., Rivet, B., Jutten, C.,
3-D Interface for the P300 Speller BCI,
HMS(50), No. 6, December 2020, pp. 604-612.
IEEE DOI 2011
Keyboards, Stereo image processing, Performance evaluation, Brain-computer interfaces, Ergonomics, 3-D virtual keyboard BibRef

Landau, O.[Ofir], Puzis, R.[Rami], Nissim, N.[Nir],
Mind Your Mind: EEG-Based Brain-Computer Interfaces and Their Security in Cyber Space,
Surveys(53), No. 1, February 2020, pp. xx-yy.
DOI Link 2006
security, cyber space, attack, privacy, EEG, detection, Brain-computer interface BibRef

Mishuhina, V.[Vasilisa], Jiang, X.D.[Xu-Dong],
Complex common spatial patterns on time-frequency decomposed EEG for brain-computer interface,
PR(115), 2021, pp. 107918.
Elsevier DOI 2104
Brain-computer interface, Common spatial patterns, Electroencephalography, Motor imagery, Signal decomposition BibRef

Hagengruber, A., Leipscher, U., Eskofier, B.M., Vogel, J.,
Electromyography for Teleoperated Tasks in Weightlessness,
HMS(51), No. 2, April 2021, pp. 130-140.
IEEE DOI 2103
Robots, Task analysis, Muscles, Electromyography, Aerospace electronics, Training, Space missions, weightlessness BibRef

Robinson, N., Chester, T.W.J., KG, S.,
Use of Mobile EEG in Decoding Hand Movement Speed and Position,
HMS(51), No. 2, April 2021, pp. 120-129.
IEEE DOI 2103
Electroencephalography, Task analysis, Decoding, Image reconstruction, Predictive models, Prediction algorithms, movement speed and position BibRef

Bernal, S.L.[Sergio Lopez], Celdran, A.H.[Alberto Huertas], Perez, G.M.[Gregorio Martinez], Barros, M.T.[Michael Taynnan], Balasubramaniam, S.[Sasitharan],
Security in Brain-Computer Interfaces: State-of-the-Art, Opportunities, and Future Challenges,
Surveys(54), No. 1, January 2021, pp. xx-yy.
DOI Link 2104
Survey, Brain-Computer Interface. BCI, safety, cybersecurity, privacy, Brain-computer interfaces BibRef

Wang, B.[Boyu], Wong, C.M.[Chi Man], Kang, Z.[Zhao], Liu, F.[Feng], Shui, C.J.[Chang-Jian], Wan, F.[Feng], Chen, C.L.P.[C. L. Philip],
Common Spatial Pattern Reformulated for Regularizations in Brain-Computer Interfaces,
Cyber(51), No. 10, October 2021, pp. 5008-5020.
IEEE DOI 2110
Electroencephalography, Linear regression, Feature extraction, Covariance matrices, Minimization, transfer learning BibRef

Huang, H.Y.[Hai-Yun], Xie, Q.Y.[Qiu-You], Pan, J.H.[Jia-Hui], He, Y.B.[Yan-Bin], Wen, Z.F.[Zhen-Fu], Yu, R.H.[Rong-Hao], Li, Y.Q.[Yuan-Qing],
An EEG-Based Brain Computer Interface for Emotion Recognition and Its Application in Patients with Disorder of Consciousness,
AffCom(12), No. 4, October 2021, pp. 832-842.
IEEE DOI 2112
Emotion recognition, Electroencephalography, Real-time systems, Support vector machines, Brain-computer interfaces, disorder of consciousness (DOC) BibRef

Zhuang, J.Y.[Jia-Yu], Geng, K.K.[Ke-Ke], Yin, G.D.[Guo-Dong],
Ensemble Learning Based Brain-Computer Interface System for Ground Vehicle Control,
SMCS(51), No. 9, September 2021, pp. 5392-5404.
IEEE DOI 2108
Electroencephalography, Feature extraction, Land vehicles, Testing, Training, Electrodes, Brain-computer interface (BCI), vehicle motion control BibRef

Mahamune, R.[Rupesh], Laskar, S.H.[Shahedul H.],
Classification of the four-class motor imagery signals using continuous wavelet transform filter bank-based two-dimensional images,
IJIST(31), No. 4, 2021, pp. 2237-2248.
DOI Link 2112
brain computer interface, common spatial pattern, continuous wavelet transform, convolutional neural network, motor imagery BibRef

