Veröffentlichungen

@article{aliConTraNetHybridNetwork2023,

title = {ConTraNet: A Hybrid Network for Improving the Classification of EEG and EMG Signals with Limited Training Data},

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

url = {https://www.sciencedirect.com/science/article/pii/S0010482523011149},

doi = {10.1016/j.compbiomed.2023.107649},

issn = {0010-4825},

year  = {2023},

date = {2023-11-02},

urldate = {2023-11-02},

journal = {Computers in Biology and Medicine},

pages = {107649},

abstract = {Objective Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data. Approach In this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a 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 short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals. Main results We evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) 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, 7-class, and 10-class decoding tasks). Significance With limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.},

keywords = {BCI, Brain computer interface, Deep learning, EEG decoding, EMG decoding, Machine Learning},

pubstate = {published},

tppubtype = {article}

}

@article{ayazhussainAdvancementsUpperBody2023,

title = {Advancements in Upper Body Exoskeleton: Implementing Active Gravity Compensation with a Feedforward Controller},

author = {Muhammad Ayaz Hussain and Ioannis Iossifidis},

url = {https://doi.org/10.48550/arXiv.2309.04698},

doi = {10.48550/arXiv.2309.04698},

year  = {2023},

date = {2023-09-09},

urldate = {2023-09-09},

journal = {arXiv:2309.04698 [cs.RO]},

abstract = {In this study, we present a feedforward control system designed for active gravity compensation on an upper body exoskeleton. The system utilizes only positional data from internal motor sensors to calculate torque, employing analytical control equations based on Newton-Euler Inverse Dynamics. Compared to feedback control systems, the feedforward approach offers several advantages. It eliminates the need for external torque sensors, resulting in reduced hardware complexity and weight. Moreover, the feedforward control exhibits a more proactive response, leading to enhanced performance. The exoskeleton used in the experiments is lightweight and comprises 4 Degrees of Freedom, closely mimicking human upper body kinematics and three-dimensional range of motion. We conducted tests on both hardware and simulations of the exoskeleton, demonstrating stable performance. The system maintained its position over an extended period, exhibiting minimal friction and avoiding undesired slewing.},

keywords = {Autonomous robotics, BCI, Computer Science - Artificial Intelligence, Computer Science - Information Theory, Computer Science - Machine Learning, Exoskeleton},

pubstate = {published},

tppubtype = {article}

}

@article{schmidtConceptsMuscleActivity2023,

title = {The Concepts of Muscle Activity Generation Driven by Upper Limb Kinematics},

author = {Marie D. Schmidt and Tobias Glasmachers and Ioannis Iossifidis},

url = {https://doi.org/10.1186/s12938-023-01116-9},

doi = {10.1186/s12938-023-01116-9},

issn = {1475-925X},

year  = {2023},

date = {2023-06-24},

urldate = {2023-06-24},

journal = {BioMedical Engineering OnLine},

volume = {22},

number = {1},

pages = {63},

abstract = {The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end-effector instead), which are similarly represented in our brains. This model is motivated by the known motion planning process in the central nervous system. That process incorporates the current body state from sensory systems and previous experiences, which might be represented as pre-learned inverse dynamics that generate associated muscle activity.},

keywords = {Artificial generated signal, BCI, Electromyography (EMG), Generative model, Inertial measurement unit (IMU), Machine Learning, Motion parameters, Muscle activity, Neural networks, transfer learning, Voluntary movement},

pubstate = {published},

tppubtype = {article}

}

@article{saifurrehman2023adaptive,

title = {Adaptive SpikeDeep-Classifier: Self-organizing and self-supervised machine learning algorithm for online spike sorting},

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

doi = {10.48550/arXiv.2304.01355},

year  = {2023},

date = {2023-05-02},

urldate = {2023-05-02},

journal = {arXiv:2304.01355 [cs, math, q-bio]},

keywords = {BCI, Machine Learning, Spike Sorting},

pubstate = {published},

tppubtype = {article}

}

@article{lehmlerTransferLearningPatientSpecific2021bb,

title = {Deep transfer learning compared to subject-specific models for sEMG decoders},

