Internal Feature Selection Method of CSP Based on L1-Norm and Dempster–Shafer Theory

Abstract

The common spatial pattern (CSP) algorithm is a well-recognized spatial filtering method for feature extraction in motor imagery (MI)-based brain-computer interfaces (BCIs). However, due to the influence of nonstationary in electroencephalography (EEG) and inherent defects of the CSP objective function, the spatial filters, and their corresponding features are not necessarily optimal in the feature space used within CSP. In this work, we design a new feature selection method to address this issue by selecting features based on an improved objective function. Especially, improvements are made in suppressing outliers and discovering features with larger interclass distances. Moreover, a fusion algorithm based on the Dempster-Shafer theory is proposed, which takes into consideration the distribution of features. With two competition data sets, we first evaluate the performance of the improved objective functions in terms of classification accuracy, feature distribution, and embeddability. Then, a comparison with other feature selection methods is carried out in both accuracy and computational time. Experimental results show that the proposed methods consume less additional computational cost and result in a significant increase in the performance of MI-based BCI systems.

Discrimination of Genuine and Acted Emotional Expressions Using EEG Signal and Machine Learning

“…The method is a univariate test and it does not consider multiple variables together and their possible interactions. Future studies should consider more robust feature selection methods such as correlation-based channel selection [46], bispectrum-based [47] and internal feature selection method of common spatial pattern [48]. In addition, to further improve the classification accuracy, researchers may combine multiple modalities such as EEG with eye tracking, EEG with functional near infrared spectroscopy or combination of the three modalities.…”

Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton

“…Notably, we acknowledged the existence of numerous alternative novel algorithms for decoding neural features of the EEG signal [ 41 , 42 , 43 , 44 , 45 ]. Among them, deep learning and EEG channel optimization methods are the most relevant methods for this study.…”

Trial Regeneration With Subband Signals for Motor Imagery Classification in BCI Paradigm

“…[7]. In terms of neurophysiology, motor imagery accompanies attenuation or enhancement of rhythmical synchrony over the sensorimotor cortex with the frequency bands of alpha (8)(9)(10)(11)(12)(13) and beta (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) [8] - [11]. This paper focuses on EEG based classification of two motor imagery tasks.…”

“…The effective features are selected using neighborhood component analysis method. A combination of brain function connectivity mechanism and one versus the rest filterbank CSP is used to improve the MI classification performance in [17]. The multi-kernel relevance vector machine (RVM) is utilized for classification.…”