6 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…