A multi stage EEG data classification using k-means and feed forward neural network

Objective: Electroencephalography based brain-computer interface techniques are widely used these days as they bring human intentions into reality. Various researchers have proposed different approaches to decode EEG signals for applications like spellers, emotion recognition, lie detection, brain games etc. The ability to analyze concealed behavior is very important for legal and security purposes. Method: This research aims to identify the concealed behavior of an individual. This paper presents a three-stage "Concealed Information Test" using wavelet transform, k-means clustering and multi-layer feed-forward neural network. The test has been developed by analyzing ERP component (P300) of EEG data during a mock crime scene. The wavelet transforms extracts time and frequency information from raw EEG data. K-means clustering clusters the wavelet coefficients into three clusters. As, neural network models the nonlinear time series data viz EEG, hence it has been utilized for classification of clustered data. Results: A hybrid 3-stage classification approach is proposed by combining the advantages of all above-mentioned approaches. EEG data is recorded for a "Concealed Information Test", for implementing the proposed framework. Conclusion: The performance of the proposed system is improved from existing approaches, by providing an accuracy of 83.1%.