37 citations · 115 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…