Using Passive BCI for Personalization of Assistive Wearable Devices: A Proof-of-Concept Study
Assistive wearable devices can significantly enhance the quality of life for individuals with movement impairments, aid the rehabilitation process, and augment movement abilities of healthy users. However, personalizing the assistance to individual preferences and needs remains a challenge. Brain-Co...
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10843245/ |
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| author | Asghar Mahmoudi Morteza Khosrotabar Klaus Gramann Stephan Rinderknecht Maziar A. Sharbafi |
| author_facet | Asghar Mahmoudi Morteza Khosrotabar Klaus Gramann Stephan Rinderknecht Maziar A. Sharbafi |
| author_sort | Asghar Mahmoudi |
| collection | DOAJ |
| description | Assistive wearable devices can significantly enhance the quality of life for individuals with movement impairments, aid the rehabilitation process, and augment movement abilities of healthy users. However, personalizing the assistance to individual preferences and needs remains a challenge. Brain-Computer Interface (BCI) offers a promising solution for this personalization problem. The overarching goal of this study is to investigate the feasibility of utilizing passive BCI technology to personalize the assistance provided by a knee exoskeleton. Participants performed seated knee flexion-extension tasks while wearing a one-degree-of-freedom knee exoskeleton with varying levels of applied force. Their brain activities were recorded throughout the movements using electroencephalography (EEG). EEG spectral bands from several brain regions were compared between the conditions with the lowest and highest exoskeleton forces to identify statistically significant changes. A Naive Bayes classifier was trained on these spectral features to distinguish between the two conditions. Statistical analysis revealed significant increases in <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> activity and decreases in <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> activity in the frontal, motor, and occipital cortices. These changes suggest heightened attention, concentration, and motor engagement when the task became more difficult. The trained Naive Bayes classifier achieved an average accuracy of approximately 72% in distinguishing between the two conditions. The outcomes of our study demonstrate the potential of passive BCI in personalizing assistance provided by wearable devices. Future research should further explore integrating passive BCI into assistive wearable devices to enhance user experience. |
| format | Article |
| id | doaj-art-9de2d337cebb4536af0e275810bc3e17 |
| institution | Kabale University |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-9de2d337cebb4536af0e275810bc3e172025-01-30T00:00:06ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-013347648710.1109/TNSRE.2025.353015410843245Using Passive BCI for Personalization of Assistive Wearable Devices: A Proof-of-Concept StudyAsghar Mahmoudi0https://orcid.org/0000-0003-1223-8334Morteza Khosrotabar1https://orcid.org/0000-0002-7143-5285Klaus Gramann2https://orcid.org/0000-0003-2673-1832Stephan Rinderknecht3Maziar A. Sharbafi4https://orcid.org/0000-0001-5727-7527Institute for Mechatronic Systems, Faculty of Mechanical Engineering, Technical University Darmstadt, Darmstadt, GermanyLauflabor Locomotion Laboratory, Institute of Sports Science, Technical University Darmstadt, Darmstadt, GermanyChair of Biological Psychology and Neuroergonomics, Institute of Psychology and Ergonomics, Technische Universitaet Berlin, Berlin, GermanyInstitute for Mechatronic Systems, Faculty of Mechanical Engineering, Technical University Darmstadt, Darmstadt, GermanyLauflabor Locomotion Laboratory, Institute of Sports Science, Technical University Darmstadt, Darmstadt, GermanyAssistive wearable devices can significantly enhance the quality of life for individuals with movement impairments, aid the rehabilitation process, and augment movement abilities of healthy users. However, personalizing the assistance to individual preferences and needs remains a challenge. Brain-Computer Interface (BCI) offers a promising solution for this personalization problem. The overarching goal of this study is to investigate the feasibility of utilizing passive BCI technology to personalize the assistance provided by a knee exoskeleton. Participants performed seated knee flexion-extension tasks while wearing a one-degree-of-freedom knee exoskeleton with varying levels of applied force. Their brain activities were recorded throughout the movements using electroencephalography (EEG). EEG spectral bands from several brain regions were compared between the conditions with the lowest and highest exoskeleton forces to identify statistically significant changes. A Naive Bayes classifier was trained on these spectral features to distinguish between the two conditions. Statistical analysis revealed significant increases in <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> activity and decreases in <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> activity in the frontal, motor, and occipital cortices. These changes suggest heightened attention, concentration, and motor engagement when the task became more difficult. The trained Naive Bayes classifier achieved an average accuracy of approximately 72% in distinguishing between the two conditions. The outcomes of our study demonstrate the potential of passive BCI in personalizing assistance provided by wearable devices. Future research should further explore integrating passive BCI into assistive wearable devices to enhance user experience.https://ieeexplore.ieee.org/document/10843245/Assistive wearable robotselectroencephalogramexoskeleton personalizationpassive brain-computer-interface |
| spellingShingle | Asghar Mahmoudi Morteza Khosrotabar Klaus Gramann Stephan Rinderknecht Maziar A. Sharbafi Using Passive BCI for Personalization of Assistive Wearable Devices: A Proof-of-Concept Study IEEE Transactions on Neural Systems and Rehabilitation Engineering Assistive wearable robots electroencephalogram exoskeleton personalization passive brain-computer-interface |
| title | Using Passive BCI for Personalization of Assistive Wearable Devices: A Proof-of-Concept Study |
| title_full | Using Passive BCI for Personalization of Assistive Wearable Devices: A Proof-of-Concept Study |
| title_fullStr | Using Passive BCI for Personalization of Assistive Wearable Devices: A Proof-of-Concept Study |
| title_full_unstemmed | Using Passive BCI for Personalization of Assistive Wearable Devices: A Proof-of-Concept Study |
| title_short | Using Passive BCI for Personalization of Assistive Wearable Devices: A Proof-of-Concept Study |
| title_sort | using passive bci for personalization of assistive wearable devices a proof of concept study |
| topic | Assistive wearable robots electroencephalogram exoskeleton personalization passive brain-computer-interface |
| url | https://ieeexplore.ieee.org/document/10843245/ |
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