Proposed blink artifacts rejection method mixing with electroencephalographic recordings using single-channel electroencephalographic recordings and non-negative matrix factorization. - Suguru Kanoga; Yasue Mitsukura (AMC, 2014)

ICA-based positive semidefinite matrix templates for eye-blink artifact removal from EEG signal with single-electrode. - Suguru Kanoga; Yasue Mitsukura (ASCC, 2015)

Eye blink artifact rejection in single-channel electroencephalographic signals by complete ensemble empirical mode decomposition and independent component analysis. - Suguru Kanoga; Yasue Mitsukura (EMBC, 2015)

Assessing the effects of voluntary and involuntary eyeblinks in independent components of electroencephalogram. - Suguru Kanoga; Masaki Nakanishi; Yasue Mitsukura (Neurocomputing, 2016)

Exploring optimal myoelectric feature indices for forearm control strategy using robust principal component analysis. - Suguru Kanoga; Akihiko Murai; Mitsunori Tada (EMBC, 2017)

Transfer Learning Over Time and Position in Wearable Myoelectric Control Systems. - Suguru Kanoga; Masashi Matsuokal; Atsunori Kanemura (EMBC, 2018)

Semi-simulation Experiments for Quantifying the Performance of SSVEP-based BCI after Reducing Artifacts from Trapezius Muscles. - Suguru Kanoga; Masaki Nakanishi; Akihiko Murai; Mitsunori Tada; Atsunori Kanemura (EMBC, 2018)

A Comparative Study of Features and Classifiers in Single-Channel EEG-Based Motor Imagery BCI. - Suguru Kanoga; Atsunori Kanemura; Hideki Asoh (GlobalSIP, 2018)

Assessing the effect of transfer learning on myoelectric control systems with three electrode positions. - Suguru Kanoga; Atsunori Kanemura (ICIT, 2018)

A No-Reference Metric of Cerebral Blood Flow Extraction for fNIRS Data. - Takayuki Hoshino; Suguru Kanoga; Atsunori Kanemura; Takeshi Ogawa (SMC, 2018)

Multi-scale dictionary learning for ocular artifact reduction from single-channel electroencephalograms. - Suguru Kanoga; Atsunori Kanemura; Hideki Asoh (Neurocomputing, 2019)

Corrigendum to "Multi-scale dictionary learning for ocular artifact reduction from single-channel electroencephalograms" [Neurocomputing 347 (2019) 240-250]. - Suguru Kanoga; Atsunori Kanemura; Hideki Asoh (Neurocomputing, 2019)

Deep Neural Networks for Grid-Based Elusive Crime Prediction Using a Private Dataset Obtained from Japanese Municipalities. - Suguru Kanoga; Naruki Kawai; Kota Takaoka (AHFE (13), 2020)

Analysis and Usage: Subject-to-subject Linear Domain Adaptation in sEMG Classification. - Takayuki Hoshino; Suguru Kanoga; Masashi Tsubaki; Atsushi Aoyama (EMBC, 2020)

Motor Imagery Classification of Finger Motions Using Multiclass CSP. - Masaki Kato; Suguru Kanoga; Takayuki Hoshino; Tadanori Fukami (EMBC, 2020)

Subject Transfer Framework Based on Source Selection and Semi-Supervised Style Transfer Mapping for Semg Pattern Recognition. - Suguru Kanoga; Takayuki Hoshino; Hideki Asoh (ICASSP, 2020)

Are armband sEMG devices dense enough for long-term use? - Sensor placement shifts cause significant reduction in recognition accuracy. - Suguru Kanoga; Atsunori Kanemura; Hideki Asoh (Biomed. Signal Process. Control., 2020)

Time-domain Mixup Source Data Augmentation of sEMGs for Motion Recognition towards Efficient Style Transfer Mapping. - Suguru Kanoga; Tomoumi Takase; Takayuki Hoshino; Hideki Asoh (EMBC, 2021)

Semi-Supervised Learning for Auditory Event-Related Potential-Based Brain-Computer Interface. - Mikito Ogino; Suguru Kanoga; Shin-Ichi Ito; Yasue Mitsukura (IEEE Access, 2021)

