Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair

Abstract

The research on the electroencephalography (EEG)-based brain–computer interface (BCI) is widely utilized for wheelchair control. The ability of the user is one factor of BCI efficiency. Therefore, we focused on BCI tasks and protocols to yield high efficiency from the robust EEG features of individual users. This study proposes a task-based brain activity to gain the power of the alpha band, which included eyes closed for alpha response at the occipital area, attention to an upward arrow for alpha response at the frontal area, and an imagined left/right motor for alpha event-related desynchronization at the left/right motor cortex. An EPOC X neuroheadset was used to acquire the EEG signals. We also proposed user proficiency in motor imagery sessions with limb movement paradigms by recommending motor imagination tasks. Using the proposed system, we verified the feature extraction algorithms and command translation. Twelve volunteers participated in the experiment, and the conventional paradigm of motor imagery was used to compare the efficiencies. With utilized user proficiency in motor imagery, an average accuracy of 83.7% across the left and right commands was achieved. The recommended MI paradigm via user proficiency achieved an approximately 4% higher accuracy than the conventional MI paradigm. Moreover, the real-time control results of a simulated wheelchair revealed a high efficiency based on the time condition. The time results for the same task as the joystick-based control were still approximately three times longer. We suggest that user proficiency be used to recommend an individual MI paradigm for beginners. Furthermore, the proposed BCI system can be used for electric wheelchair control by people with severe disabilities.

Brain–Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control

“…The proposed SSVEP-based BCI (brain control) was approximately three times longer than keyboard control (hand control). Compared with MI-based BCI [ 56 ] and the face–machine interface (FMI) [ 57 ] systems for simulated wheelchair control, the proposed SSVEP-based BCI system can yield a speed rate of 9.71 cm/s (ratio between distance and the average time taken), which is higher than the MI modality of 7.11 cm/s. However, still lower than the FMI modality was 12.03 cm/s.…”

State-of-the-Art on Brain-Computer Interface Technology

“…Neurogadgets, ranging from moving robotic spiders and balls to more practical applications, are increasingly being used for entertainment purposes. However, what is more important is that neurogadgets are also being developed to assist people with disabilities, such as those with paralysis of the limbs [5][6][7][8][9][10].…”