Abstract
Changes in psychological state have been proposed as a cause of variation in brain-computer interface performance, but little formal analysis has been conducted to support this hypothesis. In this study, we investigated the effects of three mental states—fatigue, frustration, and attention—on BCI performance. Twelve able-bodied participants were trained to use a two-class EEG-BCI based on the performance of user-specific mental tasks. Following training, participants completed three testing sessions, during which they used the BCI to play a simple maze navigation game while periodically reporting their perceived levels of fatigue, frustration, and attention. Statistical analysis indicated that there is a significant relationship between frustration and BCI performance while the relationship between fatigue and BCI performance approached significance. BCI performance was 7% lower than average when self-reported fatigue was low and 7% higher than average when self-reported frustration was moderate. A multivariate analysis of mental state revealed the presence of contiguous regions in mental state space where BCI performance was more accurate than average, suggesting the importance of moderate fatigue for achieving effortless focus on BCI control, frustration as a potential motivating factor, and attention as a compensatory mechanism to increasing frustration. Finally, a visual analysis showed the sensitivity of underlying class distributions to changes in mental state. Collectively, these results indicate that mental state is closely related to BCI performance, encouraging future development of psychologically adaptive BCIs.
Highlights
Brain-computer interfaces allow information to be conveyed to an external device, such as a computer, using cognitive activity alone (Mak and Wolpaw, 2009)
More complex arrangements are possible—Liu et al (2012) have shown that attention measured based on NIRS may improve the reliability of an EEG-BCI, while Koo et al (2015) showed that NIRS can be used to detect whether motor imagery has been performed while EEG is used to differentiate different types of motor imagery, allowing the development of a self-paced BCI
This paper investigates the effects of user mental state on BCI performance
Summary
Brain-computer interfaces allow information to be conveyed to an external device, such as a computer, using cognitive activity alone (Mak and Wolpaw, 2009). Envisioned as a means of communication and environmental control for individuals with disabilities (Wolpaw et al, 2002), more and more prospective applications of BCIs have been proposed in recent years for both healthy and disabled individuals. EEG provides a low-resolution spatial map of electrical activity on the cortex (Niedermeyer and da Silva, 2005). Despite this low resolution, it has generally been favored for BCI applications due to its relatively simple setup and low cost. Recent work on hybrid EEG-NIRS BCIs has shown that simultaneous measurement of electrical and hemodynamic activity on the cerebral cortex may allow for more accurate BCI operation by combining features from both modalities (Leamy et al, 2011; Fazli et al, 2012). More complex arrangements are possible—Liu et al (2012) have shown that attention measured based on NIRS may improve the reliability of an EEG-BCI, while Koo et al (2015) showed that NIRS can be used to detect whether motor imagery has been performed while EEG is used to differentiate different types of motor imagery, allowing the development of a self-paced BCI
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