Brain Computer Interface using EEG Based Sequential Minimal Optimization algorithms

Abstract

The concept of interfacing brains with robots/machines has been capturing human interests for a long time. The technology of the Brain-computer interface (BCI) has been aimed at building an interface between the brain and any electronic/electrical device (such as, smart home appliance, a wheelchair, and robotics devices) with the use of the electroencephalogram (EEG) that can be defined as a non-invasive approach for the measurement of the electrical potentials from the electrodes that have been placed on the scalp, produced by the activity of the brain. Over the past years, pattern classification was a highly challenging research field. Presently, the tasks of the pattern classification. In this paper, we chose motor imagery with the use of the single trial EEG signal, the SOM has been utilized to classify the signal processing algorithm ( FICA). In comparison to other algorithms of the EEG signal analyses. It has achieved a classification accuracy of up to 88.% in comparison with the other method where the reported accuracy has been 65%. The SOM classification algorithm has been fast, simple, efficient, and easy to use. It achieved satisfactory results at the BCI.