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Suguru Kanoga
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2020 – today
- 2024
- [j11]Takayuki Hoshino
, Suguru Kanoga
Comparison of fine-tuned single-source and multi-source approaches to surface electromyogram pattern recognition. Biomed. Signal Process. Control. 94: 106261 (2024) - [c19]Daisuke Yanabe, Suguru Kanoga, Vincent Frémont, Toshiyuki Murakami:
Nonlinear Dynamical System Identification Under External Disturbances by Maximum a Posteriori (MAP) Estimation for Robotics. AMC 2024: 1-5 - [c18]Suguru Kanoga, Takeshi Nomaguchi, Yusuke Kobayashi, Takayuki Hoshino, Chiaki Tsutsumi, Ryu Sugimoto, Toru Kouyama, Ryosuke Nakamura:
A Recurrent Deep Learning-Based Monthly Deforestation Prediction Model in Eight Areas for Brazilian Amazon: A Pilot Study. IGARSS 2024: 5208-5212 - [i1]Satoshi Yagi, Mitsunori Tada, Eiji Uchibe, Suguru Kanoga, Takamitsu Matsubara, Jun Morimoto:
Unsupervised Neural Motion Retargeting for Humanoid Teleoperation. CoRR abs/2406.00727 (2024) - 2023
- [j10]Suguru Kanoga
Comparison of seven shallow and deep regressors in continuous blood pressure and heart rate estimation using single-channel photoplethysmograms under three evaluation cases. Biomed. Signal Process. Control. 85: 105029 (2023) - [j9]Yuji Kumano, Suguru Kanoga
Estimating Whole-Body Walking Motion from Inertial Measurement Units at Wrist and Heels Using Deep Learning. Int. J. Autom. Technol. 17(3): 217-225 (2023) - [c17]Norihisa Namura
The Effect of Muscle Artifact Reduction Methods on Few-channel SSVEPs during Head Movements. EMBC 2023: 1-4 - 2022
- [j8]Suguru Kanoga
Subject-transfer framework with unlabeled data based on multiple distance measures for surface electromyogram pattern recognition. Biomed. Signal Process. Control. 74: 103522 (2022) - [j7]Takayuki Hoshino, Suguru Kanoga
Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers. Neurocomputing 489: 599-612 (2022) - [c16]Yuto Okawa, Suguru Kanoga
Sequential Learning on sEMGs in Short- and Long-term Situations via Self-training Semi-supervised Support Vector Machine. EMBC 2022: 3183-3186 - [c15]Suguru Kanoga
A Style Transfer Mapping and Fine-Tuning Subject Transfer Framework Using Convolutional Neural Networks for Surface Electromyogram Pattern Recognition. ICASSP 2022: 1171-1175 - 2021
- [j6]Mikito Ogino
Semi-Supervised Learning for Auditory Event-Related Potential-Based Brain-Computer Interface. IEEE Access 9: 47008-47023 (2021) - [j5]Suguru Kanoga
Semi-supervised style transfer mapping-based framework for sEMG-based pattern recognition with 1- or 2-DoF forearm motions. Biomed. Signal Process. Control. 68: 102817 (2021) - [c14]Suguru Kanoga
Time-domain Mixup Source Data Augmentation of sEMGs for Motion Recognition towards Efficient Style Transfer Mapping. EMBC 2021: 35-38 - 2020
- [j4]Suguru Kanoga
Are armband sEMG devices dense enough for long-term use? - Sensor placement shifts cause significant reduction in recognition accuracy. Biomed. Signal Process. Control. 60: 101981 (2020) - [c13]Suguru Kanoga
Deep Neural Networks for Grid-Based Elusive Crime Prediction Using a Private Dataset Obtained from Japanese Municipalities. AHFE (13) 2020: 105-112 - [c12]Takayuki Hoshino, Suguru Kanoga
Analysis and Usage: Subject-to-subject Linear Domain Adaptation in sEMG Classification. EMBC 2020: 674-677 - [c11]Masaki Kato, Suguru Kanoga
Motor Imagery Classification of Finger Motions Using Multiclass CSP. EMBC 2020: 2991-2994 - [c10]Suguru Kanoga
Subject Transfer Framework Based on Source Selection and Semi-Supervised Style Transfer Mapping for Semg Pattern Recognition. ICASSP 2020: 1349-1353
2010 – 2019
- 2019
- [j3]Suguru Kanoga
Multi-scale dictionary learning for ocular artifact reduction from single-channel electroencephalograms. Neurocomputing 347: 240-250 (2019) - [j2]Suguru Kanoga, Atsunori Kanemura
Corrigendum to "Multi-scale dictionary learning for ocular artifact reduction from single-channel electroencephalograms" [Neurocomputing 347 (2019) 240-250]. Neurocomputing 359: 526 (2019) - 2018
- [c9]Suguru Kanoga
Transfer Learning Over Time and Position in Wearable Myoelectric Control Systems. EMBC 2018: 1-4 - [c8]Suguru Kanoga
Semi-simulation Experiments for Quantifying the Performance of SSVEP-based BCI after Reducing Artifacts from Trapezius Muscles. EMBC 2018: 4824-4827 - [c7]Suguru Kanoga
A Comparative Study of Features and Classifiers in Single-Channel EEG-Based Motor Imagery BCI. GlobalSIP 2018: 474-478 - [c6]Suguru Kanoga
Assessing the effect of transfer learning on myoelectric control systems with three electrode positions. ICIT 2018: 1478-1483 - [c5]Takayuki Hoshino, Suguru Kanoga
A No-Reference Metric of Cerebral Blood Flow Extraction for fNIRS Data. SMC 2018: 83-89 - 2017
- [c4]Suguru Kanoga
Exploring optimal myoelectric feature indices for forearm control strategy using robust principal component analysis. EMBC 2017: 1796-1799 - 2016
- [j1]Suguru Kanoga
Assessing the effects of voluntary and involuntary eyeblinks in independent components of electroencephalogram. Neurocomputing 193: 20-32 (2016) - 2015
- [c3]Suguru Kanoga
ICA-based positive semidefinite matrix templates for eye-blink artifact removal from EEG signal with single-electrode. ASCC 2015: 1-6 - [c2]Suguru Kanoga
Eye blink artifact rejection in single-channel electroencephalographic signals by complete ensemble empirical mode decomposition and independent component analysis. EMBC 2015: 121-124 - 2014
- [c1]Suguru Kanoga
Proposed blink artifacts rejection method mixing with electroencephalographic recordings using single-channel electroencephalographic recordings and non-negative matrix factorization. AMC 2014: 536-541
Coauthor Index
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last updated on 2024-12-10 21:48 CET by the dblp team
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