Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing
Abstract Brain-computer interfaces (BCIs) can translate brain signals directly into commands for external devices. Electroencephalography (EEG)-based BCIs mostly rely on the classification of discrete mental states, leading to unintuitive control. The ERC-funded project "Feel Your Reach" a...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-02-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-55413-x |
| _version_ | 1797274741122269184 |
|---|---|
| author | Valeria Mondini Andreea-Ioana Sburlea Gernot R. Müller-Putz |
| author_facet | Valeria Mondini Andreea-Ioana Sburlea Gernot R. Müller-Putz |
| author_sort | Valeria Mondini |
| collection | DOAJ |
| description | Abstract Brain-computer interfaces (BCIs) can translate brain signals directly into commands for external devices. Electroencephalography (EEG)-based BCIs mostly rely on the classification of discrete mental states, leading to unintuitive control. The ERC-funded project "Feel Your Reach" aimed to establish a novel framework based on continuous decoding of hand/arm movement intention, for a more natural and intuitive control. Over the years, we investigated various aspects of natural control, however, the individual components had not yet been integrated. Here, we present a first implementation of the framework in a comprehensive online study, combining (i) goal-directed movement intention, (ii) trajectory decoding, and (iii) error processing in a unique closed-loop control paradigm. Testing involved twelve able-bodied volunteers, performing attempted movements, and one spinal cord injured (SCI) participant. Similar movement-related cortical potentials and error potentials to previous studies were revealed, and the attempted movement trajectories were overall reconstructed. Source analysis confirmed the involvement of sensorimotor and posterior parietal areas for goal-directed movement intention and trajectory decoding. The increased experiment complexity and duration led to a decreased performance than each single BCI. Nevertheless, the study contributes to understanding natural motor control, providing insights for more intuitive strategies for individuals with motor impairments. |
| first_indexed | 2024-03-07T15:02:36Z |
| format | Article |
| id | doaj.art-af3edb09a7f44ec680be159450c960c8 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:02:36Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-af3edb09a7f44ec680be159450c960c82024-03-05T19:04:17ZengNature PortfolioScientific Reports2045-23222024-02-0114111910.1038/s41598-024-55413-xTowards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processingValeria Mondini0Andreea-Ioana Sburlea1Gernot R. Müller-Putz2Institute of Neural Engineering, Graz University of TechnologyBernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of GroningenInstitute of Neural Engineering, Graz University of TechnologyAbstract Brain-computer interfaces (BCIs) can translate brain signals directly into commands for external devices. Electroencephalography (EEG)-based BCIs mostly rely on the classification of discrete mental states, leading to unintuitive control. The ERC-funded project "Feel Your Reach" aimed to establish a novel framework based on continuous decoding of hand/arm movement intention, for a more natural and intuitive control. Over the years, we investigated various aspects of natural control, however, the individual components had not yet been integrated. Here, we present a first implementation of the framework in a comprehensive online study, combining (i) goal-directed movement intention, (ii) trajectory decoding, and (iii) error processing in a unique closed-loop control paradigm. Testing involved twelve able-bodied volunteers, performing attempted movements, and one spinal cord injured (SCI) participant. Similar movement-related cortical potentials and error potentials to previous studies were revealed, and the attempted movement trajectories were overall reconstructed. Source analysis confirmed the involvement of sensorimotor and posterior parietal areas for goal-directed movement intention and trajectory decoding. The increased experiment complexity and duration led to a decreased performance than each single BCI. Nevertheless, the study contributes to understanding natural motor control, providing insights for more intuitive strategies for individuals with motor impairments.https://doi.org/10.1038/s41598-024-55413-x |
| spellingShingle | Valeria Mondini Andreea-Ioana Sburlea Gernot R. Müller-Putz Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing Scientific Reports |
| title | Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing |
| title_full | Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing |
| title_fullStr | Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing |
| title_full_unstemmed | Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing |
| title_short | Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing |
| title_sort | towards unlocking motor control in spinal cord injured by applying an online eeg based framework to decode motor intention trajectory and error processing |
| url | https://doi.org/10.1038/s41598-024-55413-x |
| work_keys_str_mv | AT valeriamondini towardsunlockingmotorcontrolinspinalcordinjuredbyapplyinganonlineeegbasedframeworktodecodemotorintentiontrajectoryanderrorprocessing AT andreeaioanasburlea towardsunlockingmotorcontrolinspinalcordinjuredbyapplyinganonlineeegbasedframeworktodecodemotorintentiontrajectoryanderrorprocessing AT gernotrmullerputz towardsunlockingmotorcontrolinspinalcordinjuredbyapplyinganonlineeegbasedframeworktodecodemotorintentiontrajectoryanderrorprocessing |