Optimization of parameters for improving the performance of EEG-based BCI system

• Published in: Journal of reliable intelligent environments 2021-06, Vol.7 (2), p.145-156
• Main Authors: Ghumman, Mandeep Kaur, Singh, Satvir, Singh, Navtej, Jindal, Balkrishan
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Brain research; Classification; Computer Science; Electroencephalography; Feature extraction; Filter banks; Health Informatics; Human-computer interface; Independent component analysis; Kernels; Optimization; Original Article; Parameters; Performance and Reliability; Polynomials; Simulation and Modeling; Software Engineering/Programming and Operating Systems; Support vector machines; User Interfaces and Human Computer Interaction
• ISSN: 2199-4668; 2199-4676
• DOI: 10.1007/s40860-020-00117-y

Brain–computer interface (BCI) is an active domain which has attracted attention of the research community in recent years. It offers huge potential as a technology which can estimate the intention of a user by analysis of brain signals and establish a communication channel directly between a human brain and an external device. Electroencephalography (EEG) is the most popular signal acquisition technique due to its ease of use and simplicity. In EEG-based BCI systems, electrodes are placed on specific positions on the scalp of the subject to record electrical activity. The BCI system consists of sequential stages of signal acquisition, its preprocessing, feature extraction and feature classification. It is an active research area which has a focus on improving classification accuracy in motor imagery-based BCI systems. The first stage in a BCI system is to acquire EEG signals from different positions of the scalp of the human subject. The acquired brain signals are preprocessed to remove artifacts before these are fed to feature the extraction stage. In this paper, independent component analysis (ICA) technique is used to remove artifacts from acquired signals. Filter bank common spatial pattern (FBCSP) technique is then used for feature extraction and feature selection. A feature classification approach based on support vector machine (SVM) is proposed in this work and its performance is enhanced by optimizing its polynomial kernel parameters. Selection of kernel parameters is done by grid search method using the fivefold cross-validation procedure. The proposed approach is then executed on publicly available data set 2a of BCI Competition IV. Results show that the proposed approach offers higher classification accuracy and lower misclassification rate as compared to other methods executed on the same dataset, as reported in literature.