Agarwal, P.[Prabhakar], Kumar, S.[Sandeep],
Electroencephalography based imagined alphabets classification using spatial and time-domain features,
IJIST(32), No. 1, 2022, pp. 111-122.
DOI Link 2201
brain-computer interface, classification, electroencephalogram, imagined alphabets BibRef

Abibullaev, B.[Berdakh], Zollanvari, A.[Amin],
A Systematic Deep Learning Model Selection for P300-Based Brain-Computer Interfaces,
SMCS(52), No. 5, May 2022, pp. 2744-2756.
IEEE DOI 2205
Brain modeling, Deep learning, Feature extraction, Electroencephalography, Visualization, Electrodes, Decoding, P300 waves BibRef

Liu, X.J.[Xue-Jie], Kasmarik, K.[Kathryn], Abbass, H.[Hussein],
Assessing Player Profiles of Achievement, Affiliation, and Power Motivation Using Electroencephalography,
SMCS(52), No. 6, June 2022, pp. 3648-3658.
IEEE DOI 2206
Electroencephalography, Games, Psychology, Task analysis, Brain modeling, Particle measurements, Labeling, Computer game, player motivation BibRef

Wang, Z.P.[Zhong-Peng], He, B.B.[Bei-Bei], Zhou, Y.J.[Yi-Jie], Chen, L.[Long], Gu, B.[Bin], Liu, S.[Shuang], Xu, M.[Minpeng], He, F.[Feng], Ming, D.[Dong],
Incorporating EEG and EMG Patterns to Evaluate BCI-Based Long-Term Motor Training,
HMS(52), No. 4, August 2022, pp. 648-657.
IEEE DOI 2208
Electromyography, Electroencephalography, Training, Task analysis, Electrodes, Couplings, Visualization, Brain-computer interface, transfer entropy BibRef

Fumanal-Idocin, J.[Javier], Wang, Y.K.[Yu-Kai], Lin, C.T.[Chin-Teng], Fernández, J.[Javier], Sanz, J.A.[José Antonio], Bustince, H.[Humberto],
Motor-Imagery-Based Brain-Computer Interface Using Signal Derivation and Aggregation Functions,
Cyber(52), No. 8, August 2022, pp. 7944-7955.
IEEE DOI 2208
Electroencephalography, Feature extraction, Decision making, Rhythm, Data mining, Task analysis, Smart cities, signal processing BibRef

Fumanal-Idocin, J., Vidaurre, C., Fernandez, J., Gómez, M., Andreu-Perez, J., Prasad, M., Bustince, H.,
Supervised penalty-based aggregation applied to motor-imagery based brain-computer-interface,
PR(145), 2024, pp. 109924.
Elsevier DOI 2311
Brain-computer interface, Motor imagery, Penalty function, Aggregation functions, Classification, Signal processing BibRef

Kim, H.S.[Hyun-Seok], Ahn, M.H.[Min-Hee], Min, B.K.[Byoung-Kyong],
Deep-Learning-Based Automatic Selection of Fewest Channels for Brain-Machine Interfaces,
Cyber(52), No. 9, September 2022, pp. 8668-8680.
IEEE DOI 2208
Electroencephalography, Decoding, Brain modeling, Feature extraction, Task analysis, Deep learning, electroencephalography (EEG) BibRef

Shi, H.[Haonan], Bi, L.Z.[Lu-Zheng], Yang, Z.[Zhenge], Fei, W.J.[Wei-Jie],
A Novel Control Framework of Brain-Controlled Vehicle Based on Fuzzy Logic and Model Predictive Control,
ITS(23), No. 11, November 2022, pp. 21777-21789.
IEEE DOI 2212
Brain modeling, Adaptation models, Probabilistic logic, Vehicles, Task analysis, Electroencephalography, Wheels, model predictive control BibRef

Chang, E.[Edward],
This Implant Turns Brain Waves Into Words: A brain-computer interface deciphers commands intended for the vocal tract,
Spectrum(60), No. 2, February 2023, pp. 20-25.
IEEE DOI 2302
Brain modeling, Implants, Touch sensitive screens, Clinical trials, Text recognition, Brain-computer interfaces BibRef

Mahamune, R.[Rupesh], Laskar, S.H.[Shahedul H.],
An automatic channel selection method based on the standard deviation of wavelet coefficients for motor imagery based brain-computer interfacing,
IJIST(33), No. 2, 2023, pp. 714-728.
DOI Link 2303
brain computer interface, common spatial pattern, continuous wavelet transform, convolutional neural network, motor imagery BibRef

