Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI

Brain–computer interfaces for communication and control

Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927

Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters

Jöbsis, F. F. (1977) · Science · doi:10.1126/science.929199 · Cited by 3,899

A review of classification algorithms for EEG-based brain–computer interfaces

Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r01 · Cited by 2,665

Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general

Zander, T. O., & Kothe, C. A. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025005 · Cited by 947

Primary Motor and Sensory Cortex Activation during Motor Performance and Motor Imagery: A Functional Magnetic Resonance Imaging Study

Porro, C. A., Francescato, M. P., Cettolo, V., Diamond, M. E., Baraldi, P., Zuiani, C., Bazzocchi, M., & di Prampero, P. E. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-23-07688.1996 · Cited by 924

Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain–computer interface

Sitaram, R., Zhang, H., Guan, C., Thulasidas, M., Hoshi, Y., Ishikawa, A., Shimizu, K., & Birbaumer, N. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.11.005 · Cited by 598

Brain–computer interface using a simplified functional near-infrared spectroscopy system

Coyle, S. M., Ward, T. E., & Markham, C. M. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/3/007 · Cited by 505

On the suitability of near-infrared (NIR) systems for next-generation brain–computer interfaces

Coyle, S. M., Ward, T. E., Markham, C., & McDarby, G. (2004) · Physiological Measurement · doi:10.1088/0967-3334/25/4/003 · Cited by 361

Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain–computer interface

Naseer, N., & Hong, K. (2013) · Neuroscience Letters · doi:10.1016/j.neulet.2013.08.021 · Cited by 301

Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface

Khan, M. J., Hong, M. J., & Hong, K.-S. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00244 · Cited by 280

An online hybrid BCI combining SSVEP and EOG-based eye movements

Zhang, J., Gao, S., Zhou, K., Cheng, Y., & Mao, S. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1103935 · Cited by 26

Improved dilation CapsuleNet for motor imagery and mental arithmetic classification based on fNIRS

Li, Y., Xu, T., Li, J., Wan, F., & Wang, H. (2024) · Brain-Apparatus Communication A Journal of Bacomics · doi:10.1080/27706710.2024.2335886 · Cited by 24

Robust Adaptive Synchronization of Ring Configured Uncertain Chaotic FitzHugh–Nagumo Neurons under Direction-Dependent Coupling

Iqbal, M., Rehan, M., & Hong, K. (2018) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2018.00006 · Cited by 24

Brain-Based Binary Communication Using Spatiotemporal Features of fNIRS Responses

Nagels-Coune, L., Benitez-Andonegui, A., Reuter, N., Lührs, M., Goebel, R., Weerd, P. D., Riecke, L., & Sorger, B. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00113 · Cited by 23

Decoding Three Different Preference Levels of Consumers Using Convolutional Neural Network: A Functional Near-Infrared Spectroscopy Study

Qing, K., Huang, R., & Hong, K. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.597864 · Cited by 26

Ternary Near-Infrared Spectroscopy Brain-Computer Interface With Increased Information Transfer Rate Using Prefrontal Hemodynamic Changes During Mental Arithmetic, Breath-Holding, and Idle State

Shin, J., Kwon, J., Choi, J., & Im, C. (2018) · IEEE Access · doi:10.1109/access.2018.2822238 · Cited by 23

Artificial Immune System–Negative Selection Classification Algorithm (NSCA) for Four Class Electroencephalogram (EEG) Signals

Rashid, N., Iqbal, J., Mahmood, F., Abid, A., Khan, U. S., & Tiwana, M. I. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00439 · Cited by 22

Benchmarking framework for machine learning classification from fNIRS data

Benerradi, J., Clos, J., Landowska, A., Valstar, M., & Wilson, M. L. (2023) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2023.994969 · Cited by 23

Classification of Hemodynamic Responses Associated With Force and Speed Imagery for a Brain-Computer Interface