Semi-supervised style transfer mapping-based framework for sEMG-based pattern recognition with 1- or 2-DoF forearm motions. - Suguru Kanoga; Takayuki Hoshino; Hideki Asoh (Biomed. Signal Process. Control., 2021)

Sequential Learning on sEMGs in Short- and Long-term Situations via Self-training Semi-supervised Support Vector Machine. - Yuto Okawa; Suguru Kanoga; Takayuki Hoshino; Tohru Nitta (EMBC, 2022)

A Style Transfer Mapping and Fine-Tuning Subject Transfer Framework Using Convolutional Neural Networks for Surface Electromyogram Pattern Recognition. - Suguru Kanoga; Takayuki Hoshino; Mitsunori Tada (ICASSP, 2022)

Subject-transfer framework with unlabeled data based on multiple distance measures for surface electromyogram pattern recognition. - Suguru Kanoga; Takayuki Hoshino; Hideki Asoh (Biomed. Signal Process. Control., 2022)

Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers. - Takayuki Hoshino; Suguru Kanoga; Masashi Tsubaki; Atsushi Aoyama (Neurocomputing, 2022)

The Effect of Muscle Artifact Reduction Methods on Few-channel SSVEPs during Head Movements. - Norihisa Namura; Suguru Kanoga (EMBC, 2023)

Estimating Whole-Body Walking Motion from Inertial Measurement Units at Wrist and Heels Using Deep Learning. - Yuji Kumano; Suguru Kanoga; Masataka Yamamoto; Hiroshi Takemura; Mitsunori Tada (Int. J. Autom. Technol., 2023)

Comparison of seven shallow and deep regressors in continuous blood pressure and heart rate estimation using single-channel photoplethysmograms under three evaluation cases. - Suguru Kanoga; Takayuki Hoshino; Satoshi Kamei; Takehito Kobayashi; Tsuguo Ohmori; Masaaki Uchiyama; Mitsunori Tada (Biomed. Signal Process. Control., 2023)

Nonlinear Dynamical System Identification Under External Disturbances by Maximum a Posteriori (MAP) Estimation for Robotics. - Daisuke Yanabe; Suguru Kanoga; Vincent Frémont; Toshiyuki Murakami (AMC, 2024)

Channel- and Label-Flip Data Augmentation for Motor Imagery-Based Brain-Computer Interfaces. - Takayuki Hoshino; Suguru Kanoga; Atsushi Aoyama (EMBC, 2024)

Deep Generative Replay-based Class-incremental Continual Learning in sEMG-based Pattern Recognition. - Suguru Kanoga; Ryo Karakida; Takayuki Hoshino; Yuto Okawa; Mitsunori Tada (EMBC, 2024)

A Recurrent Deep Learning-Based Monthly Deforestation Prediction Model in Eight Areas for Brazilian Amazon: A Pilot Study. - Suguru Kanoga; Takeshi Nomaguchi; Yusuke Kobayashi; Takayuki Hoshino; Chiaki Tsutsumi; Ryu Sugimoto; Toru Kouyama; Ryosuke Nakamura (IGARSS, 2024)

Evaluating the Impact of Replay-Based Continual Learning on Long-Term sEMG Pattern Recognition in Instance-Incremental Learning. - Yuto Okawa; Suguru Kanoga; Takayuki Hoshino; Shin-nosuke Ishikawa (IEEE Access, 2024)

Comparison of fine-tuned single-source and multi-source approaches to surface electromyogram pattern recognition. - Takayuki Hoshino; Suguru Kanoga; Masashi Tsubaki; Atsushi Aoyama (Biomed. Signal Process. Control., 2024)

Unsupervised Neural Motion Retargeting for Humanoid Teleoperation. - Satoshi Yagi; Mitsunori Tada; Eiji Uchibe; Suguru Kanoga; Takamitsu Matsubara; Jun Morimoto (CoRR, 2024)

Comparison of Nine Deep Regressors in Continuous Blood Pressure Estimation Using Single-Channel Photoplethysmograms under the PulseDB. - Takumi Yamamoto; Suguru Kanoga; Yuta Sugiura (SII, 2025)

Continual learning in inertial measurement unit-based human activity recognition with user-centric class-incremental learning scenario. - Suguru Kanoga; Yuya Tsukiji; Ryo Karakida (Expert Syst. Appl., 2025)