A5063196354- EEG and Brain-Computer Interfaces
- Neural dynamics and brain function
- Neuroscience and Neural Engineering
- Advanced Memory and Neural Computing
- Gaze Tracking and Assistive Technology
- Muscle activation and electromyography studies
Universitätsklinikum Knappschaftskrankenhaus Bochum
2020-2023
Ruhr University Bochum
2020-2023
Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people characterization cognitive impairments. Successful decoding these bio-signals is however non-trivial because time-varying non-stationary characteristics. Furthermore, existence short- long-range dependencies in time-series signal makes even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures...
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 in non-invasive BCI applications but often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG deeply hidden. State-of-the-art deep-learning algorithms successful learning hidden, patterns. However, quality quantity presented inputs pivotal....
Objective. Advancements in electrode design have resulted micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted can record spike activity (SA) one or more neurons along background (BA). The aim this study is to isolate SA neural source. This process called sorting classification. Advanced algorithms are time consuming because human intervention at various stages pipeline. Current approaches lack...
Abstract Invasive brain–computer-interfaces (BCIs) aim to improve severely paralyzed patient’s (e.g. tetraplegics) quality of life by using decoded movement intentions let them interact with robotic limbs. We argue that the performance in controlling an end-effector a BCI depends on three major factors: decoding error, missing somatosensory feedback and alignment error caused translation and/or rotation relative real or perceived body. Using virtual reality (VR) model ideal decoder healthy...
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 in non-invasive BCI applications but often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG deeply hidden. State-of-the-art deep-learning algorithms successful learning hidden, patterns. However, quality quantity presented inputs pivotal. Here, we...