Analysis and Implementation of Brain Waves Feature Extraction and Classification to Control Robotic Hand
Keywords:BCI, Humanoid robotic hand, Pattern recognition, Features extraction, Support vector machine, Common spatial patterns, Principal component analysis, Channel selection algorithm
In this paper, (i) time domain, frequency domain and spatial domain feature extraction methods were investigated. (ii) Two dimensionality reduction methods were proposed, implemented and compared. (iii) The method pair (feature extraction + dimensionality reduction) that owns the lowest classification error rate will be used to learn a machine learning algorithm to control robotic hand in offline mode. Two classes EEG dataset of three bipolar channels was used. The extracted feature vectors were fed into Support Vector Machine with Radial Basis Function kernel (SVM-RBF) to train the classifier. The experimented time domain feature extraction methods were: Mean Absolute Value (MAV), integrated Absolute Value (IAV), Zero Crossing (ZC), Root Mean Square (RMS), Waveform Length (WL) and Slope Sign Change (SSC). Frequency domain feature was the Autoregressive Feature (AR). Finally, the spatial domain feature was the Common Spatial Patterns (CSP). Matlab codes for Principal Component Analysis (PCA) and channel selection algorithm were designed and used to reduce the dimensionality of the features vector. Results showed that CSP features got the lowest error rate for both dimensionality reduction technique with 2.14%. Results recommends to use channel selection algorithm over PCA since it owns the lowest processing time of 8.2s over 8.5s for PCA.