13 citations · 32 across the 8 of their papers we have counts for
8 papers
MarkovType: A Markov Decision Process Strategy for Non-Invasive Brain-Computer Interfaces Typing Systems
Elifnur Sunger, Yunus Bicer, Deniz Erdogmus +1
Brain-Computer Interfaces (BCIs) help people with severe speech and motor disabilities communicate and interact with their environment using neural activity. This work focuses on t…
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…
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…
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…
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…
Efficient Modeling of Morphing Wing Flight Using Neural Networks and Cubature Rules
Paul Ghanem, Yunus Bicer, Deniz Erdogmus +1
Fluidic locomotion of flapping Micro Aerial Vehicles (MAVs) can be very complex, particularly when the rules from insect flight dynamics (fast flapping dynamics and light wings) ar…
Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation
Yunus Bicer, Ali Alizadeh, Nazim Kemal Ure +2
The objective of this paper is to develop a sample efficient end-to-end deep learning method for self-driving cars, where we attempt to increase the value of the information extrac…
Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment
Ali Alizadeh, Majid Moghadam, Yunus Bicer +3
Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the tra…