author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},

editor = {{IOP Publishing},

url = {https://dx.doi.org/10.1088/1741-2552/ac9860},

doi = {10.1088/1741-2552/ac9860},

year  = {2022},

date = {2022-10-28},

urldate = {2022-10-28},

journal = {Journal of Neural Engineering},

volume = {19},

number = {5},

abstract = {{Objective. Accurate decoding of surface electromyography (sEMG) is pivotal for muscle-to-machine-interfaces and their application e.g. rehabilitation therapy. sEMG signals have high inter-subject variability, due to various factors, including skin thickness, body fat percentage, and electrode placement. Deep learning algorithms require long training time and tend to overfit if only few samples are available. In this study, we aim to investigate methods to calibrate deep learning models to a new user when only a limited amount of training data is available. Approach. Two methods are commonly used in the literature, subject-specific modeling and transfer learning. In this study, we investigate the effectiveness of transfer learning using weight initialization for recalibration of two different pretrained deep learning models on new subjects data and compare their performance to subject-specific models. We evaluate two models on three publicly available databases (non invasive adaptive prosthetics database 2–4) and compare the performance of both calibration schemes in terms of accuracy, required training data, and calibration time. Main results. On average over all settings, our transfer learning approach improves 5%-points on the pretrained models without fine-tuning, and 12%-points on the subject-specific models, while being trained for 22% fewer epochs on average. Our results indicate that transfer learning enables faster learning on fewer training samples than user-specific models. Significance. To the best of our knowledge, this is the first comparison of subject-specific modeling and transfer learning. These approaches are ubiquitously used in the field of sEMG decoding. But the lack of comparative studies until now made it difficult for scientists to assess appropriate calibration schemes. Our results guide engineers evaluating similar use cases.},

keywords = {BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning},

pubstate = {published},

tppubtype = {article}

}

@article{xavierfidencioErrorrelated,

title = {Error-Related Potentials in Reinforcement Learning-Based Brain-Machine Interfaces},

author = {Aline Xavier Fidencio and Christian Klaes and Ioannis Iossifidis},

url = {https://www.frontiersin.org/article/10.3389/fnhum.2022.806517},

doi = {https://doi.org/10.3389/fnhum.2022.806517},

year  = {2022},

date = {2022-06-24},

urldate = {2022-06-24},

journal = {Frontiers in Human Neuroscience},

volume = {16},

abstract = {The human brain has been an object of extensive investigation in different fields. While several studies have focused on understanding the neural correlates of error processing, advances in brain-machine interface systems using non-invasive techniques further enabled the use of the measured signals in different applications. The possibility of detecting these error-related potentials (ErrPs) under different experimental setups on a single-trial basis has further increased interest in their integration in closed-loop settings to improve system performance, for example, by performing error correction. Fewer works have, however, aimed at reducing future mistakes or learning. We present a review focused on the current literature using non-invasive systems that have combined the ErrPs information specifically in a reinforcement learning framework to go beyond error correction and have used these signals for learning.},

keywords = {BCI, EEG, error-related potentials, Machine Learning, Reinforcement learning},

pubstate = {published},

tppubtype = {article}

}

@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},

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{aliAnchoredSTFTGNAAExtension2021a,

title = {Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method},

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

url = {https://www.nature.com/articles/s41598-022-07992-w},

doi = {https://doi.org/10.1038/s41598-022-07992-w},

issn = {2045-2322},

year  = {2022},

date = {2022-03-10},

urldate = {2022-03-10},

journal = {Nature Scientific Reports},

volume = {12},

issue = {1},

pages = {4245},

abstract = {Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs is pivotal. Here, we propose a novel feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a novel augmentation method, called gradient norm adversarial augmentation (GNAA). 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. In addition, we also propose a new CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms all state-of-the-art methods and yields an average classification accuracy of 90.7 % and 89.54 % on BCI competition II dataset III and BCI competition IV dataset 2b, respectively.},

keywords = {Adversarial NN, BCI, computer science, EEG, Machine Learning, Quantitative Biology, Quantitative Methods},

pubstate = {published},

tppubtype = {article}

}

@article{schmidt2022motion,

title = {From Motion to Muscle},

author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},

doi = {https://doi.org/10.48550/arXiv.2201.11501},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {arXiv: 2201.11501 [cs.LG]},

keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},

pubstate = {published},

tppubtype = {article}

}

@article{aliAnchoredSTFTGNAAExtension2021,

title = {Anchored-STFT and GNAA: An Extension of STFT in Conjunction with an Adversarial Data Augmentation Technique for the Decoding of Neural Signals},