Yin, X., Xu, B., Jiang, C., Fu, Y., Wang, Z., Li, H., & Shi, G. (2015) · Journal of Medical Systems · doi:10.1007/s10916-015-0236-0 · Cited by 21

An Augmented-Reality fNIRS-Based Brain-Computer Interface: A Proof-of-Concept Study

Benitez-Andonegui, A., Burden, R., Benning, R., Möckel, R., Lührs, M., & Sorger, B. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00346 · Cited by 23

Driver drowsiness detection using heart rate and behavior methods: A study

Wadhwa, A., & Roy, S. S. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819314-3.00011-2 · Cited by 21

A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series

Keshmiri, S., Sumioka, H., Yamazaki, R., & Ishiguro, H. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00015 · Cited by 22

Eyes-closed hybrid brain-computer interface employing frontal brain activation

Shin, J., Müller, K., & Hwang, H.-J. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196359 · Cited by 21

A ternary classification using machine learning methods of distinct estrogen receptor activities within a large collection of environmental chemicals

Zhang, Q., Lu, Y., Wu, Y., Ji, L., Chen, Y., Zhao, M., & Dong, X. (2016) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2016.12.088 · Cited by 23

Enhancing Classification Accuracy of Transhumeral Prosthesis: A Hybrid sEMG and fNIRS Approach

Sattar, N. Y., Kausar, Z., Usama, S. A., Naseer, N., Farooq, U., Abdullah, A., Hussain, S. Z., Khan, U. S., Khan, H., & Mirtaheri, P. (2021) · IEEE Access · doi:10.1109/access.2021.3099973 · Cited by 20

SPD-CNN: A plain CNN-based model using the symmetric positive definite matrices for cross-subject EEG classification with meta-transfer-learning

Chen, L., Yu, Z., & Yang, J. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.958052 · Cited by 20

Improvement of Classification Accuracy of Four-Class Voluntary-Imagery fNIRS Signals using Convolutional Neural Networks

Milu, M. H., Rahman, M. A., Rashid, M. A., Kuwana, A., & Kobayashi, H. (2023) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.5703 · Cited by 20

Prediction of Forelimb Reach Results From Motor Cortex Activities Based on Calcium Imaging and Deep Learning

Li, C., Chan, D. C. W., Yang, X., Ke, Y., & Yung, W. (2019) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2019.00088 · Cited by 20

Early Detection of Mental Stress Using Advanced Neuroimaging and Artificial IntelligencePreprint

Al-Shargie, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.08511 · Cited by 24

Research on Emotion Recognition Method of Cerebral Blood Oxygen Signal Based on CNN-Transformer Network

Jin, Z., Xing, Z., Wang, Y., Fang, S., Gao, X., & Dong, X. (2023) · Sensors · doi:10.3390/s23208643 · Cited by 18

TAGL: Temporal-Guided Adaptive Graph Learning Network for Coordinated Movement Classification

Li, L., Zhang, M., Chen, Y., Wang, K., Zhou, G., & Huang, Q. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3423311 · Cited by 18

Channel selection in motor imaginary-based brain-computer interfaces: a particle swarm optimization algorithm

Zhang, L., & Wei, Q. (2019) · Journal of Integrative Neuroscience · doi:10.31083/j.jin.2019.02.17 · Cited by 18

Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating

Shin, J., & Im, C. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00168 · Cited by 18

Classification of electroencephalogram signals using wavelet-CSP and projection extreme learning machine

Dai, Y., Zhang, X., Chen, Z., & Xu, X. (2018) · Review of Scientific Instruments · doi:10.1063/1.5006511 · Cited by 17

Classification of Motor Imagery Events from Prefrontal Hemodynamics for BCI Application

Rahman, M. A., Haque, M. M., Anjum, A., Mollah, M. N., & Ahmad, M. (2019) · Algorithms for intelligent systems · doi:10.1007/978-981-13-7564-4_2 · Cited by 16

Optimization enabled deep residual neural network for motor imagery EEG signal classification