Meng, M.[Ming], Dong, Z.C.[Zhi-Chao], Gao, Y.[Yunyuan], She, Q.S.[Qing-Shan],
Optimal channel and frequency band-based feature selection for motor imagery electroencephalogram classification,
IJIST(33), No. 2, 2023, pp. 670-679.
DOI Link 2303
brain-computer interface, common spatial pattern, electroencephalogram, motor imagery BibRef

Spapé, M.[Michiel], Davis, K.M.[Keith M.], Kangassalo, L.[Lauri], Ravaja, N.[Niklas], Sovijärvi-Spapé, Z.[Zania], Ruotsalo, T.[Tuukka],
Brain-Computer Interface for Generating Personally Attractive Images,
AffCom(14), No. 1, January 2023, pp. 637-649.
IEEE DOI 2303
Faces, Visualization, Generative adversarial networks, Electroencephalography, Psychology, Brain modeling, individual differences BibRef

Wang, J.R.[Jia-Rong], Bi, L.Z.[Lu-Zheng], Fei, W.J.[Wei-Jie],
Multitask-Oriented Brain-Controlled Intelligent Vehicle Based on Human-Machine Intelligence Integration,
SMCS(53), No. 4, April 2023, pp. 2510-2521.
IEEE DOI 2303
Electroencephalography, Task analysis, Decoding, Feature extraction, Ash, Turning, Electrodes, neural decoding BibRef

Bernal, S.L.[Sergio López], Celdrán, A.H.[Alberto Huertas], Pérez, G.M.[Gregorio Martínez],
Eight Reasons to Prioritize Brain-Computer Interface Cybersecurity,
CACM(66), No. 4, April 2023, pp. 68-78.
DOI Link 2303
BibRef

Schiliro, F.[Francesco], Moustafa, N.[Nour], Razzak, I.[Imran], Beheshti, A.[Amin],
DeepCog: A Trustworthy Deep Learning-Based Human Cognitive Privacy Framework in Industrial Policing,
ITS(24), No. 7, July 2023, pp. 7485-7493.
IEEE DOI 2307
Electroencephalography, Brain modeling, Privacy, Industrial Internet of Things, Data models, Data privacy, industrial policing BibRef

Kaur, M.[Manvir], Upadhyay, R.[Rahul], Kumar, V.[Vinay],
E-CNNet: Time-reassigned Multisynchrosqueezing transform-based deep learning framework for MI-BCI task classification,
IJIST(33), No. 4, 2023, pp. 1406-1423.
DOI Link 2307
brain-computer interfaces (BCIs), convolution neural network (CNN), time-reassigned Multisynchrosqueezing transform (TMSST) BibRef

Wu, L.[Le], Liu, A.[Aiping], Ward, R.K.[Rabab K.], Wang, Z.J.[Z. Jane], Chen, X.[Xun],
Signal Processing for Brain-Computer Interfaces: A review and current perspectives,
SPMag(40), No. 5, July 2023, pp. 80-91.
IEEE DOI 2307
Affective computing, Signal processing, Clinical diagnosis, Medical diagnostic imaging, Face recognition, Biomedical signal processing BibRef

Hernández-Álvarez, L.[Luis], Barbierato, E.[Elena], Caputo, S.[Stefano], de Fuentes, J.M.[José María], González-Manzano, L.[Lorena], Encinas, L.H.[Luis Hernández], Mucchi, L.[Lorenzo],
KeyEncoder: A secure and usable EEG-based cryptographic key generation mechanism,
PRL(173), 2023, pp. 1-9.
Elsevier DOI 2310
Autoencoders, Discrete wavelet transform, Electroencephalogram, Key generation, Privacy-preserving BibRef

Wu, D.R.[Dong-Rui], Lu, B.L.[Bao-Liang], Hu, B.[Bin], Zeng, Z.G.[Zhi-Gang],
Affective Brain-Computer Interfaces (aBCIs): A Tutorial,
PIEEE(111), No. 10, October 2023, pp. 1314-1332.
IEEE DOI 2310
BibRef

Chu, X.X.[Xing-Xing], Yu, Y.[Yang], Liu, K.X.[Kai-Xuan], Ye, Z.[Zeqi], Hu, D.[Dewen], Zeng, L.L.[Ling-Li],
Multi-Brain Coding Expands the Instruction Set in SSVEP-Based Brain-Computer Interfaces,
HMS(53), No. 5, October 2023, pp. 915-923.
IEEE DOI 2310
BibRef

