Simultaneously Spatiospectral Pattern Learning and Contaminated Trial Pruning for Electroencephalography-Based Brain Computer Interface

Chun-Ping Shieh,You-Yin Chen,Shih-Hung Yang,Chao-Hung Kuo,Yu-Chun Lo,Yun-Ting Kuo,Yu-Shun Liu

doi:10.3390/sym12091387

Abstract

Electroencephalography (EEG)-based brain computer interfaces (BCIs) translate motor imagery commands into the movements of an external device (e.g., a robotic arm). The automatic design of spectral and spatial filters is a challenging task, as the frequency bands of the spectral filters must be predefined by previously published studies and given that they may be affected during trials by artifacts and improper motor imagery (MI). This study aimed to eliminate the contaminated trials automatically during classifier training, and to simultaneously learn the spectral and spatial patterns without the need for predefined frequency bands. Compared with previous studies that measured the discriminative power of a frequency band based on mutual information, this study determined the difference of the class conditional probability density function between two MI classes. This information was further shared to measure the contamination level of the trial that simplified the computation. A particle-based approximation technique iteratively constructed a filter bank that extracted discriminative features, and simultaneously removed potentially contaminated trials. The particle weight was estimated by an analysis of variance F-test instead of mutual information as commonly used in previous studies. The experimental results of a publicly available dataset revealed that the proposed method outperformed the other BCI in terms of the classification accuracy. Asymmetrical spatial patterns were found on left- versus right-hand MI classifications. The learnt spectral and spatial patterns were consistent with prior neurophysiological knowledge.

Highlights

A brain computer interface (BCI) provides direct communication between the brain and an external device that translates neuronal signals into user commands without the use of the peripheral nerve and muscle pathways
BCI Competition IV Datasets 2a provided by the Institute for Knowledge Discovery were used for the evaluation of the proposed Parzen Windows-based spatiospectral patterns with trial pruning (PWSPTP) [32]
The present study proposed a PWSPTP that constructed a filter bank without predefined frequency bands and without contaminated trials during the training phase

Summary

Introduction

A brain computer interface (BCI) provides direct communication between the brain and an external device that translates neuronal signals into user commands without the use of the peripheral nerve and muscle pathways. Many research groups have created electroencephalography (EEG)-based BCIs that offer sufficient time resolution, usability, and are associated with lower brain surgery risks [1,2,3,4]. The analysis of EEG signals induced by motor imagery (MI), i.e., the imagery of body-part movements, has received considerable attention because of its widespread application in motor tasks, such as wheelchairs and mouse cursor controls [1]. Many data-driven approaches have been developed to automatically design BCIs, low signal-to-noise ratios of EEG signals and the highly complex connectivity of the human brain make the decoding of the user’s intention difficult. The EEG signal possesses task-specific features in the spectral and spatial domains during the MI task [5]. The spectral features are extracted by a spectral filter with a specific frequency band

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