Abstract:
Literature shows that cultivation of cognitive capacities are negatively affected by
five major mental factors, namely, Sensory desire, Anger or 111 will, Sloth torpor,
Restlessness and Doubt. In many instances they do not appear in isolation, yet as a
combination of one or more such factors. Sometimes a factor or more can cause to
arise another. This complex behavior results in not being able to exactly determine
which one of the factor is dominant. Identifying the dominant mental factor for the
disturbance of a person had been a hard task to accomplish since it needs a proper
mechanism and a criteria. Yet, it’s essential to treat and overcome the disturbance.
Identifying the dominant mental factor for the disturbance is a vital lead and kind of
a initiative to few other research areas as well. Therefore research into identification
ofthe mental factor that predominantly disturbs a person in his/her studies, daily life
and career has become a paramount research interest. A research has been carried out
to identify the predominant mental factor for disturbance of an individual by
capturing and analyzing Electroencephalography (EEG) brain waves. The research
has been conducted to capture EEG wave signals and to train an Artificial Neural
Networks for sessions where we exactly know the dominant mental factor. The
trained ANN has integrated with a Multi Agent Systems which receives output from
ANN for a given EEG waves from of a person in a particular session as percentage
values of above mentioned major mental factors, and deliberate on the output
generated by the ANN to decide on the most probable. ANN has fourteen inputs
which aligns with the sensors of Emotiv EPOC EEG headset and has five outputs
which gives percentage values of each mental hindrance that was available in the fed
brain wave. Multi agent system consist of five agents representing each mental
factor. MAS enhances the result given by ANN and finally come up with the most
dominant mental factor for the disturbance of the given brain wave based on mental
hindrances. Accuracy of the final result thoroughly depend on data sets which has
been used to train ANN and ontology of the agents.
