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Found 10 papers, 0 papers with code
Ruhr Hand Motion Catalog of Human Center-Out Transport Trajectories in 3D Task-Space Captured by a Redundant Measurement System
We introduce a systematic dataset of 3D center-out task-space trajectories of human hand transport movements in a natural setting.
Advancements in Upper Body Exoskeleton: Implementing Active Gravity Compensation with a Feedforward Controller
In this study, we present a feedforward control system designed for active gravity compensation on an upper body exoskeleton.
Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes
Our approach models activation patterns of thresholded nodes in (deep) artificial neural networks as stochastic processes.
Adaptive SpikeDeep-Classifier: Self-organizing and self-supervised machine learning algorithm for online spike sorting
The proposed algorithm is the first spike sorting algorithm that automatically learns the abrupt changes in the distribution of noise and SA.
Invariance to Quantile Selection in Distributional Continuous Control
In recent years distributional reinforcement learning has produced many state of the art results.
ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces
Approach: In this work, we introduce a single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that is equally useful for EEG-HMI and EMG-HMI paradigms.
From Motion to Muscle
Voluntary human motion is the product of muscle activity that results from upstream motion planning of the motor cortical areas.
Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification
In this study, we investigate the effectiveness of transfer learning using weight initialization for recalibration of two different pretrained deep learning models on a new subjects data, and compare their performance to subject-specific models.
Anchored-STFT and GNAA: An extension of STFT in conjunction with an adversarial data augmentation technique for the decoding of neural signals
GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier.
SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm
We proposed a novel spike sorting pipeline, based on a set of supervised and unsupervised learning algorithms.