Kumar, T. R., Mahalaxmi, U. S. B. K., Ramakrishna, M. M., & Bhatt, D. D. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104317 · Cited by 16

IVCAN: An improved visual curve attention network for fNIRS-Based motor imagery/execution classification

Li, Y., Li, S., Yuan, Z., Zhao, S., Wan, F., Xu, T., Zhang, H., & Wang, H. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107679 · Cited by 15

Initial-dip-based classification for fNIRS-BCI

Zafar, A., Ghafoor, U., Yaqub, M. A., & Hong, K. (2019) · Neural Imaging and Sensing · doi:10.1117/12.2511595 · Cited by 15

Performance Improvement for Detecting Brain Function Using fNIRS: A Multi-Distance Probe Configuration With PPL Method

Chen, X., Song, X., Chen, L., An, X., & Ming, D. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.569508 · Cited by 15

Brain–Computer Interfaces in Acute and Subacute Disorders of Consciousness

Xu, R., Spataro, R., Allison, B. Z., & Guger, C. (2021) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0000000000000810 · Cited by 15

Classification of three categories from prefrontal cortex using LSTM networks: fNIRS study

Yoo, S.-H., Woo, S., & Amad, Z. (2018) · International Conference on Control, Automation and Systems (ICCAS) · Cited by 14

Performance Prediction for a Near-Infrared Spectroscopy-Brain–Computer Interface Using Resting-State Functional Connectivity of the Prefrontal Cortex

Shin, J., & Im, C. (2018) · International Journal of Neural Systems · doi:10.1142/s0129065718500235 · Cited by 14

Differential Entropy Preserves Variational Information of Near-Infrared Spectroscopy Time Series Associated With Working Memory

Keshmiri, S., Sumioka, H., Yamazaki, R., & Ishiguro, H. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00033 · Cited by 14

Multi-class Classification of Motor Execution Tasks using fNIRS

Shamsi, F., & Najafizadeh, L. (2019) · IEEE Signal Processing in Medicine and Biology Symposium · doi:10.1109/spmb47826.2019.9037856 · Cited by 14

Activation Modeling and Classification of Voluntary and Imagery Movements From the Prefrontal fNIRS Signals

Rahman, M. A., Rashid, M. A., Ahmad, M., Kuwana, A., & Kobayashi, H. (2020) · IEEE Access · doi:10.1109/access.2020.3042249 · Cited by 14

Wayfinding Information Cognitive Load Classification Based on Functional Near-Infrared Spectroscopy

Zhu, Q., Shi, Y., & Du, J. (2021) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000984 · Cited by 13

Functional Mapping of the Brain for Brain–Computer Interfacing: A Review

SINGH, S. P., Mishra, S., Gupta, S., Padmanabhan, P., Lu, J. X., Colin, T. K. A., Tsai, Y. T., Kejia, T., Sankarapillai, P., Mohan, A., et al. (2023) · Electronics · doi:10.3390/electronics12030604 · Cited by 14

Lie detection from fNIR signal and NeuroImage

Rahman, M. A., & Ahmad, M. (2016) · International Conference on Medical Engineering, Health Informatics and Technology (MediTec) · doi:10.1109/meditec.2016.7835382 · Cited by 14

Drowsiness Detection During a Driving Task Using fNIRS

Khan, R. A., Naseer, N., & Khan, M. J. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-811926-6.00013-0 · Cited by 14

Sensor Location Optimization of Wireless Wearable fNIRS System for Cognitive Workload Monitoring Using a Data-Driven Approach for Improved Wearability

Siddiquee, M. R., Atri, R., Marquez, J. S., Hasan, S. M. S., Ramon, R., & Bai, O. (2020) · Sensors · doi:10.3390/s20185082 · Cited by 14

Effects of Daily Stress in Mental State Classification

Park, S., & Dong, S.-Y. (2020) · IEEE Access · doi:10.1109/access.2020.3035799 · Cited by 14