Davis, K.M.[Keith M.], Spape, M.[Michiel], Ruotsalo, T.[Tuukka],
Contradicted by the Brain: Predicting Individual and Group Preferences via Brain-Computer Interfacing,
AffCom(14), No. 4, October 2023, pp. 3094-3105.
IEEE DOI 2312
BibRef

Wankhade, M.M.[Megha M.], Chorage, S.S.[Suvarna S.],
Optimized Neural Network with Refined Features for Categorization of Motor Imaginary Signals,
IJIG(23), No. 6 2023, pp. 2350053.
DOI Link 2312
BibRef

Kolathod, M.J.T.[Muhamed Jishad Thrikkannoor], Sanjay, M.,
Use of covariance matrix images for electroencephalography signal classification for multiclass motor imagery-based brain computer interface,
IJIST(34), No. 1, 2024, pp. e22935.
DOI Link 2401
AlexNet, BCI, brain-machine interface, CNN, covariance matrices, EEG classification, transfer learning BibRef

Shishavan, H.H.[Hossein Hamidi], Behzadi, M.M.[Mohammad Mahdi], Lohan, D.J.[Danny J.], Dede, E.M.[Ercan M.], Kim, I.[Insoo],
Closed-Loop Brain Machine Interface System for In-Vehicle Function Controls Using Head-Up Display and Deep Learning Algorithm,
ITS(25), No. 7, July 2024, pp. 6594-6603.
IEEE DOI Code:
WWW Link. 2407
Electroencephalography, Visualization, Brain modeling, Graphical user interfaces, Convolution, Calibration, steady state visual evoked potentials (SSVEP) BibRef

Huang, W.C.[Wei-Chen], Guan, Z.J.[Zi-Jing], Li, K.[Kendi], Zhou, Y.J.[Ya-Jun], Li, Y.Q.[Yuan-Qing],
An Affective Brain-Computer Interface Based on a Transfer Learning Method,
AffCom(15), No. 3, July 2024, pp. 929-941.
IEEE DOI 2409
Electroencephalography, Emotion recognition, Brain modeling, Real-time systems, Transfer learning, Task analysis, Calibration, neural pattern BibRef

Mello, C.[Chad], Weingart, T.[Troy], Rudd, E.M.[Ethan M.],
Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface,
ICPR22(1062-1068)
IEEE DOI 2212
Deep learning, Transfer learning, Data collection, Benchmark testing, Assistive technologies, Brain modeling, Brain-computer interfaces BibRef

Lee, B.H.[Byeong-Hoo], Cho, J.H.[Jeong-Hyun], Kwon, B.H.[Byoung-Hee], Lee, S.W.[Seong-Whan],
Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface,
ICPR22(1090-1097)
IEEE DOI 2212
Training, Face recognition, Feature extraction, Brain modeling, Electroencephalography, Brain-computer interfaces, Adversarial machine learning BibRef

Davis, K.M.[Keith M.], de la Torre-Ortiz, C.[Carlos], Ruotsalo, T.[Tuukka],
Brain-Supervised Image Editing,
CVPR22(18459-18468)
IEEE DOI 2210
Semantics, Supervised learning, Inspection, Generative adversarial networks, Electroencephalography, Vision + X BibRef

Ma, X.[Xin], Duan, Y.P.[Yi-Ping], Hu, S.[Shuzhan], Tao, X.M.[Xiao-Ming], Ge, N.[Ning],
EEG Based Visual Classification With Multi-Feature Joint Learning,
ICIP21(264-268)
IEEE DOI 2201
Deep learning, Visualization, Time-frequency analysis, Neuroscience, Feature extraction, Brain modeling, Joint learning BibRef

Orrú, G.[Giulia], Micheletto, M.[Marco], Terranova, F.[Fabio], Marcialis, G.L.[Gian Luca],
Electroencephalography signal processing based on textural features for monitoring the driver's state by a Brain-Computer Interface,
ICPR21(2853-2860)
IEEE DOI 2105
Time-frequency analysis, Protocols, Neural activity, Signal processing algorithms, Signal processing, Brain-computer interfaces BibRef

Aznan, N.K.N.[Nik Khadijah Nik], Atapour-Abarghouei, A.[Amir], Bonner, S.[Stephen], Connolly, J.D.[Jason D.], Breckon, T.P.[Toby P.],
Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI,
ICPR21(10418-10425)
IEEE DOI 2105
Training, Visualization, Brain modeling, Electroencephalography, Data models, Real-time systems, Calibration BibRef

