A genetic interval type-2 fuzzy logic-based approach for generating interpretable linguistic models for the brain P300 phenomena recorded via brain–computer interfaces

One of the important areas of brain–computer interface (BCI) research is to identify event-related potentials (ERPs) which are spatial–temporal patterns of the brain activity that happen after presentation of a stimulus and before execution of a movement. One of the important ERPs is the P300 which is an endogenous component of ERPs with a latency of about 300 ms which is elicited by significant stimuli (visual, or auditory). Various machine learning-based classifiers have been used to predict the P300 events and relate them to the human intended activities. However, the vast majority of the employed techniques like Bayesian linear discriminant analysis (BLDA) and regularized fisher linear discriminant analysis (RFLDA) are black box models which are difficult to understand and analyse by a normal clinician. In addition, due to the inter- and intra-user uncertainties associated with the P300 events, most of the existing classifiers need to be trained for a specific user under specific circumstances and the classifier needs to be retrained for different users or change of circumstances. In this paper, we present an interval type-2 fuzzy logic-based classifier which is able to handle the users’ uncertainties to produce better prediction accuracies than other competing classifiers such as BLDA or RFLDA. In addition, the generated type-2 fuzzy classifier is learnt from data via genetic algorithms to produce a small number of rules with a rule length of only one antecedent to maximise the transparency and interpretability for the normal clinician. We also employ a feature selection system based on an ensemble neural networks recursive feature selection which is able to find the effective time instances within the effective sensors in relation to given P300 event. We will present various experiments which were performed on standard data sets and using real-data sets obtained from real subjects’ experiments performed in the BCI laboratory in King Abdulaziz University. It will be shown that the produced type-2 fuzzy logic-based classifier will learn simple rules which are easy to understand explaining the events in question. In addition, the produced type-2 fuzzy logic classifier will be able to give better accuracies when compared to BLDA or RFLDA on various human subjects on the standard and real-world data sets

A Survey on the Project in title

In this paper we present a survey of work that has been done in the project ldquo;Unsupervised Adaptive P300 BCI in the framework of chaotic theory and stochastic theoryrdquo;we summarised the following papers, (Mohammed J Alhaddad amp; 2011), (Mohammed J. Alhaddad amp; Kamel M, 2012), (Mohammed J Alhaddad, Kamel, amp; Al-Otaibi, 2013), (Mohammed J Alhaddad, Kamel, amp; Bakheet, 2013), (Mohammed J Alhaddad, Kamel, amp; Al-Otaibi, 2014), (Mohammed J Alhaddad, Kamel, amp; Bakheet, 2014), (Mohammed J Alhaddad, Kamel, amp; Kadah, 2014), (Mohammed J Alhaddad, Kamel, Makary, Hargas, amp; Kadah, 2014), (Mohammed J Alhaddad, Mohammed, Kamel, amp; Hagras, 2015).We developed a new pre-processing method for denoising P300-based brain-computer interface data that allows better performance with lower number of channels and blocks. The new denoising technique is based on a modified version of the spectral subtraction denoising and works on each temporal signal channel independently thus offering seamless integration with existing pre-processing and allowing low channel counts to be used. We also developed a novel approach for brain-computer interface data that requires no prior training. The proposed approach is based on interval type-2 fuzzy logic based classifier which is able to handle the usersrsquo; uncertainties to produce better prediction accuracies than other competing classifiers such as BLDA or RFLDA. In addition, the generated type-2 fuzzy classifier is learnt from data via genetic algorithms to produce a small number of rules with a rule length of only one antecedent to maximize the transparency and interpretability for the normal clinician. We also employ a feature selection system based on an ensemble neural networks recursive feature selection which is able to find the effective time instances within the effective sensors in relation to given P300 event. The basic principle of this new class of techniques is that the trial with true activation signal within each block has to be different from the rest of the trials within that block. Hence, a measure that is sensitive to this dissimilarity can be used to make a decision based on a single block without any prior training. The new methods were verified using various experiments which were performed on standard data sets and using real-data sets obtained from real subjects experiments performed in the BCI lab in King Abdulaziz University. The results were compared to the classification results of the same data using previous methods. Enhanced performance in different experiments as quantitatively assessed using classification block accuracy as well as bit rate estimates was confirmed. It will be shown that the produced type-2 fuzzy logic based classifier will learn simple rules which are easy to understand explaining the events in question. In addition, the produced type-2 fuzzy logic classifier will be able to give better accuracies when compared to BLDA or RFLDA on various human subjects on the standard and real-world data sets