Research on fNIRS Recognition Method of Upper Limb Movement Intention

Li, C., Xu, Y., He, L., Zhu, Y., Kuang, S., & Sun, L. (2021) · Electronics · doi:10.3390/electronics10111239 · Cited by 14

Movement related events classification from functional near infrared spectroscopic signal

Rahman, M. A., & Ahmad, M. (2016) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccitechn.2016.7860196 · Cited by 13

Functional Near-Infrared Spectroscopy in neurodegenerative disease: a review

Xie, L., Liu, Y., Gao, Y., & Zhou, J. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1469903 · Cited by 13

Pilot Study on Gait Classification Using fNIRS Signals

Jin, H., Li, C., & Xu, J. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/7403471 · Cited by 13

A Cognition-Related Neural Oscillation Pattern, Generated in the Prelimbic Cortex, Can Control Operant Learning in Rats

Hernández-González, S., Andreu-Sánchez, C., Martín-Pascual, M. Á., Gruart, A., & Delgado-Garcia, J. M. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3651-16.2017 · Cited by 16

Hybrid EEG-NIRS based active command generation for quadcopter movement control

Khan, M. J., Zafar, A., & Hong, K. (2016) · International Automatic Control Conference · doi:10.1109/cacs.2016.7973909 · Cited by 12

Characterizing Computer Access Using a One-Channel EEG Wireless Sensor

Molina-Cantero, A. J., Guerrero-Cubero, J., Gómez, I., Merino-Monge, M., & Silva-Silva, J. (2017) · Sensors · doi:10.3390/s17071525 · Cited by 13

Initial dip detection based on both HbO and HbR vector-based phase analysis

Zafar, A., Hong, K., & Khan, M. J. (2016) · Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) · doi:10.1109/sice.2016.7749234 · Cited by 12

Initial-dip Based Quadcopter Control: Application to fNIRS-BCI

Zafar, A., Khan, M. J., Park, J., & Hong, K. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.09.072 · Cited by 12

Sensor Fusion in Human Cyber Sensor System for Motion Artifact Removal from NIRS Signal

Siddiquee, M. R., Xue, T., Marquez, J. S., Atri, R., Ramon, R., Mayrand, R. P., Leung, C., & Bai, O. (2019) · Conference on Human System Interactions · doi:10.1109/hsi47298.2019.8942617 · Cited by 11

fNIRS Signals Classification with Ensemble Learning and Adaptive Neuro-Fuzzy Inference System

Esfahani, M. M., & Sadati, S. H. (2021) · International Conference on Signal Processing and Intelligent Systems (ICSPIS) · doi:10.1109/icspis54653.2021.9729388 · Cited by 11

A Multi-channel NIRS System for Prefrontal Mental Task Classification Employing Deep Forest Algorithm

Wen, Y., Qi, X., Cui, S., Chen, C., Chen, M., Zhao, J. P., & Wang, G. (2019) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2019.8919082 · Cited by 11

Enhancement in classification accuracy of motor imagery signals with visual aid: An fNIRS-BCI Study

Ghafoor, U., Zafar, A., Yaqub, M. A., & Hong, K. (2019) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas47443.2019.8971763 · Cited by 11

A Multichannel fNIRS System for Prefrontal Mental Task Classification with Dual-level Excitation and Deep Forest Algorithm

Chen, C., Wen, Y., Cui, S., Qi, X., Liu, Z., Zhou, L., Chen, M., Zhao, J. P., & Wang, G. (2020) · Journal of Sensors · doi:10.1155/2020/1567567 · Cited by 11

Comparison of artificial neural network and support vector machine classifications for fNIRS-based BCI

Naseer, N., Hong, K., Khan, M. J., & Bhutta, M. R. (2015) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.1109/iccas.2015.7364654 · Cited by 11

Online Recognition of the Mental States of Drivers with an fNIRS-Based Brain-Computer Interface Using Deep Neural Network

