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

Applying Fitts’ Law to Gesture Based Computer Interactions

abstract: As gesture interfaces become more main-stream, it is increasingly important to investigate the behavioral characteristics of these interactions – particularly in three-dimensional (3D) space. In this study, Fitts’ method was extended to such input technologies, and the applicability of Fitts’ law to gesture-based interactions was examined. The experiment included three gesture-based input devices that utilize different techniques to capture user movement, and compared them to conventional input technologies like touchscreen and mouse. Participants completed a target-acquisition test and were instructed to move a cursor from a home location to a spherical target as quickly and accurately as possible. Three distances and three target sizes were tested six times in a randomized order for all input devices. A total of 81 participants completed all tasks. Movement time, error rate, and throughput were calculated for each input technology. Results showed that the mean movement time was highly correlated with the target's index of difficulty for all devices, providing evidence that Fitts’ law can be extended and applied to gesture-based devices. Throughputs were found to be significantly lower for the gesture-based devices compared to mouse and touchscreen, and as the index of difficulty increased, the movement time increased significantly more for these gesture technologies. Error counts were statistically higher for all gesture-based input technologies compared to mouse. In addition, error counts for all inputs were highly correlated with target width, but little impact was shown by movement distance. Overall, the findings suggest that gesture-based devices can be characterized by Fitts’ law in a similar fashion to conventional 1D or 2D devices

RubberEdge: Reducing Clutching by Combining Position and Rate Control with Elastic Feedback

Position control devices enable precise selection, but significant clutching degrades performance. Clutching can be reduced with high control-display gain or pointer acceleration, but there are human and device limits. Elastic rate control eliminates clutching completely, but can make precise selection difficult. We show that hybrid position-rate control can outperform position control by 20% when there is significant clutching, even when using pointer acceleration. Unlike previous work, our RubberEdge technique eliminates trajectory and velocity discontinuities. We derive predictive models for position control with clutching and hybrid control, and present a prototype RubberEdge position-rate control device including initial user feedback.Comment: 10 page

Ability-Based Methods for Personalized Keyboard Generation

This study introduces an ability-based method for personalized keyboard generation, wherein an individual's own movement and human-computer interaction data are used to automatically compute a personalized virtual keyboard layout. Our approach integrates a multidirectional point-select task to characterize cursor control over time, distance, and direction. The characterization is automatically employed to develop a computationally efficient keyboard layout that prioritizes each user's movement abilities through capturing directional constraints and preferences. We evaluated our approach in a study involving 16 participants using inertial sensing and facial electromyography as an access method, resulting in significantly increased communication rates using the personalized keyboard (52.0 bits/min) when compared to a generically optimized keyboard (47.9 bits/min). Our results demonstrate the ability to effectively characterize an individual's movement abilities to design a personalized keyboard for improved communication. This work underscores the importance of integrating a user's motor abilities when designing virtual interfaces.Comment: 20 pages, 7 figure

Not Just Pointing: Shannon's Information Theory as a General Tool for Performance Evaluation of Input Techniques

This article was submitted to the ACM CHI conference in September 2017, and rejected in December 2017. It is currently under revision.Input techniques serving, quite literally, to allow users to send information to the computer, the information theoretic approach seems tailor-made for their quantitative evaluation. Shannon's framework makes it straightforward to measure the performance of any technique as an effective information transmission rate, in bits/s. Apart from pointing, however, evaluators of input techniques have generally ignored Shannon, contenting themselves with less rigorous methods of speed and accuracy measurements borrowed from psychology. We plead for a serious consideration in HCI of Shannon's information theory as a tool for the evaluation of all sorts of input techniques. We start with a primer on Shannon's basic quantities and the theoretical entities of his communication model. We then discuss how the concepts should be applied to the input techniques evaluation problem. Finally we outline two concrete methodologies, one focused on the discrete timing and the other on the continuous time course of information gain by the computer

Un conjunto de datos y método de post-procesamiento para evaluación de interfaz humano-máquina de dispositivos de tipo apuntador

The evaluation of human-machine interfaces (HMI) requires quantitative metrics to define the ability of a person to effectively achieve their goals using the HMI. In particular, for pointing-device type HMIs such as the computer mouse, an experiment quantifying movement by performing repetitive target selections allows defining a useful metric known as throughput (TP) using the Fitts' Law test. In this work, a dataset obtained from an automated protocol application is presented, which is made publicly available through an on-line platform. A post-processing method to obtain performance parameters from the dataset is also presented, and its output is used to validate the data against similar experiments in the literature.La evaluation de interfaces humano-maquina (HMI) requiere métricas cuantitativas para definir la capacidad de una persona para lograr eficazmente sus objetivos utilizando la HMI. En particular, para las HMIs del tipo dispotivo apuntador como el ratón de la computadora, un experimento que cuatifica el movimiento al realizar selecciones repetitivas de objetivos permite definir una métrica util conocida como rendimiento (TP) utilizando la prueba de la Ley de Fitts. En este trabajo, se presenta un conjunto de datos obtenido a partir de la aplicación de un protocolo automatizado, el cual esta disponible publicamente a través de una plataforma en línea. También se presenta un método de post-procesamiento para obtener parámetros de rendimiento a partir del conjunto de datos, y sus resultados se utilizan para validar los datos en comparación con experimentos similares en la literatura.Facultad de Informátic

Evaluating Human Performance for Image-Guided Surgical Tasks

The following work focuses on the objective evaluation of human performance for two different interventional tasks; targeted prostate biopsy tasks using a tracked biopsy device, and external ventricular drain placement tasks using a mobile-based augmented reality device for visualization and guidance. In both tasks, a human performance methodology was utilized which respects the trade-off between speed and accuracy for users conducting a series of targeting tasks using each device. This work outlines the development and application of performance evaluation methods using these devices, as well as details regarding the implementation of the mobile AR application. It was determined that the Fitts’ Law methodology can be applied for evaluation of tasks performed in each surgical scenario, and was sensitive to differentiate performance across a range which spanned experienced and novice users. This methodology is valuable for future development of training modules for these and other medical devices, and can provide details about the underlying characteristics of the devices, and how they can be optimized with respect to human performance

The Challenges in Modeling Human Performance in 3D Space with Fitts’ Law

With the rapid growth in virtual reality technologies, object interaction is becoming increasingly more immersive, elucidating human perception and leading to promising directions towards evaluating human performance under different settings. This spike in technological growth exponentially increased the need for a human performance metric in 3D space. Fitts' law is perhaps the most widely used human prediction model in HCI history attempting to capture human movement in lower dimensions. Despite the collective effort towards deriving an advanced extension of a 3D human performance model based on Fitts' law, a standardized metric is still missing. Moreover, most of the extensions to date assume or limit their findings to certain settings, effectively disregarding important variables that are fundamental to 3D object interaction. In this review, we investigate and analyze the most prominent extensions of Fitts' law and compare their characteristics pinpointing to potentially important aspects for deriving a higher-dimensional performance model. Lastly, we mention the complexities, frontiers as well as potential challenges that may lay ahead.Comment: Accepted at ACM CHI 2021 Conference on Human Factors in Computing Systems (CHI '21 Extended Abstracts