3 research outputs found
Quadcopter Flight Control Using a Non-invasive Multi-Modal Brain Computer Interface
Brain-Computer Interfaces (BCIs) translate neuronal information into commands to control external software or hardware, which can improve the quality of life for both healthy and disabled individuals. Here, a multi-modal BCI which combines motor imagery (MI) and steady-state visual evoked potential (SSVEP) is proposed to achieve stable control of a quadcopter in three-dimensional physical space. The complete information common spatial pattern (CICSP) method is used to extract two MI features to control the quadcopter to fly left-forward and right-forward, and canonical correlation analysis (CCA) is employed to perform the SSVEP classification for rise and fall. Eye blinking is designed to switch these two modes while hovering. Real-time feedback is provided to subjects by a global camera. Two flight tasks were conducted in physical space in order to certify the reliability of the BCI system. Subjects were asked to control the quadcopter to fly forward along the zig-zag pattern to pass through a gate in the relatively simple task. For the other complex task, the quadcopter was controlled to pass through two gates successively according to an S-shaped route. The performance of the BCI system is quantified using suitable metrics and subjects are able to acquire 86.5% accuracy for the complicated flight task. It is demonstrated that the multi-modal BCI has the ability to increase the accuracy rate, reduce the task burden, and improve the performance of the BCI system in the real world
Controlling a Mouse Pointer with a Single-Channel EEG Sensor
Goals: The purpose of this study was to analyze the feasibility of using the information
obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer.
We used a low-cost headset, with one dry sensor placed at the FP1 position, to steer a mouse
pointer and make selections through a combination of the user’s attention level with the detection of
voluntary blinks. There are two types of cursor movements: spinning and linear displacement. A
sequence of blinks allows for switching between these movement types, while the attention level
modulates the cursor’s speed. The influence of the attention level on performance was studied.
Additionally, Fitts’ model and the evolution of the emotional states of participants, among other
trajectory indicators, were analyzed. (2) Methods: Twenty participants distributed into two groups
(Attention and No-Attention) performed three runs, on different days, in which 40 targets had
to be reached and selected. Target positions and distances from the cursor’s initial position were
chosen, providing eight different indices of difficulty (IDs). A self-assessment manikin (SAM)
test and a final survey provided information about the system’s usability and the emotions of
participants during the experiment. (3) Results: The performance was similar to some brain–computer
interface (BCI) solutions found in the literature, with an averaged information transfer rate (ITR)
of 7 bits/min. Concerning the cursor navigation, some trajectory indicators showed our proposed
approach to be as good as common pointing devices, such as joysticks, trackballs, and so on. Only
one of the 20 participants reported difficulty in managing the cursor and, according to the tests, most
of them assessed the experience positively. Movement times and hit rates were significantly better for
participants belonging to the attention group. (4) Conclusions: The proposed approach is a feasible
low-cost solution to manage a mouse pointe