Volume 4, Issue 2, 2014

By | August 12, 2018

Toward An Adaptive P300 Based Brain Computer

Mohammed J. Alhaddad, Information Technology Department, King Abdulaziz University, Jeddah, Saudi Arabia.
Mahmoud Kamel, Information System Department, King Abdulaziz University, Jeddah, Saudi Arabia.
Noura Al-Otaibi, Computer Department, King Abdulaziz University, Jeddah, Saudi Arabia.

Abstract—BCI represents a new communication way for people who suffering from neuromuscular impairment. In other words, BCI allow them to communicate with outside an environment, control prosthetic or other external devices by using only their brain activity. BCI system based on change of brain activity such that p300 potential. In BCI based on P300, the paradigm with some possible choices – such that letters or image – are present to users. One of the major problems in P300 application is the difficult to find a P300 response from a single trial.
Hence several trials are performed for each element in order to decrease the error in prediction. This led to spend long time for predicted the user intent. For this purpose, this study concerns on Bayesian method that can take adaptive decision about stopping a data acquisition modules as soon as reach to reliable decision. The main advantage of using adaptive number of trials is increased a communication speed with keeping a good classification accuracy.

Keywords- P300; BCI; Aggregation; Bayesian theory; Adaptive.


Software Agents Interaction Algorithms in Virtual Learning Environment

Zahi A.M. Abu Sarhan
Faculty of Information Technology: Software Engineering Department , Applied Science Private University, Amman, Jordan.

Abstract—This paper highlights the multi-agent learning virtual environment and agent’s communication algorithms. The researcher proposed three algorithms required software agent’s interaction in virtual learning information system environment. The first proposed algorithm is agents interaction localization algorithm, the second one is the dynamic agents distribution algorithm (load distribution algorithm), and the third model is Agent communication algorithm based on using agents intermediaries. The main objectives of these algorithms are to reduce the response time for any agents’ changes in virtual learning environment (VLE) by increasing the information exchange intensity between software agents and reduce the overall network load, and to improve the communication between mobile agents in distributed information system to support effectiveness. Finally the paper describe the algorithms of information exchange between mobile agents in VLE based on the expansion of the address structure and the use of an agent, intermediary agents, matchmaking agents ,brokers and their entrepreneurial functions.

Keywords- multi-agent system; agent interaction models; Intermediary Agents; Virtual Learning Environment; Brokering Agents;
Matchmaking Agents.

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