37 citations · 85 across the 15 of their papers we have counts for
15 papers
Generative versus Discriminative Approaches for Class-Incremental Learning of EMG Signals: Effectiveness of Scale Mixture Modeling
Seitaro Yoneda, Suguru Kanoga, Akira Furui
In electromyogram (EMG)-based motion recognition, it is impractical to predefine all motions that may be required during deployment, necessitating class-incremental learning that s…
VAE-Based Synthetic EMG Generation with Mix-Consistency Loss for Recognizing Unseen Motion Combinations
Itsuki Yazawa, Akira Furui
Electromyogram (EMG)-based motion classification using machine learning has been widely employed in applications such as prosthesis control. While previous studies have explored ge…
Towards Cross-Subject EMG Pattern Recognition via Dual-Branch Adversarial Feature Disentanglement
Xinyue Niu, Akira Furui
Cross-subject electromyography (EMG) pattern recognition faces significant challenges due to inter-subject variability in muscle anatomy, electrode placement, and signal characteri…
Inter-Subject Variance Transfer Learning for EMG Pattern Classification Based on Bayesian Inference
Seitaro Yoneda, Akira Furui
In electromyogram (EMG)-based motion recognition, a subject-specific classifier is typically trained with sufficient labeled data. However, this process demands extensive data coll…
Recognition of Unseen Combined Motions via Convex Combination-based EMG Pattern Synthesis for Myoelectric Control
Itsuki Yazawa, Seitaro Yoneda, Akira Furui
Electromyogram (EMG) signals recorded from the skin surface enable intuitive control of assistive devices such as prosthetic limbs. However, in EMG-based motion recognition, collec…
EEG-Based Inter-Patient Epileptic Seizure Detection Combining Domain Adversarial Training with CNN-BiLSTM Network
Rina Tazaki, Tomoyuki Akiyama, Akira Furui
Automated epileptic seizure detection from electroencephalogram (EEG) remains challenging due to significant individual differences in EEG patterns across patients. While existing…
Classification of Carotid Plaque with Jellyfish Sign Through Convolutional and Recurrent Neural Networks Utilizing Plaque Surface Edges
Takeshi Yoshidomi, Shinji Kume, Hiroaki Aizawa +1
In carotid arteries, plaque can develop as localized elevated lesions. The Jellyfish sign, marked by fluctuating plaque surfaces with blood flow pulsation, is a dynamic characteris…
Bayesian Approach for Adaptive EMG Pattern Classification Via Semi-Supervised Sequential Learning
Seitaro Yoneda, Akira Furui
Intuitive human-machine interfaces may be developed using pattern classification to estimate executed human motions from electromyogram (EMG) signals generated during muscle contra…
Evaluating Classifier Confidence for Surface EMG Pattern Recognition
Akira Furui
Surface electromyogram (EMG) can be employed as an interface signal for various devices and software via pattern recognition. In EMG-based pattern recognition, the classifier shoul…
Automated Classification of General Movements in Infants Using a Two-stream Spatiotemporal Fusion Network
Yuki Hashimoto, Akira Furui, Koji Shimatani +4
The assessment of general movements (GMs) in infants is a useful tool in the early diagnosis of neurodevelopmental disorders. However, its evaluation in clinical practice relies on…
A Time-Series Scale Mixture Model of EEG with a Hidden Markov Structure for Epileptic Seizure Detection
Akira Furui, Tomoyuki Akiyama, Toshio Tsuji
In this paper, we propose a time-series stochastic model based on a scale mixture distribution with Markov transitions to detect epileptic seizures in electroencephalography (EEG).…
EMG Pattern Recognition via Bayesian Inference with Scale Mixture-Based Stochastic Generative Models
Akira Furui, Takuya Igaue, Toshio Tsuji
Electromyogram (EMG) has been utilized to interface signals for prosthetic hands and information devices owing to its ability to reflect human motion intentions. Although various E…
Biomimetic Control of Myoelectric Prosthetic Hand Based on a Lambda-type Muscle Model
Akira Furui, Kosuke Nakagaki, Toshio Tsuji
Myoelectric prosthetic hands are intended to replace the function of the amputee's lost arm. Therefore, developing robotic prosthetics that can mimic not only the appearance and fu…
Non-Gaussianity Detection of EEG Signals Based on a Multivariate Scale Mixture Model for Diagnosis of Epileptic Seizures
Akira Furui, Ryota Onishi, Akihito Takeuchi +2
Objective: The detection of epileptic seizures from scalp electroencephalogram (EEG) signals can facilitate early diagnosis and treatment. Previous studies suggested that the Gauss…
A Scale Mixture-Based Stochastic Model of Surface EMG Signals With Variable Variances
Akira Furui, Hideaki Hayashi, Toshio Tsuji
Objective: Surface electromyogram (EMG) signals have typically been assumed to follow a Gaussian distribution. However, the presence of non-Gaussian signals associated with muscle…