Controlling a Mouse Pointer with a Single-Channel EEG Sensor

Abstract

(1) 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 pointer.

A Hardware-Based Configurable Algorithm for Eye Blink Signal Detection Using a Single-Channel BCI Headset

“…The detection of eye blinks is currently applied as a human-machine interface (HMI) in many areas of application [9,10]. For instance, in the automotive field, it is used as a safety measure for detecting fatigue [11][12][13] and to prevent possible accidents [14,15].…”

SSVEP-Based Brain-Computer Interface Controlled Robotic Platform With Velocity Modulation

“…Currently, most brain-controlled robotic platforms use discrete movement commands and constant velocity [29]. Studies involving velocity modulation explored the effects of factors, such as attention, distance, and frequency [20], [21], [23]. Differently, this study focused on stimulus brightnessbased velocity modulation.…”

“…In addition to stimulus frequency and distance, voluntary attention increased stimulus-driven EEG activity [22]. Integrating the attention factor, Molina-Cantero et.al [23] utilized SSVEP responses obtained from a single-channel EEG headset known as NeuroSky Mindwave (NM). The NM was able to provide attention levels, which were then used to modulate the speed of a controlled cursor.…”

A Novel Eeg Classification Method for Quantifying Kinematic Parameters

“…Sensorimotor rhythm amplitude has also been used for 2D cursor control [12], robotic arm control [5], however to improve movement, adaptive weights were applied to minimize the difference between actual and predicted target locations during tasks. Molina-Cantero et al [13] controlled a mouse pointer with a single channel EEG. The speed of the mouse pointer was modulated by users' attention level, though the minimum and maximum values were pre-defined.…”