Paste

Abstract Computer cursor control using electroencephalogram (EEG) signals is a common and well-studied brain-computer interface (BCI). The emphasis of the literature has been primarily on evaluation of the objective measures of assistive BCIs such as accuracy of the neural decoder whereas the subjective measures such as user’s satisfaction play an essential role for the overall success of a BCI. As far as we know, the BCI literature lacks a comprehensive evaluation of the usability of the mind-controlled computer cursor in terms of decoder efficiency (accuracy), user experience, and relevant confounding variables concerning the platform for the public use. To fill this gap, we conducted a two-dimensional EEG-based cursor control experiment among 28 healthy participants. The computer cursor velocity was controlled by the imagery of hand movement using a paradigm presented in the literature named imagined body kinematics (IBK) with a low-cost wireless EEG headset. We evaluated the usability of the platform for different objective and subjective measures while we investigated the extent to which the training phase may influence the ultimate BCI outcome. We conducted pre- and post- BCI experiment interview questionnaires to evaluate the usability. Analyzing the questionnaires and the testing phase outcome shows a positive correlation between the individuals’ ability of visualization and their level of mental controllability of the cursor. Despite individual differences, analyzing training data shows the significance of electrooculogram (EOG) on the predictability of the linear model. The results of this work may provide useful insights towards designing a personalized user-centered assistive BCI.

Keywords: Brain-Computer Interface, Cursor control, EEG, Usability, Confounding variables, Imagined Body Kinematics Go to: I. INTRODUCTION A brain-controlled computer cursor has been utilized as a testbed for developing assistive BCIs. Many BCI systems have been designed to harness the computer cursor using noninvasive brain imaging techniques such as electroencephalogram (EEG) [1, 2]. Several intention-driven (endogenous) BCI paradigms have been established based on various EEG monitoring techniques, including motor imagery (MI) [1, 2], and imagined body kinematics (IBK) [3–5]. While MI encodes brainwaves over the sensorimotor area on alpha and beta frequency bands, IBK encodes the associated motor activity in temporal fluctuations of EEG signals. There is a vast BCI literature revolving around sensorimotor rhythm (SMR) based motor imagery. For instance, Bai et al. [6] designed a two-directional and a four-directional cursor control BCI systems using motor execution and motor imagery paradigms. They achieved an average accuracy of 88% and 80% in a two-dimensional control task and 58% and 45% for a four-dimensional control task using motor execution and motor imagery, respectively.

Despite its successes, MI-based assistive BCIs are prone to two main shortcomings. First, the user’s training takes days, if not weeks, to happen. Learning to modulate the brain activity in the frequency bands/brain areas of interest on particular brain areas is often long. For instance, it might take days for users to move a computer cursor with adequate accuracy [1, 2, 7]. Second, MI paradigm is not considered a natural way of control [3] since there is not necessarily a correlated kinematics between motor execution (or imagination) and the controlled object [8]. The discrepancy may add more to the potential user’s frustration and affect the overall acceptance of the assistive BCI. This article is one of the few studies that investigate the capability of BCI using imagined body kinematics.

Several studies have witnessed that IBK paradigm may help to reduce the duration of training [9]. Bradberry et al. [3] studied an EEG-based BCI paradigm that maps natural imagery movement of the dominant hand onto the velocity of a computer cursor in two-dimensional space. The results showed that users could acquire acceptable controllability over the cursor within less than an hour of user’s training. Ofner et al. exploited the IBK paradigm in a trial-based experiment [10], and Kim et al. compared the efficacy of the paradigm to control the trajectory of a robotic arm using different classifiers with execution and imagination of different body parts [11]. The imagined kinematics can be extracted from low-frequency EEG signals (usually less than 1Hz) [3, 4, 10, 11]. A similar approach has been applied in invasive BCI studies where participants with implanted electrodes were able to maintain high controllability over a computer cursor [12].

A few previous studies gave evidence that among different measures of upper limb body kinematics (position, velocity, acceleration, etc.), velocity may be easier to determine from neural activities, and may attain a more robust predictive measure in both offline and real-time applications [3, 13–15]. Fo…