Enhancing Electrocorticography Brain Computer Interfaces with Genetics

Abstract

BrainComputer Interfaces (BCI) technology exploits the brain’s intuitive computing power. So far, the development of BCIs was considered to be science fiction. Scientists have been trying to decode brain signals since the introduction of Electroencephalography (EEG). EEG equipment is extensively utilized for recording the brain signals in BCI systems due to its attributes such as non-invasive, the potential for user mobility,high time resolution,and comparatively low cost. Of late, ElectroCorticoGraphy (ECoG)has garnered much interest as a recording technique for utilization in BCIs. ECoG will involve recording electrical signals from the human brain’s surface, often in patients who are being monitored prior to surgery. In comparison to the EEG, the ECoG has a higher spatial resolution. In this paper, the ECoG signals are pre-processed, and the features are extracted by utilizing Wavelet Packet Trees (WPT) and Common Spatial Patterns (CSP). Feature selection is carried out by the Genetic Algorithm (GA). ECoG signal classification is done using Random Forest, Logistic Regression, and Support Vector Machine (SVM) classifier.