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Act as what you think : towards personalized EEG interaction through attentional and embedded LSTM learning
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Li, Xinglin, Deng, Hanhui, Ouyang, Jinhui, Wan, Huayan, Yu, Weiren and Wu, Di (2023) Act as what you think : towards personalized EEG interaction through attentional and embedded LSTM learning. IEEE Transactions on Mobile Computing . pp. 1-13. doi:10.1109/tmc.2023.3283022 ISSN 1558-0660.
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Official URL: https://doi.org/10.1109/tmc.2023.3283022
Abstract
The “mind-controlling” capability has always been in mankind's fantasy. With the recent advancements in electroencephalograph (EEG) techniques, brain-computer interface (BCI) researchers have explored some solutions to allow individuals to perform various tasks using their minds. However, the commercial off-the-shelf devices to run accurate EEG signal collection are usually expensive and the comparably cheaper devices can only present coarse results, which prevents the practical application of these devices in domestic services. To tackle this challenge, we propose and develop an end-to-end solution that enables fine brain-robot interaction (BRI) through embedded learning of coarse EEG signals from low-cost devices, namely PerBCI, so that people having difficulty moving, such as the elderly, can mind command and control a robot to perform some basic household tasks. Our contributions are three folds: 1) We present a stacked long short-term memory (BiLSTM) structure, along with specific pre-processing techniques to handle the time-dependency of EEG signals and their classification. 2) We propose a personalized design to adaptively capture multiple features and achieve accurate recognition of individual EEG signals by enhancing the signal interpretation of BiLSTM with an attention mechanism. 3) We develop a low-cost, real-time and end-to-end BRI system that can run our PerBCI models and algorithms in the embedded robot platform to perform more than one type of domestic task based on the users' EEG signal inputs. Our real-world experiments with elderly participants of diverse backgrounds in a home setting and system comparison with other approaches show that the proposed end-to-end solution with low cost can achieve satisfactory run-time speed, accuracy and energy-efficiency.
Item Type: | Journal Article | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Journal or Publication Title: | IEEE Transactions on Mobile Computing | ||||||
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | ||||||
ISSN: | 1558-0660 | ||||||
Official Date: | 2023 | ||||||
Dates: |
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Page Range: | pp. 1-13 | ||||||
DOI: | 10.1109/tmc.2023.3283022 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
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