| Title: |
An SSVEP Stimuli Design using Real-time Camera View with Object Recognition |
| Authors: |
Chen, SK; Chen, CS; Wang, YK; Lin, CT |
| Publisher Information: |
IEEE |
| Publication Year: |
2021 |
| Collection: |
University of Technology Sydney: OPUS - Open Publications of UTS Scholars |
| Description: |
© 2020 IEEE. Most SSVEP-based stimuli BCIs are pre-defined using the white blocks. This kind of scenario lead less flexibility in the real life. To represent the flickers with the location, types and configurations of the objects in real world, this paper proposes an SSVEP-based BCI using real-time camera view with object recognition algorithm to provide intuitive BCI for users. A deep learning-based object recognition algorithm is used to calculate the location of the objects on the online camera view from a depth camera. After the bounding box of the objects is estimated, the location of the SSVEP flickers are designed to overlap on the object locations. An overlapping FFT and SVM is used to recognize the EEG signals into corresponding classes. In experimental results, the classification rate for camera view scenario is more than 94.1%. The results show that proposed SSVEP stimuli design is available to create an intuitive and reliable human machine interaction. The proposed results can be used for the users who have motor disabilities to further used to interact with assistive devices, such as: robotic arm and wheelchairs. |
| Document Type: |
conference object |
| File Description: |
application/pdf |
| Language: |
English |
| ISBN: |
978-1-72812-547-3; 1-72812-547-2 |
| Relation: |
http://purl.org/au-research/grants/arc/DP180100670; http://purl.org/au-research/grants/arc/DP180100656; University of Technology Sydney; Department of Defence; Defence Science and Technology Group of the Department of Defence; 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020; 2020 IEEE Symposium Series on Computational Intelligence (SSCI); 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2020, 00, pp. 562-567; http://hdl.handle.net/10453/147232 |
| Availability: |
http://hdl.handle.net/10453/147232 |
| Rights: |
info:eu-repo/semantics/openAccess ; © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Accession Number: |
edsbas.2E296B52 |
| Database: |
BASE |