Fu, H.L., Fang, P.H., Chi, C.Y., Kuo, C.T., Liu, M.H., Hsu, H.M., Hsieh, C.H., Liang, S.F., Hsieh, S., Yang, C.T.,
Application of Brain-Computer Interface and Virtual Reality in Advancing Cultural Experience,
VCIP20(351-354)
IEEE DOI 2102
brain-computer interfaces, electroencephalography, medical signal processing, virtual reality, cultural experience BibRef

Riyad, M.[Mouad], Khalil, M.[Mohammed], Adib, A.[Abdellah],
Incep-EEGNEt: A Convnet for Motor Imagery Decoding,
ICISP20(103-111).
Springer DOI 2009
BibRef

Tezza, D.[Dante], Garcia, S.[Sarah], Hossain, T.[Tamjid], Andujar, M.[Marvin],
Brain eRacing: An Exploratory Study on Virtual Brain-Controlled Drones,
VAMR19(II:150-162).
Springer DOI 1909
BibRef

Vourvopoulos, A.[Athanasios], Marin-Pardo, O.[Octavio], Neureither, M.[Meghan], Saldana, D.[David], Jahng, E.[Esther], Liew, S.L.[Sook-Lei],
Multimodal Head-Mounted Virtual-Reality Brain-Computer Interface for Stroke Rehabilitation,
VAMR19(I:165-179).
Springer DOI 1909
BibRef

Martínez-Cagigal, V.[Víctor], Santamaría-Vázquez, E.[Eduardo], Hornero, R.[Roberto],
Controlling a Smartphone with Brain-Computer Interfaces: A Preliminary Study,
AMDO18(34-43).
Springer DOI 1807
BibRef

Sharbaf, M.E., Fallah, A., Rashidi, S.,
Shrinkage estimator based common spatial pattern for multi-class motor imagery classification by hybrid classifier,
IPRIA17(26-31)
IEEE DOI 1712
brain-computer interfaces, covariance matrices, electroencephalography, feature extraction, shrinkage estimator BibRef

Liao, Q.H.[Qing-Hai], Liu, M.[Ming], Zhang, W.C.[Wen-Chong], Shi, P.[Peng],
Visual Tracking and Servoing System for Experiment of Optogenetic Control of Brain Activity,
CVS17(543-552).
Springer DOI 1711
BibRef

Camilleri, T.A.[Tracey A.], Camilleri, K.P.[Kenneth P.], Fabri, S.G.[Simon G.],
Segmentation and Labelling of EEG for Brain Computer Interfaces,
CAIP15(I:288-299).
Springer DOI 1511
BibRef

Lee, M.H.[Min-Ho], Fazli, S., Mehnert, J., Lee, S.W.[Seong-Whan],
Improving the Performance of Brain-Computer Interface Using Multi-modal Neuroimaging,
ACPR13(511-515)
IEEE DOI 1408
brain-computer interfaces BibRef

Christopher, K.R., Kapur, A., Carnegie, D.A., Grimshaw, G.M.,
Implementing 3D visualizations of EEG signals in artistic applications,
IVCNZ13(364-369)
IEEE DOI 1402
brain-computer interfaces BibRef

Ouyang, W.J.[Wen-Jia], Cashion, K., Asari, V.K.,
Electroencephelograph based brain machine interface for controlling a robotic arm,
AIPR13(1-7)
IEEE DOI 1408
biomedical electrodes BibRef

Durandau, G.[Guillaume], Suleiman, W.[Wael],
Toward a Unified Framework for EMG Signals Processing and Controlling an Exoskeleton,
CRV14(291-298)
IEEE DOI 1406
electromyography. EMG Computational modeling BibRef

Zhou, W.[Wei], Yang, Y.[Ya], Yu, Z.L.[Zhu-Liang],
Discriminative dictionary learning for EEG signal classification in Brain-computer interface,
ICARCV12(1582-1585).
IEEE DOI 1304
BibRef

Satti, A.[Abdul], Guan, C.T.[Cun-Tai], Coyle, D.[Damien], Prasad, G.[Girijesh],
A Covariate Shift Minimisation Method to Alleviate Non-stationarity Effects for an Adaptive Brain-Computer Interface,
ICPR10(105-108).
IEEE DOI 1008
BibRef

