activity
20212023
most citedUser Training with Error Augmentation for Electromyogram-based Gesture Classification

11 citations · 14 across the 6 of their papers we have counts for

collaborators

6 papers

cs.HC2023★ 1 cited

Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder

Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger +5

We study the task of gesture recognition from electromyography (EMG), with the goal of enabling expressive human-computer interaction at high accuracy, while minimizing the time re…

cs.LG2023★ 1 cited

Stabilizing Subject Transfer in EEG Classification with Divergence Estimation

Niklas Smedemark-Margulies, Ye Wang, Toshiaki Koike-Akino +4

Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test sub jects. We reduce this performance decrease using ne…

eess.SP2023

A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition

Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger +5

Objective: The objective of the study is to efficiently increase the expressivity of surface electromyography-based (sEMG) gesture recognition systems. Approach: We use a problem t…

cs.HC2023★ 11 cited

User Training with Error Augmentation for Electromyogram-based Gesture Classification

Yunus Bicer, Niklas Smedemark-Margulies, Basak Celik +7

We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wrist-band configuratio…

eess.SP2022

Recursive Estimation of User Intent from Noninvasive Electroencephalography using Discriminative Models

Niklas Smedemark-Margulies, Basak Celik, Tales Imbiriba +2

We study the problem of inferring user intent from noninvasive electroencephalography (EEG) to restore communication for people with severe speech and physical impairments (SSPI).…

cs.LG2021★ 1 cited

AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data

Niklas Smedemark-Margulies, Ye Wang, Toshiaki Koike-Akino +1

We provide a regularization framework for subject transfer learning in which we seek to train an encoder and classifier to minimize classification loss, subject to a penalty measur…