Improving classification accuracy using intra-session classifier training and implementation for a BCI based on automated parameter selection

This work presents the findings of a research study which investigated the possibility of automating parameter selection for an EEG-based P300-driven Brain-Computer Interface (BCI) using a Genetic Algorithm (GA). Although this approach showed marked advantages over current practices, the GA approach required lengthy execution times which render it infeasible for online utilisation. The GA method was subsequently replaced by the less execution time-intensive N-fold cross-validation (NFCV) for the meta-optimisation of feature extraction and pre-processing parameters using Fisher’s Linear Discriminant Analysis (FLDA). In addition, this work sought to improve BCI classification accuracy using a training data collection and application protocol that the authors refer to as ‘Intra-session classifier training and implementation’. Command misclassifications are detrimental to BCI operation since they either result in the execution of the wrong command or they require correction action which is time consuming. The intra-session training protocol has been used in prior instances for BCI development however this work is a novel application of the intra-session protocol to P300-based BCIs. Moreover, there is no formal study that investigates the effect of the intra-session protocol on the classification accuracy of BCIs. This work directly addresses this issue. Intra-session classifier training and implementation using NFCV as a basis for automated parameter proposal yields a classification accuracy of 82.94% compared to 45.44% for the inter-session approach using data-insensitive parameters. The findings are significant as intra-session P300-BCI classifier training can be applied to any P300-based BCI regardless of the existing application platform to obtain improved classification performance. Syan, Harnarinesingh and Palaniappan BIOGRAPHICAL STATEMENTS Professor Chanan S Syan graduated from the University of Bradford, UK in 1983 with a BEng (Hons) in Mechanical Engineering. In 1988 he obtained a doctorate from the University of Hull, UK. He has over 12 years of industrial experience and over 25 years in academia at all levels. Presently, he is head of Production Engineering and Management, Leader of Graduate programmes and Professor at the University of the West Indies. Specializations include – Brain Computer Interfaces, Manufacturing, Design and Manufacture and Asset Management. He has lead research, managed successful research projects and published widely internationally in all forms of learned publications. Mr. Randy Harnarinesingh graduated with the BSc in Electrical & Computer Engineering (First Class Honours) from the University of the West Indies at St. Augustine, Trinidad in 2008. He is currently pursuing the PhD in Mechanical Engineering from the University of the West Indies at St. Augustine, Trinidad. His post-graduate research is in the area of Brain-Computer Interfaces and Autonomous Robotics. Dr. Ramaswamy Palaniappan is currently an academic with the School of Computer Science and Electronic Engineering, University of Essex, United Kingdom. His current research interests include biological signal processing, brain-computer interfaces (BCI), biometrics, artificial neural networks, genetic algorithms, and image processing. To date, he has published over 100 papers in peer-reviewed journals, book chapters, and conference proceedings. Dr. Palaniappan is a senior member of the Institute of Electrical and Electronics Engineers and IEEE Engineering in Medicine and Biology Society, member in Institution of Engineering and Technology, and Biomedical Engineering Society. He also serves as editorial board member for several international journals. His pioneering work on BCI has received international recognition.