Huve, G., Takahashi, K., & Hashimoto, M. (2019) · IEEE International Conference on Mechatronics (ICM) · doi:10.1109/icmech.2019.8722936 · Cited by 11

Technical system of electroencephalography-based brain–computer interface: Advances, applications, and challenges

Yu, H., Mu, Q., Liu, C., Wang, S., & Sun, J. (2025) · Neural Regeneration Research · doi:10.4103/nrr.nrr-d-25-00217 · Cited by 11

Enhancing Cognitive Workload Classification Using Integrated LSTM Layers and CNNs for fNIRS Data Analysis

Khan, M. A., Asadi, H., Qazani, M. R. C., Arogbonlo, A., Pedrammehr, S., Anwar, A., Zhou, H., Wei, L., Bhatti, A., Oladazimi, S., et al. (2025) · Computers · doi:10.3390/computers14020073 · Cited by 13

Identification of gait imagery based on fNIRS and class-dependent sparse representation

Li, H., Gong, A., Zhao, L., Wang, F., Qian, Q., Zhou, J., & Fu, Y. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102597 · Cited by 10

Drowsiness detection using fNIRS in different time windows for a passive BCI

Khan, M. J., Liu, X., Bhutta, M. R., & Hong, K. (2016) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2016.7523628 · Cited by 10

Brain-computer interface using deep neural network and its application to mobile robot control

Huve, G., Takahashi, K., & Hashimoto, M. (2018) · IEEE International Workshop on Advanced Motion Control (AMC) · doi:10.1109/amc.2019.8371082 · Cited by 10

Classification of fNIRS Signals for Decoding Right- and Left-Arm Movement Execution Using SVM for BCI Applications

Janani, A., & Sasikala, M. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-8354-9_29 · Cited by 9

Classifying Motion Intention of Step Length and Synchronous Walking Speed by Functional Near-Infrared Spectroscopy

Zhu, Y., Li, C., Jin, H., & Sun, L. (2021) · Cyborg and Bionic Systems · doi:10.34133/2021/9821787 · Cited by 9

It takes two (seconds): decreasing encoding time for two-choice functional near-infrared spectroscopy brain–computer interface communication

Vorreuther, A., Bastian, L., Benitez-Andonegui, A., Evenblij, D., Riecke, L., Lührs, M., & Sorger, B. (2023) · Neurophotonics · doi:10.1117/1.nph.10.4.045005 · Cited by 9

An online three-class Transcranial Doppler ultrasound brain computer interface

Goyal, A., Samadani, A.-A., Guerguerian, A., & Chau, T. T. (2016) · Neuroscience Research · doi:10.1016/j.neures.2015.12.013 · Cited by 9

Effect of anodal tDCS on human prefrontal cortex observed by fNIRS

Bhutta, M. R., Woo, S.-W., Khan, M. J., & Hong, K. (2016) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2016.7523752 · Cited by 9

Online Classification of Motor Imagery Using EEG and fNIRS: A Hybrid Approach with Real Time Human-Computer Interaction

Hirsch, G., Dirodi, M., Xu, R., Reitner, P., & Guger, C. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-50726-8_30 · Cited by 9

Optical Mapping of Brain Activity Underlying Directionality and Its Modulation by Expertise in Mandarin/English Interpreting

He, Y., Hu, Y., Yang, Y., Li, D., & Hu, Y. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.649578 · Cited by 9

Classification of Event-Related Potentials with Regularized Spatiotemporal LCMV Beamforming

Kerchove, A. V. D., Libert, A., Wittevrongel, B., & Hulle, M. M. V. (2022) · Applied Sciences · doi:10.3390/app12062918 · Cited by 9

Relevant Feature Integration and Extraction for Single-Trial Motor Imagery Classification

Li, L., Xu, G., Zhang, F., Xie, J., & Li, M. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00371 · Cited by 9

