Cooperative learning for biomedical signal processing and recognition

Advances in adaptive signal processing and analysis of multichannel data recorded from human body, require cooperative learning to enable communications between the nodes of the network. In more complex cases where the human body is involved in executing multiple and different tasks simultaneously, the learning process is subject to both learning and differentiating between the sensor groups. In this article, the concept of connectivity, as applied to electroencephalograms (EEGs), followed by cooperative learning and the theory involved are explored. Next, the concept of multitask diffusion adaptation and learning will be discussed. Finally, we see how cooperative learning can model a complex biological system as well as restoration of movement related brain potentials. In addition, a cooperative tracking system is introduced for detection and tracking of the changes in brain event related potentials.