Spark and Rule-KNN based scalable machine learning framework for EEG deceit identification

Abstract

Brain computer interface (BCI) provides communication between the computer and the brain. It is the combination of hardware and software which provides non-muscular channel to send the various messages to control the computer. BCI is useful in various medical applications such as patients with neuromuscular injuries, locked-in syndrome (LiS) etc. BCI is not only useful in medical applications, but also useful in lie detection, entertainment, etc. In this paper, spark and rule-KNN based scalable framework has been presented using BCI with the EEG data collected on 20 subjects in which 10 are acted as innocent and 10 are acted as guilty. Using BCI P300, Deceit identification Test (DIT) is performed. To perform DIT, we classify the P300 signals which have a positive peak of 300 ms–1000 ms in one stimulus start. Data processing is performed with band pass filter to cut the frequency ranges and features are extracted using non-parametric weighted feature extraction followed by rule based discriminant classification. For training and testing, the data ratio selected as 80:20 and achieved the accuracy 92.46 %. Proposed framework provides better results in comparison with existing models presented in literature. Hence this model is accurate, scalable and fault tolerant.