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Schaeffer, M.-C., & Aksenova, T. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00540 · Cited by 9

fNIRS-Based Brain–Computer Interface Using Deep Neural Networks for Classifying the Mental State of Drivers

Huve, G., Takahashi, K., & Hashimoto, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01424-7_35 · Cited by 9

Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer

Zhao, D., Yang, J., Bai, D., Okoye, M. O., & Hiroshi, Y. (2020) · Electronics · doi:10.3390/electronics9040594 · Cited by 8

Cross-Subject fNIRS Signals Channel-Selection based on Multi-Objective NSGA-II Algorithm

Esfahani, M. M., & Sadati, S. H. (2021) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme54433.2021.9750364 · Cited by 8

Drone Control Using Functional Near-Infrared Spectroscopy

Zafar, A., Ghafoor, U., Khan, M. J., & Hong, K. (2018) · International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) · doi:10.1109/ecticon.2018.8619893 · Cited by 8

Decoding of Walking Imagery and Idle State Using Sparse Representation Based on fNIRS

Li, H., Gong, A., Zhao, L., Zhang, W., Wang, F., & Fu, Y. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/6614112 · Cited by 8

Application of Deep Learning Techniques to Diagnose Mild Cognitive Impairment: Functional Near-Infrared Spectroscopy Study

Kang, M., & Hong, K. (2021) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas52745.2021.9650036 · Cited by 7

Matching pursuit-based analysis of fNIRS in combination with cascade PCA and reliefF for mental task recognition

Goshvarpour, A., & Goshvarpour, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119283 · Cited by 7

Multi-class fNIRS Classification of Motor Execution Tasks with Application to Brain-Computer Interfaces

Shamsi, F., & Najafizadeh, L. (2021) · Springer eBooks · doi:10.1007/978-3-030-67494-6_1 · Cited by 7

Functional Near-Infrared Imaging for Biomedical Applications

Miao, Y., & Radamson, H. H. (2024) · IntechOpen eBooks · doi:10.5772/intechopen.1006636 · Cited by 6

A multiple linear regression model approach for two-class fNIR data classification

Galib, S. M. S., Islam, S. M. R., & Rahman, M. A. (2020) · Iran Journal of Computer Science · doi:10.1007/s42044-020-00064-0 · Cited by 6

Recognition of Flexion and Extension Imagery Involving the Right and Left Arms Based on Deep Belief Network and Functional Near-Infrared Spectroscopy

Fu, Y., Chen, R., Gong, A., Qian, Q., Ding, N., Zhang, W., Su, L., & Zhao, L. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/5533565 · Cited by 6

Drowsiness detection in dorsolateral-prefrontal cortex using fNIRS for a passive-BCI

Khan, M. J., Hong, K., Naseer, N., & Bhutta, M. R. (2015) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.1109/iccas.2015.7364653 · Cited by 6

Shining Light on the Human Brain: An Optical BCI for Communicating with Patients with Brain Injuries

Abdalmalak, A., Laforge, G., Yip, L. C. M., Milej, D. F., González-Lara, L. E., Anazodo, U., Owen, A. M., & Lawrence, K. S. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9283123 · Cited by 6

Crossing time windows optimization based on mutual information for hybrid BCI

Meng, M., Dai, L., She, Q., Ma, Y., & Kong, W. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2021392 · Cited by 6

Classification of ADHD Using fNIRS Signals Based on Functional Connectivity and Interval Features

Wang, J., Liao, W., & Jin, X. (2021) · International Conference on Computational Intelligence and Applications (ICCIA) · doi:10.1109/iccia52886.2021.00029 · Cited by 6

A functional near-infrared spectroscopy investigation of item-method directed forgetting

Jing, J., Qi, M., & Gao, H. (2022) · Neuroscience Research · doi:10.1016/j.neures.2022.09.001 · Cited by 4

Classification of Tactile and Motor Velocity-Evoked Hemodynamic Response in Primary Somatosensory and Motor Cortices as Measured by Functional Near-Infrared Spectroscopy

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