A5028997194- EEG and Brain-Computer Interfaces
- Muscle activation and electromyography studies
- Stroke Rehabilitation and Recovery
- Neuroscience and Neural Engineering
- Neural dynamics and brain function
- ECG Monitoring and Analysis
- Prosthetics and Rehabilitation Robotics
- Human-Automation Interaction and Safety
- Gaze Tracking and Assistive Technology
- Quality and Safety in Healthcare
- Control Systems and Identification
- Assistive Technology in Communication and Mobility
- Motor Control and Adaptation
- Nerve Injury and Rehabilitation
- Occupational Health and Safety Research
- Neural Networks and Applications
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
- Ergonomics and Human Factors
- Healthcare Technology and Patient Monitoring
- Sparse and Compressive Sensing Techniques
- Cerebral Palsy and Movement Disorders
- Fault Detection and Control Systems
German Research Centre for Artificial Intelligence
2014-2024
University of Bremen
2012-2014
The rehabilitation of patients should not only be limited to the first phases during intense hospital care but also support and therapy guaranteed in later stages, especially daily life activities if patient’s state requires this. However, aid given patient needed as much it is required. To allow this, automatic self-initiated movement patient-cooperative control strategies have developed integrated into assistive systems. In this work, we give an overview different kinds neuromuscular...
Assistive devices, like exoskeletons or orthoses, often make use of physiological data that allow the detection prediction movement onset. Movement onset can be detected at executing site, skeletal muscles, as by means electromyography. intention analysis brain activity, recorded by, e.g., electroencephalography, in behavior subject eye analysis. These different approaches used depending on kind neuromuscular disorder, state therapy assistive device. In this work we conducted experiments...
A current trend in the development of assistive devices for rehabilitation, example exoskeletons or active orthoses, is to utilize physiological data enhance their functionality and usability, by predicting patient’s upcoming movements using electroencephalography (EEG) electromyography (EMG). However, these modalities have different temporal properties classification accuracies, which results specific advantages disadvantages. To use analysis rehabilitation devices, processing should be...
The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied better future interaction between human and machine. infer upcoming context-based behavior relevant brain states the have detected. This achieved by reading (BR), a passive approach for single trial EEG analysis that makes use supervised machine learning (ML) methods. In this work we propose BR able detect concrete...
Advanced man-machine interfaces (MMIs) are being developed for teleoperating robots at remote and hardly accessible places. Such MMIs make use of a virtual environment can therefore the operator immerse him-/herself into robot. In this paper, we present our MMI multi-robot control. Our adapt to changes in task load engagement online. Applying approach embedded Brain Reading improve user support efficiency interaction. The level was inferred from single-trial detectability P300-related brain...
DATA REPORT article Front. Hum. Neurosci., 22 January 2024Sec. Brain-Computer Interfaces Volume 18 - 2024 | https://doi.org/10.3389/fnhum.2024.1304311
A relevant issue of neuro-interfacing wearable robots in rehabilitation is the necessity to have training data, since collection sufficient data from patients within a reasonable recording time not always possible. However, use historic (e.g., session-to-session transfer, subject-to-subject transfer) can often lead reduction classification performance which affected by selection (i.e., was chosen for transfer). In this paper, we analyze two approaches handle reduction. First, used...
Prediction of voluntary movements from electroencephalographic (EEG) signals is widely used and investigated for applications like brain-computer interfaces (BCIs) or in the field rehabilitation. Different combinations of signal processing machine learning methods can be found literature for solving this task. Machine algorithms suffer small signal-to-noise ratios non-stationarity EEG signals. Due to non-stationarity, prediction performance a fixed classifier may degrade over...
This paper presents a dataset containing recordings of the electroencephalogram (EEG) and electromyogram (EMG) from eight subjects who were assisted in moving their right arm by an active orthosis device. The supported movements elbow joint movements, i.e., flexion extension arm. While was actively subject's arm, some errors deliberately introduced for short duration time. During this time, moved opposite direction. In paper, we explain experimental setup present behavioral analyses across...