33 citations · 78 across the 5 of their papers we have counts for
6 papers
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…
A Neural Network Based on the Johnson Translation System and Related Application to Electromyogram Classification
Hideaki Hayashi, Taro Shibanoki, Toshio Tsuji
Electromyogram (EMG) classification is a key technique in EMG-based control systems. The existing EMG classification methods do not consider the characteristics of EMG features tha…
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis
Hideaki Hayashi, Taro Shibanoki, Keisuke Shima +2
This paper proposes a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-…