Brain-Computer Interface

11Optimized Feature Selection Techniques for Classifying Electrocorticography Signals

B. Paulchamy¹, R. Uma Maheshwari¹*, D. Sudarvizhi AP(Sr. G)², R. Anandkumar AP(Sr. G)² and Ravi G. ³

¹Hindusthan Institute of Technology, Coimbatore, Tamil Nadu, India

²KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India

³Department of ECE, Sona College of Technology, Salem, Tamil Nadu, India

Abstract

The combination of hardware and software communication systems that consists of external devices or control computers that use cerebral activity is Brain-Computer Interface (BCI). BCI helps to communicate with severely impaired people who have been wholly paralyzed or “locked” by neurological neuromuscular conditions. A BCI system works to classify brain signals and carry out computer-controlled actions using machine learning algorithms. As a recording technique for BCI, electrocorticography (ECoG) is better suited for fundamental neuroscience. The signal acquisition stage in a generic BCI framework captures brain signals and reduces noise and process artifacts. The pre-processing phase prepares the signals for further processing in a suitable way. The extraction stage identifies discriminative information in the recorded brain signals. Once measured, the signal is mapped to a vector containing the signals observed with useful and discriminant characteristics. The function of the Auto-Regressive (AR) model and Wavelet Transform functions are extracted. The features extracted ...