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

year  = {2021},

date = {2021-08-01},

urldate = {2021-08-01},

journal = {arXiv:2011.14694 [cs, q-bio]},

abstract = {Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs is pivotal. Here, we propose a novel feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a novel augmentation method, called gradient norm adversarial augmentation (GNAA). 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. In addition, we also propose a new CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms all state-of-the-art methods and yields an average classification accuracy of 90.7 % and 89.54 % on BCI competition II dataset III and BCI competition IV dataset 2b, respectively.},

keywords = {BCI, Machine Learning, Quantitative Biology, Quantitative Methods},

pubstate = {published},

tppubtype = {article}

}

@article{lehmler2021deep,

title = {Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification},

author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},

url = {https://api.semanticscholar.org/CorpusID:245634948},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

keywords = {BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning},

pubstate = {published},

tppubtype = {article}

}

@article{10.1088/1741-2552/abc8d4,

title = {SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm},

author = {Muhammad Saif-ur-Rehman and Omair Ali and Susanne Dyck and Robin Lienkämper and Marita Metzler and Yaroslav Parpaley and Jörg Wellmer and Charles Liu and Brian Lee and Spencer Kellis and Richard Andersen and Ioannis Iossifidis and Tobias Glasmachers and Christian Klaes},

url = {http://iopscience.iop.org/article/10.1088/1741-2552/abc8d4},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Journal of Neural Engineering},

abstract = {Objective. Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more neurons along with background activity (BA). The aim of this study is to isolate SA of each neural source. This process is called spike sorting or spike classification. Advanced spike sorting algorithms are time consuming because of the human intervention at various stages of the pipeline. Current approaches lack generalization because the values of hyperparameters are not fixed, even for multiple recording sessions of the same subject. In this study, a fully automatic spike sorting algorithm called “SpikeDeep-Classifier” is proposed. The values of hyperparameters remain fixed for all the evaluation data. Approach. The proposed approach is based on our previous study (SpikeDeeptector) and a novel background activity rejector (BAR), which are both supervised learning algorithms and an unsupervised learning algorithm (K-means). SpikeDeeptector and BAR are used to extract meaningful channels and remove BA from the extracted meaningful channels, respectively. The process of clustering becomes straight-forward once the BA is completely removed from the data. Then, K-means with a predefined maximum number of clusters is applied on the remaining data originating from neural sources only. Lastly, a similarity-based criterion and a threshold are used to keep distinct clusters and merge similar looking clusters. The proposed approach is called cluster accept or merge (CAOM) and it has only two hyperparameters (maximum number of clusters and similarity threshold) which are kept fixed for all the evaluation data after tuning. Main Results. We compared the results of our algorithm with ground-truth labels. The algorithm is evaluated on data of human patients and publicly available labeled non-human primates (NHPs) datasets. The average accuracy of BAR on datasets of human patients is 92.3% which is further reduced to 88.03% after (K-means + CAOM). In addition, the average accuracy of BAR on a publicly available labeled dataset of NHPs is 95.40% which reduces to 86.95% after (K-mean + CAOM). Lastly, we compared the performance of the SpikeDeep-Classifier with two human experts, where SpikeDeep-Classifier has produced comparable results. Significance. The results demonstrate that “SpikeDeep-Classifier” possesses the ability to generalize well on a versatile dataset and henceforth provides a generalized well on a versatile dataset and henceforth provides a generalized and fully automated solution to offline spike sorting.},

keywords = {BCI, CNN, Machine Learning, Spike Sorting},

pubstate = {published},

tppubtype = {article}

}

@article{ali2020improving,

title = {Improving the performance of EEG decoding using anchored-STFT in conjunction with gradient norm adversarial augmentation},

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

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {arXiv preprint arXiv:2011.14694},

keywords = {Adversarial NN, BCI, EEG, Machine Learning},

pubstate = {published},

tppubtype = {article}

}

@article{Saif-ur-Rehman2019,

title = {SpikeDeeptector: a deep-learning based method for detection of neural spiking activity},

author = {Muhammad Saif-ur-Rehman and Robin Lienkämper and Yaroslav Parpaley and Jörg Wellmer and Charles Liu and Brian Lee and Spencer Kellis and Richard Andersen and Ioannis Iossifidis and Tobias Glasmachers and Christian Klaes},

url = {https://iopscience.iop.org/article/10.1088/1741-2552/ab1e63/meta},

doi = {10.1088/1741-2552/ab1e63},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Journal of Neural Engineering},

volume = {16},

number = {5},

pages = {056003},

abstract = {Objective . In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which on...},

keywords = {BCI, CNN, Data Reduction, Machine Learning, Spike Sorting},

pubstate = {published},

tppubtype = {article}

}