Brain activity recognition research has been a challenging area for many decades since Hans Berger described electroencephalogram (EEG) in 1929. Many previous researches cannot successfully identify EEG status due to dynamic brain activities and complicated brain correlation. This article adopts multiagent-based methods to analyze EEG datasets, which can enhance the analytical efficiency through incorporating autonomous, self-coordination characteristics of agents. Intelligent agents are autonomous applications that can improve system compatibility. The preliminary results indicate that the combination of time-dependency correlation method with multiagent method is an efficient solution for brain activity recognition.
Scopus Subject Areas
- Theoretical Computer Science
- Computer Science Applications
- Computer Networks and Communications
- Computational Theory and Mathematics
- brain informatics
- EEG pattern recognition