Web Search via an Efficient and Effective Brain-Machine Interface
This provides a novel search interface for users with severe neuromuscular disorders, offering an incremental improvement by integrating brain signals into existing search paradigms.
The paper tackles the problem of traditional search interactions by developing a Brain-Machine Search Interface (BMSI) system that uses EEG signals to enable hands-free search tasks, such as query reformulation and result re-ranking based on real-time user satisfaction feedback, achieving real-time decoding and interaction without mouse or keyboard.
While search technologies have evolved to be robust and ubiquitous, the fundamental interaction paradigm has remained relatively stable for decades. With the maturity of the Brain-Machine Interface, we build an efficient and effective communication system between human beings and search engines based on electroencephalogram(EEG) signals, called Brain-Machine Search Interface(BMSI) system. The BMSI system provides functions including query reformulation and search result interaction. In our system, users can perform search tasks without having to use the mouse and keyboard. Therefore, it is useful for application scenarios in which hand-based interactions are infeasible, e.g, for users with severe neuromuscular disorders. Besides, based on brain signals decoding, our system can provide abundant and valuable user-side context information(e.g., real-time satisfaction feedback, extensive context information, and a clearer description of information needs) to the search engine, which is hard to capture in the previous paradigm. In our implementation, the system can decode user satisfaction from brain signals in real-time during the interaction process and re-rank the search results list based on user satisfaction feedback. The demo video is available at http://www.thuir.cn/group/YQLiu/datasets/BMSISystem.mp4.