Design Considerations for Long Term Non-invasive Brain Computer Interface Training With Tetraplegic CYBATHLON Pilot.

Neethu Robinson,Nicole Wenderoth,Rea Lehner,Tushar Chouhan,Paulina Kratka,Ernest Mihelj,Frédéric Debraine,Cuntai Guan

doi:10.3389/fnhum.2021.648275

Abstract

Several studies in the recent past have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into control commands. While a multitude of studies have demonstrated the theoretic potential of BCI, a point of concern is that the studies are still confined to lab settings and mostly limited to healthy, able-bodied subjects. The CYBATHLON 2020 BCI race represents an opportunity to further develop BCI design strategies for use in real-time applications with a tetraplegic end user. In this study, as part of the preparation to participate in CYBATHLON 2020 BCI race, we investigate the design aspects of BCI in relation to the choice of its components, in particular, the type of calibration paradigm and its relevance for long-term use. The end goal was to develop a user-friendly and engaging interface suited for long-term use, especially for a spinal-cord injured (SCI) patient. We compared the efficacy of conventional open-loop calibration paradigms with real-time closed-loop paradigms, using pre-trained BCI decoders. Various indicators of performance were analyzed for this study, including the resulting classification performance, game completion time, brain activation maps, and also subjective feedback from the pilot. Our results show that the closed-loop calibration paradigms with real-time feedback is more engaging for the pilot. They also show an indication of achieving better online median classification performance as compared to conventional calibration paradigms (p = 0.0008). We also observe that stronger and more localized brain activation patterns are elicited in the closed-loop paradigm in which the experiment interface closely resembled the end application. Thus, based on this longitudinal evaluation of single-subject data, we demonstrate that BCI-based calibration paradigms with active user-engagement, such as with real-time feedback, could help in achieving better user acceptability and performance.

Highlights

In recent years, several Brain Computer Interface (BCI) competitions have been conducted with the goal of providing high-quality brain data to researchers to build effective tools and algorithms that may potentially be deployed in real-world environments (Sajda et al, 2003; Blankertz et al, 2004, 2006; Brunner et al, 2008; Tangermann et al, 2012)
The study is designed toward preparing the pilot to participate in Cybathlon 2020 BCI race
This paper presented a long-term evaluation of BCI performance including one tetraplegic pilot

Summary

Introduction

Several BCI competitions have been conducted with the goal of providing high-quality brain data to researchers to build effective tools and algorithms that may potentially be deployed in real-world environments (Sajda et al, 2003; Blankertz et al, 2004, 2006; Brunner et al, 2008; Tangermann et al, 2012). The data from these competitions are usually published as open access, allowing researchers to further investigate the brain activity data corresponding to various motor and cognitive tasks. Neurophysiological evidence to supplement the quantitative performance metrics is presented

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