Implementing Clustering Methodology by Obtaining Centroids of Sensor Nodes for Human Brain Functionality

The BCIs are normally designed at the assisting, restoring and expanding human sensor-vehicles and the cognitive elements through the availability of more direct information transfer routs. This form of communication is done between the human brain and the exterior devices. In the invasive BCI, sensors are incorporate in the brain, which is situated on the cerebrum surface. At this juncture, the invasive BCI that utilizes the wireless sensors have not been attained. The vital purpose of the sensor nodes is gathering the information from the various domains before processing them to the BS. In the BS, the applications are found. Nonetheless, by assuring a more direct form of information transfer, the pathways between the sensor and sinks are capable of draining the energy in the nodes. This is due to the high requirement of power in the transferring messages. In that case, it is necessary that the nodes collaborate with each other to make sure that information transfer is possible within the nodes comprising of the sinks. This research proposes WSN based brain functionality sensing. For the acquired EEG data, k means clustering is applied to form the group of sensor nodes with cluster head. Then the krill herd algorithm is applied to optimize the cluster heads. The krill herd algorithm technique is used to select the optimized cluster heads. Then based on the sensor nodes and cluster heads, the brain functionality is identified. After that, the data is transmitted to the base station.