Brain-Controlled, AR-based Home Automation System using SSVEP-based Brain-Computer Interface and EOG-based Eye Tracker: A Feasibility Study for the Elderly End Useropen access
- Authors
- Park, Seonghun; Ha, Jisoo; Park, Jimin; Lee, Kyeonggu; Im, Chang Hwan
- Issue Date
- Jan-2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Visualization; Electrodes; Switches; Home appliances; Electroencephalography; Usability; Older adults; Augmented reality; brain-computer interface; electroencephalography; electrooculography; steady-state visual evoked potential
- Citation
- IEEE Transactions on Neural Systems and Rehabilitation Engineering, v.31, pp.544 - 553
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Volume
- 31
- Start Page
- 544
- End Page
- 553
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/185762
- DOI
- 10.1109/TNSRE.2022.3228124
- ISSN
- 1534-4320
- Abstract
- Over the past decades, brain-computer interfaces (BCIs) have been developed to provide individuals with an alternative communication channel toward external environment. Although the primary target users of BCI technologies include the disabled or the elderly, most newly developed BCI applications have been tested with young, healthy people. In the present study, we developed an online home appliance control system using a steady-state visual evoked potential (SSVEP)-based BCI with visual stimulation presented in an augmented reality (AR) environment and electrooculogram (EOG)-based eye tracker. The performance and usability of the system were evaluated for individuals aged over 65. The participants turned on the AR-based home automation system using an eye-blink-based switch, and selected devices to control with three different methods depending on the user’s preference. In the online experiment, all 13 participants successfully completed the designated tasks to control five home appliances using the proposed system, and the system usability scale exceeded 70. Furthermore, the BCI performance of the proposed online home appliance control system surpassed the best results of previously reported BCI systems for the elderly.
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