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- Title
A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning.
- Authors
Lee, Jongmin; Kim, Minju; Heo, Dojin; Kim, Jongsu; Kim, Min-Ki; Lee, Taejun; Park, Jongwoo; Kim, HyunYoung; Hwang, Minho; Kim, Laehyun; Kim, Sung-Phil
- Abstract
Brain-computer interfaces (BCIs) have a potential to revolutionize humancomputer interaction by enabling direct links between the brain and computer systems. Recent studies are increasingly focusing on practical applications of BCIs--e.g., home appliance control just by thoughts. One of the non-invasive BCIs using electroencephalography (EEG) capitalizes on event-related potentials (ERPs) in response to target stimuli and have shown promise in controlling home appliance. In this paper, we present a comprehensive dataset of online ERP-based BCIs for controlling various home appliances in diverse stimulus presentation environments. We collected online BCI data from a total of 84 subjects among whom 60 subjects controlled three types of appliances (TV: 30, door lock: 15, and electric light: 15) with 4 functions per appliance, 14 subjects controlled a Bluetooth speaker with 6 functions via an LCD monitor, and 10 subjects controlled air conditioner with 4 functions via augmented reality (AR). Using the dataset, we aimed to address the issue of inter-subject variability in ERPs by employing the transfer learning in two different approaches. The first approach, "within-paradigm transfer learning," aimed to generalize the model within the same paradigm of stimulus presentation. The second approach, "cross-paradigm transfer learning," involved extending the model from a 4-class LCD environment to different paradigms. The results demonstrated that transfer learning can effectively enhance the generalizability of BCIs based on ERP across different subjects and environments.
- Subjects
HOUSEHOLD appliances; ENTERPRISE resource planning software; BRAIN-computer interfaces; EVOKED potentials (Electrophysiology); ELECTRIC lighting; AUGMENTED reality
- Publication
Frontiers in Human Neuroscience, 2024, p1
- ISSN
1662-5161
- Publication type
Article
- DOI
10.3389/fnhum.2024.1320457