Published Paper Details:

Few years ago it was a fantasy or a science fiction, to control the working of real world devices with thoughts. But now it has become a reality, thanks to Brain Computer Interface (BCI). BCI based on scalp electroencephalogram (EEG) signals is a non-invasive technique that is the major and economical mode of data acquisition from brain. Other forms of non-invasive brain data acquisition are Magneto encephalography (MEG), Functional magnetic resonance imaging (fMRI), Functional near-infrared spectroscopy (fNRIS). BCI can be applied to plethora of varied applications from both medical and non-medical fields. In medical field, it can be applied in prevention, detection and diagnosis, rehabilitation and restoration. In non medical field it can be applied in neuroergonomics, smart environment, gaming applications etc. BCI is a direct communication pathway between a human or animal brain and an external device. With BCI, a person with all kinds of disabilities but having a functioning brain can do anything to everything. As brain is the most complex organ of the human body, the data acquired from the brain will also exhibit complex features. To analyze these features we have to use efficient and competent algorithms. Algorithms working on the concept of collective intelligence are found to perform better in the anlaysis of EEG signals. There are numerous Swarm intelligence (SI) algorithms like Ant colony optimisation(ACO), Particle swarm optimisation(PSO), Bee colony optimization (BCO) that harness the collective intelligence. Here we endeavour to present the survey on the usage of swarm intelligence algorithms on the EEG data collected from the IoT device.