Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2511.07936 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917073008984064 |
|---|---|
| author | Park, Ji-Ha Kwak, Heon-Gyu Shin, Gi-Hwan Jeon, Yoo-In Park, Sun-Min Hwang, Ji-Yeon Lee, Seong-Whan |
| author_facet | Park, Ji-Ha Kwak, Heon-Gyu Shin, Gi-Hwan Jeon, Yoo-In Park, Sun-Min Hwang, Ji-Yeon Lee, Seong-Whan |
| contents | Brain-computer interface (BCI) research, while promising, has largely been confined to static and fixed environments, limiting real-world applicability. To move towards practical BCI, we introduce a real-time wireless imagined speech electroencephalogram (EEG) decoding system designed for flexibility and everyday use. Our framework focuses on practicality, demonstrating extensibility beyond wired EEG devices to portable, wireless hardware. A user identification module recognizes the operator and provides a personalized, user-specific service. To achieve seamless, real-time operation, we utilize the lab streaming layer to manage the continuous streaming of live EEG signals to the personalized decoder. This end-to-end pipeline enables a functional real-time application capable of classifying user commands from imagined speech EEG signals, achieving an overall 4-class accuracy of 62.00 % on a wired device and 46.67 % on a portable wireless headset. This paper demonstrates a significant step towards truly practical and accessible BCI technology, establishing a clear direction for future research in robust, practical, and personalized neural interfaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07936 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward Practical BCI: A Real-time Wireless Imagined Speech EEG Decoding System Park, Ji-Ha Kwak, Heon-Gyu Shin, Gi-Hwan Jeon, Yoo-In Park, Sun-Min Hwang, Ji-Yeon Lee, Seong-Whan Artificial Intelligence Brain-computer interface (BCI) research, while promising, has largely been confined to static and fixed environments, limiting real-world applicability. To move towards practical BCI, we introduce a real-time wireless imagined speech electroencephalogram (EEG) decoding system designed for flexibility and everyday use. Our framework focuses on practicality, demonstrating extensibility beyond wired EEG devices to portable, wireless hardware. A user identification module recognizes the operator and provides a personalized, user-specific service. To achieve seamless, real-time operation, we utilize the lab streaming layer to manage the continuous streaming of live EEG signals to the personalized decoder. This end-to-end pipeline enables a functional real-time application capable of classifying user commands from imagined speech EEG signals, achieving an overall 4-class accuracy of 62.00 % on a wired device and 46.67 % on a portable wireless headset. This paper demonstrates a significant step towards truly practical and accessible BCI technology, establishing a clear direction for future research in robust, practical, and personalized neural interfaces. |
| title | Toward Practical BCI: A Real-time Wireless Imagined Speech EEG Decoding System |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2511.07936 |