A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection.

Chi-Chun Lo,Bor-Shyh Lin,Shang-Ho Tsai,Wai-Chi Fang,Yu-Chun Chen,Tsung-Yi Chien

doi:10.3390/s16020213

Abstract

Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype.

Highlights

The motor imagery-based brain-computer interface (MI-based BCI) is the direct communication pathway between an external machine and the brain so that the user can control the external machine by the mental activity of motor imagery [1,2]
In order to improve the accuracy of MI-based BCIs, several spatial filters, such as common spatial pattern (CSP), independent component analysis (ICA), Laplacian derivations, and common average reference (CAR) [7,8,9], are proposed to enhance the EEG features related to motor imagery in the specific region
Channel selection is a kind of feature selection, which can be characterized as a special filter [10] used for selecting related-feature channels and removing irrelevant or noisy channels to improve the BCI performance [11]

Summary

Introduction

The motor imagery-based brain-computer interface (MI-based BCI) is the direct communication pathway between an external machine and the brain so that the user can control the external machine by the mental activity of motor imagery [1,2]. MI-based BCI is a type of active BCI which does not require any external stimuli [3]. The design of MI-based BCIs is based on the above phenomena reflecting the sensorimotor brain activity. In order to improve the accuracy of MI-based BCIs, several spatial filters, such as common spatial pattern (CSP), independent component analysis (ICA), Laplacian derivations, and common average reference (CAR) [7,8,9], are proposed to enhance the EEG features related to motor imagery in the specific region. One of the alternative approaches is channel selection. Lan et al, proposed an EEG channel selection approach using the mutual information technique [12]. The mutual information between the channels and the class labels was used for ranking all the channels

Methods

Results

Discussion

Conclusion