Amcalar, A.[Armagan], Cetin, M.[Mujdat],
Design, Implementation and Evaluation of a Real-Time P300-based Brain-Computer Interface System,
ICPR10(117-120).
IEEE DOI 1008
BibRef

Ang, K.K.[Kai Keng], Guan, C.T.[Cun-Tai], Lee, K.[Kerry], Lee, J.Q.[Jie Qi], Nioka, S.[Shoko], Chance, B.[Britton],
A Brain-Computer Interface for Mental Arithmetic Task from Single-Trial Near-Infrared Spectroscopy Brain Signals,
ICPR10(3764-3767).
IEEE DOI 1008
BibRef

Argunsah, A.O.[Ali Ozgur], Cetin, M.[Mujdat],
AR-PCA-HMM Approach for Sensorimotor Task Classification in EEG-based Brain-Computer Interfaces,
ICPR10(113-116).
IEEE DOI 1008
BibRef

Yazdani, A.[Ashkan], Vesin, J.M.[Jean-Marc], Izzo, D.[Dario], Ampatzis, C.[Christos], Ebrahimi, T.[Touradj],
Implicit retrieval of salient images using Brain Computer Interface,
ICIP10(3169-3172).
IEEE DOI 1009
BibRef

Lu, S.J.[Shi-Jian], Guan, C.T.[Cun-Tai], Zhang, H.H.[Hai-Hong],
Subject-independent brain computer interface through boosting,
ICPR08(1-4).
IEEE DOI 0812
EEG classification BibRef

Lotte, F.[Fabien], Mouchere, H.[Harold], Lecuyer, A.[Anatole],
Pattern rejection strategies for the design of self-paced EEG-based Brain-Computer Interfaces,
ICPR08(1-5).
IEEE DOI 0812
BibRef

Kapoor, A.[Ashish], Shenoy, P.[Pradeep], Tan, D.[Desney],
Combining brain computer interfaces with vision for object categorization,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Poli, R.[Riccardo], Salvaris, M.[Mathew], Cinel, C.[Caterina],
Evolutionary Synthesis of a Trajectory Integrator for an Analogue Brain-Computer Interface Mouse,
EvoIASP11(214-223).
Springer DOI 1104
BibRef

Poli, R.[Riccardo], Cinel, C.[Caterina], Citi, L.[Luca], Sepulveda, F.[Francisco],
Evolutionary Brain Computer Interfaces,
EvoIASP07(301-310).
Springer DOI 0704
BibRef

Millán, J.D.[José Del_R.], Ferrez, P.W.[Pierre W.], Galán, F.[Ferran], Lew, E.[Eileen], Chavarriaga, R.[Ricardo],
Non-invasive Brain-Actuated Interaction,
BVAI07(438-447).
Springer DOI 0710
BibRef

Liefhold, C.[Christian], Grosse-Wentrup, M.[Moritz], Gramann, K.[Klaus], Buss, M.[Martin],
Comparison of Adaptive Spatial Filters with Heuristic and Optimized Region of Interest for EEG Based Brain-Computer-Interfaces,
DAGM07(274-283).
Springer DOI 0709
BibRef

Song, X.M.[Xiao-Mu], Iordanescu, G.[George], Wyrwicz, A.M.[Alice M.],
One-class Machine Learning for Brain Activation Detection,
CVPR07(1-6).
IEEE DOI 0706
BibRef

Zhu, X.Y.[Xiao-Yuan], Wu, J.K.[Jian-Kang], Cheng, Y.M.[Yi-Min], Wang, Y.X.[Yi-Xiao],
GMM-Based Classification Method for Continuous Prediction in Brain-Computer Interface,
ICPR06(I: 1171-1174).
IEEE DOI 0609
BibRef

Zhang, H.H.[Hai-Hong], Guan, C.T.[Cun-Tai],
A Kernel-based Signal Localization Method for NIRS Brain-computer Interfaces,
ICPR06(I: 1158-1161).
IEEE DOI 0609
BibRef

Cheng, D.S.[Dong Seon], d'Amato, V., Murino, V.,
Wavelet-based Processing of EEG Data for Brain-Computer Interfaces,
VHCI05(III: 74-74).
IEEE DOI 0507
BibRef

Xie, H.B.[Hong-Bo], Huang, H.[Hai], Wang, Z.Z.[Zhi-Zhong],
Multiple Feature Domains Information Fusion for Computer-Aided Clinical Electromyography,
CAIP05(304).
Springer DOI 0509
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Multiple Sclerosis Detection and Analysis .