![]() These competitions have served as a means to benchmark the performance of offline BCI and allowed researchers to evaluate and propose novel BCI algorithms by analysing the previously recorded data. 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. In recent years, 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 articles (Chaudhary et al., 2016, 2020 McFarland et al., 2020) have comprehensively reviewed the application of BCI as a communication, control, and rehabilitation tool for tetraplegic patients and highlighted that the research in this area is yet to fully evaluate ease-of-use for the end-user and the safe and efficient deployment of BCI in an out-of-the-lab environment. The reports on BCI applications for tetraplegic patients with SCI include multi-joint robotic arm control using intracortical recordings (Hochberg et al., 2006), cursor control using electrocorticographic activity (Wang et al., 2013), hand orthotic control and wheel chair control in virtual environment using non-invasive electroencephalographic activity (Pfurtscheller et al., 2000 Leeb et al., 2007). A few case-studies using invasive and non-invasive BCIs have demonstrated the application of BCI as a motor assistive technology for survivors of spinal cord injury (SCI). While many studies have demonstrated the theoretic potential of BCI, especially by deploying novel machine learning methods for detecting distinct task-specific attributes of the brain, a point of concern that remains is that the studies are still confined to lab settings and mostly limited to healthy able-bodied subjects (Lotte et al., 2018). Over the last few decades, several neuroengineering and neuroscience studies have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into commands that control an application or device (McFarland and Wolpaw, 2011 Chaudhary et al., 2016 Abiri et al., 2019 He et al., 2020). 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. 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. They also show an indication of achieving better online median classification performance as compared to conventional calibration paradigms ( p = 0.0008). ![]() Our results show that the closed-loop calibration paradigms with real-time feedback is more engaging for the pilot. 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. We compared the efficacy of conventional open-loop calibration paradigms with real-time closed-loop paradigms, using pre-trained BCI decoders. ![]() 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. 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 CYBATHLON 2020 BCI race represents an opportunity to further develop BCI design strategies for use in real-time applications with a tetraplegic end user. ![]() 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. 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.
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