In this research, we propose spatio-spectral convolutional neural networks with relatively short segment of EEG data (0.2s) having 83.4% accuracy on gait state ...

Abstract—EEG-based BCI was recently applied to lower limb exoskeleton robots. Various machine learning decoders have shown high accuracy performance on ...

... Regarding the locomotion modes of the existing LMR methods, Park et al. [35] proposed a spatio-spectral convolutional neural networks (CNN) having 83.4% ...

EEG-based gait state and gait intention recognition using spatio-spectral convolutional neural network ... gait rehabilitation using EEG. J Choi, H Kang, SH ...

EEG-based Gait State and Intention Recognition Using Spatio-Spectral Convolutional Neural Network ... Neural Network Model based on Temporal and Spatial Feature ...

Mar 26, 2022 · EEG-based gait state and gait intention recognition using spatio-spectral convolutional neural network. ... EEG and sEMG features based gait ...

May 24, 2022 · S. Park et al., “EEG-Based Gait State and Gait Intention Recognition Using Spatio-Spectral Convolutional Neural Network”. IEEE International ...

Current literature focuses on either body recognition based on RGB images or gait recognition based on body shapes and walking patterns; both have their ...

Missing: Intention Spatio-

The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals.