A Usability Study of Low-cost Wireless Brain-Computer Interface for Cursor Control Using Online Linear Model

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; Confounding variables; Cursor control; EEG; Imagined Body Kinematics; Usability.

Fig. 1.

Schematic of the EEG-based BCI platform in cursor control task

Fig. 2.

A sample schematic of target acquisition phase. The phase starts with a 5s preparation cue followed by 40 trials, each of which lasts no more than 15s. There is a 2s interval period between the trials. A fixation cue of a blank screen was displayed during the interval period.

Fig. 3.

A sample trial of actual cursor velocity (dashed curve) and predicted velocity from participant Sub1 in a) horizontal and b) vertical directions. Data is based on training phase of the experiment. Vx represents the horizontal velocity, and Vy represents the vertical velocity both in pixel/second.

Fig. 4.

Distribution of GoF of all trials from all participants in (a) horizontal and (b) vertical directions. The total number of trials is 140 in each direction (28 participants and 5 trials each). The GoF has a mean [STD] of 68.98 [27.72] and 42.09 [26.76] in horizontal and vertical directions, respectively.

Fig. 5.

Mean cursor trajectories during the target acquisition phase for participants a) Sub1 and b) Sub2. Mean trajectory is the average length-normalized of the successful trials. Orange, blue, yellow, and purple curves are the mean trajectories corresponding to the left, right, top, and bottom targets, respectively.

Fig. 7.

Boxplot of GoF with respect to self-assessed visualization and imagination ability (Question 1 in Table II). GoF has mean [STD] of 52.13[23.79], 57.71[21.61], and 52.78[31.72] for average, slightly above average, and moderately above average, respectively.

Fig. 8.

Boxplot of hit rate with respect to self-assessed visualization and imagination ability (Question 1 in TableTable II). GoF has mean [STD] of 59.14[19.15], 70.00[14.40], and 80.83[7.64] for average, slightly above average, and moderately above average, respectively.

Fig. 9.

Boxplot of GoF grouped according to the level of attention span (Question 2 in Table II). GoF has mean [STD] of 50.93[24.76], 51.71[22.89], and 64.23[17.30] for below average, average, and above average groups, respectively.

Fig. 10.

Boxplot of hit rate grouped according to the level of attention span (Question 2 in Table II). The hit rate has mean [STD] of 64.64[17.17], 62.47[18.55], and 73.34[15.30] for below average, average, and above average groups, respectively.

Fig. 11.

Boxplot of hit rate with respect to the level of controllability over cursor during hitting all four targets (Question 4 in Table III). The hit rate has a mean [STD] of 64.13[13.59], 66.05[18.14], and 71.18[19.56] for poor, fair, and good, respectively. Details are summarized in Table V

Fig. 12.

Correlation between outcome measures of training (GoF) and target acquisition phases (hit rate) in (a) horizontal direction, (b) vertical direction, and (c) both directions combined. The correlation coefficient is 0.30 in (a), 0.37 in (b), and 0.25 in (c).

Fig. 13.

The schematic of the post-processing pipeline to evaluate the impact of neural/EOG components on to the model predictability

Fig. 14.

Estimated independent components of a) neural activity, b) vertical eye movement, and c) horizontal eye movement from a sample participant.

Fig. 15.

Violin plot of Goodness of Fit (%) for horizontal cursor velocity showing the contribution of different components.

Fig. 16.

Violin plot of Goodness of Fit (%) for vertical cursor velocity showing the contribution of different components.