Human Behavior Integration Improves Classification Rates in Real-Time BCI

Abstract

Brain-computer interfaces (BCI) offer potential for individuals with a variety of motor and sensory disabilities to interact with their environment, communicate and control mobility aids. Two key factors which affect the performance of a BCI and its usability are the feedback given to the participant and the subject's motivation. This paper presents the results from a study investigating the effects of feedback and motivation on the performance of the Strathclyde Brain Computer Interface. The paper discusses how the performance of the system can be improved by behavior integration and human-in-the-loop design.

Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework

“…Recent research on motor control and neuroprosthestics provide convincing data that control theoretic principles can be successfully applied to model brain and behavior. At the most basic level, augmenting error-feedback proportionally improves the speed of both visuomotor (Patton et al, 2013 ) and BCI (Grychtol et al, 2010 ) adaptive learning. Remarkably, predictions from advanced models based on optimal control can match experimental data at both the behavioral (Todorov, 2004 ; Nagengast et al, 2009 ) and the neural level (Héliot et al, 2010 ).…”

“…Conversely, a thermostat without sensory access to the actual parameter of temperature, and irrespective of how complicated its internal model(s) of the environment may be, would quickly accumulate errors and eventually bring about a very large temperature drift. Given evidence that the brain respects control theoretic principles (Todorov, 2004 ; Marken, 2009 ; Grychtol et al, 2010 ), it is reasonable to hypothesize that the direct sensing accomplished by a BCI enables control of specific brain oscillations that might otherwise fall outside the scope of conscious awareness. Therefore, the first advantage of NFB may be to quite literally enlarge the cerebral sensorium , and thereby enable implicit control of covert brain activity that may have no direct behavioral correlate(s), e.g., activity associated with auditory hallucinations (McCarthy-Jones, 2012 ).…”

“…Recent work points to an intrinsic (neural) source of behavioral variability that may underlie an animal’s attempts to “find” the appropriate behavior (Heisenberg et al, 2001 ). As a result, we propose that NFB learning, whether it be continuous or intermittent, may be better conceptually formulated by control-theoretic closed-loop models (Todorov, 2004 ; Marken, 2009 ; Grychtol et al, 2010 ). In practice, this can be condensed to the following sequence of events: initially the fluctuating feedback signal reflects stochastic (i.e., unconditioned) neural variability (Legenstein et al, 2010 ), consequently on random occasions this neural variability will infrequently generate activity that will meet the threshold for reward (i.e., which represents zero feedback-error); upon presentation of the sensory cue/reward, the brain may then “memorize” the distinct neural/behavioral state as an internal set-point, by releasing a reward-modulated signal for synaptic plasticity, e.g., dopamine (Legenstein et al, 2008 ).…”

Combining BCI with Virtual Reality: Towards New Applications and Improved BCI

“…Another BCI controlled wheelchair study was performed by Grychtol et al [25] with healthy users. Their results confirmed how voluntary behavioral modification brought about by VR feedback can help to improve the performance of a BCI system.…”

“…Furthermore, characteristic EEG changes during VE conditions were reported in [56], where a dominant ERS pattern which was permanently present in the CAVE, was less pronounced in the HMD and not existing at all in the normal feedback. Nevertheless, it was presented in Section 3.1 that VR improves the BCI performance; either the users achieved their best results within VR [40,56,25] compared to normal feedback or even 100 % performance result in VR [39] or achieved the lowest error with virtual feedback [41]. Generally it can be stated that VR feedback amplifies both positive and negative feedback effects on the performance.…”