Publications

@article{aliConTraNetSingleEndtoend2022,

title = {ConTraNet: A Single End-to-End Hybrid Network for EEG-based and EMG-based Human Machine Interfaces},

author = {Omair Ali and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

url = {http://arxiv.org/abs/2206.10677},

doi = {10.48550/arXiv.2206.10677},

year  = {2022},

date = {2022-06-21},

urldate = {2022-06-21},

journal = {arXiv:2206.10677 [q-bio.NC]},

abstract = {Objective: Electroencephalography (EEG) and electromyography (EMG) are two non-invasive bio-signals, which are widely used in human machine interface (HMI) technologies (EEG-HMI and EMG-HMI paradigm) for the rehabilitation of physically disabled people. Successful decoding of EEG and EMG signals into respective control command is a pivotal step in the rehabilitation process. Recently, several Convolutional neural networks (CNNs) based architectures are proposed that directly map the raw time-series signal into decision space and the process of meaningful features extraction and classification are performed simultaneously. However, these networks are tailored to the learn the expected characteristics of the given bio-signal and are limited to single paradigm. In this work, we addressed the question that can we build a single architecture which is able to learn distinct features from different HMI paradigms and still successfully classify them. 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. ConTraNet uses CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the long-range dependencies in the signal, which are crucial for the classification of EEG and EMG signals. Main results: We evaluated and compared the ConTraNet with state-of-the-art methods on three publicly available datasets which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, and 10-class decoding tasks). Significance: The results suggest that ConTraNet is robust to learn distinct features from different HMI paradigms and generalizes well as compared to the current state of the art algorithms.},

keywords = {BCI, Machine Learning, neural processing, signal processing},

pubstate = {published},

tppubtype = {article}

}

@article{aliConTraNetSingleEndtoend2022b,

title = {ConTraNet: A Single End-to-End Hybrid Network for EEG-based and EMG-based Human Machine Interfaces},

author = {Omair Ali and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

url = {http://arxiv.org/abs/2206.10677},

doi = {10.48550/arXiv.2206.10677},

year  = {2022},

date = {2022-06-21},

urldate = {2022-06-21},

abstract = {Objective: Electroencephalography (EEG) and electromyography (EMG) are two non-invasive bio-signals, which are widely used in human machine interface (HMI) technologies (EEG-HMI and EMG-HMI paradigm) for the rehabilitation of physically disabled people. Successful decoding of EEG and EMG signals into respective control command is a pivotal step in the rehabilitation process. Recently, several Convolutional neural networks (CNNs) based architectures are proposed that directly map the raw time-series signal into decision space and the process of meaningful features extraction and classification are performed simultaneously. However, these networks are tailored to the learn the expected characteristics of the given bio-signal and are limited to single paradigm. In this work, we addressed the question that can we build a single architecture which is able to learn distinct features from different HMI paradigms and still successfully classify them. 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. ConTraNet uses CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the long-range dependencies in the signal, which are crucial for the classification of EEG and EMG signals. Main results: We evaluated and compared the ConTraNet with state-of-the-art methods on three publicly available datasets which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, and 10-class decoding tasks). Significance: The results suggest that ConTraNet is robust to learn distinct features from different HMI paradigms and generalizes well as compared to the current state of the art algorithms.},

keywords = {BCI, Machine Learning, neural processing, signal processing},

pubstate = {published},

tppubtype = {article}

}

@article{Zibner2011,

title = {Dynamic neural fields as building blocks of a cortex-inspired architecture for robotic scene representation},

author = {S K U Zibner and C Faubel and I Iossifidis and G Schöner},

doi = {10.1109/TAMD.2011.2109714},

issn = {19430604},

year  = {2011},

date = {2011-01-01},

journal = {IEEE Transactions on Autonomous Mental Development},

volume = {3},

number = {1},

abstract = {Based on the concepts of dynamic field theory (DFT), we present an architecture that autonomously generates scene representations by controlling gaze and attention, creating visual objects in the foreground, tracking objects, reading them into working memory, and taking into account their visibility. At the core of this architecture are three-dimensional dynamic neural fields (DNFs) that link feature to spatial information. These three-dimensional fields couple into lower dimensional fields, which provide the links to the sensory surface and to the motor systems. We discuss how DNFs can be used as building blocks for cognitive architectures, characterize the critical bifurcations in DNFs, as well as the possible coupling structures among DNFs. In a series of robotic experiments, we demonstrate how the DNF architecture provides the core functionalities of a scene representation. textcopyright 2011 IEEE.},

keywords = {dynamic field theory (DFT), dynamical systems, embodied cognition, neural processing, Robotics},

pubstate = {published},

tppubtype = {article}

}

@article{Zibner2011b,

title = {Dynamic neural fields as building blocks of a cortex-inspired architecture for robotic scene representation},

author = {S K U Zibner and Christian Faubel and Ioannis Iossifidis and G Schöner},

doi = {10.1109/TAMD.2011.2109714},

issn = {19430604},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

journal = {IEEE Transactions on Autonomous Mental Development},

volume = {3},

number = {1},

abstract = {Based on the concepts of dynamic field theory (DFT), we present an architecture that autonomously generates scene representations by controlling gaze and attention, creating visual objects in the foreground, tracking objects, reading them into working memory, and taking into account their visibility. At the core of this architecture are three-dimensional dynamic neural fields (DNFs) that link feature to spatial information. These three-dimensional fields couple into lower dimensional fields, which provide the links to the sensory surface and to the motor systems. We discuss how DNFs can be used as building blocks for cognitive architectures, characterize the critical bifurcations in DNFs, as well as the possible coupling structures among DNFs. In a series of robotic experiments, we demonstrate how the DNF architecture provides the core functionalities of a scene representation. textcopyright 2011 IEEE.},

keywords = {dynamic field theory (DFT), dynamical systems, embodied cognition, neural processing, Robotics},

pubstate = {published},

tppubtype = {article}

}