Melody Moore Jackson

Towards an Independent Brain - Computer Interface Using Steady State Visual Evoked Potentials

OBJECTIVE Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze. METHODS Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs. RESULTS Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control. CONCLUSIONS The data demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCIs dependence on muscle activity is a function of many factors, including the display, task, environment, and user. SIGNIFICANCE SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.

Applications for Brain-Computer Interfaces

Brain-computer Interfaces (BCIs) have been studied for nearly thirty years, with the primary motivation of providing assistive technologies for people with very severe motor disabilities. The slow speeds, high error rate, susceptibility to artifact, and complexity of BCI systems have been challenges for implementing workable real-world systems. However, recent advances in computing and bio-sensing technologies have improved the outlook for BCI applications, making them promising not only as assistive technologies but also for mainstream applications. This chapter presents a survey of applications for BCI systems, both historical and recent, in order to characterize the broad range of possibilities for neural control.

Individual Characteristics and Their Effect on Predicting Mu Rhythm Modulation

Brain–computer interfaces (BCIs) offer users with severe motor disabilities a nonmuscular input channel for communication and control but require that users achieve a level of literacy and be able to harness their appropriate electrophysiological responses for effective use of the interface. There is currently no formalized process for determining a users aptitude for control of various BCIs without testing on an actual system. This study presents how basic information captured about users may be used to predict modulation of mu rhythms, electrical variations in the motor cortex region of the brain that may be used for control of a BCI. Based on data from 55 able-bodied users, we found that the interaction of age and daily average amount of hand-and-arm movement by individuals correlates to their ability to modulate mu rhythms induced by actual or imagined movements. This research may be expanded into a more robust model linking individual characteristics and control of various BCIs.

An fNIR based BMI for letter construction using continuous control

A long term goal of assistive technology research is to build creative expression applications where subjects can extemporaneously express themselves. Sketch drawing is one form of creative expression. In this work, we demonstrate the usability of a brain-machine interface (BMI) for expression using a letter drawing task. We describe empirical results that represent a first step toward assistive applications for creative expression.

Continuous Control Paradigms for Direct Brain Interfaces

Direct Brain Interfaces (DBIs) offer great possibilities for people with severe disabilities to communicate and control their environments. However, many DBI systems implement discrete selection, such as choosing a letter from an alphabet, which offers limited control over certain tasks. Continuous control is important for applications such as driving a wheelchair or drawing for creative expression. This paper describes two projects currently underway at the Georgia Tech BrainLab exploring continuous control interface paradigms for an EEG-based approach centered on responses from visual cortex, and functional near Infrared (fNIR) imaging of the language center of the brain.

Current Trends in Brain–Computer Interface (BCI) Research and Development

A brain–computer interface (BCI), sometimes called a direct neural interface or a brain–machine interface, detects and interprets brain signals and uses the results to communicate a user’s intent (Wolpaw, Birbaumer, McFarland, Pfurtscheller, & Vaughan, 2002). Because these systems directly translate brain activity into action, without depending on peripheral nerves and muscles, a major goal of BCI research has been to establish BCI technology as an assistive device to be used by people with severe motor disabilities. BCIs have shown encouraging possibilities in providing people, including those who cannot use their muscles but are cognitively intact, with alternative methods for interacting with the outside world (Nam, Lee, & Johnson, 2010; Schalk, McFarland, Hinterberger, Birbaumer, & Wolpaw, 2004). Despite long interest in the possibility to control devices directly using brain signals (e.g., Fetz & Finocchio, 1971; Vidal, 1973, 1977), it has only been in the past 20 years that sustained research has begun, and only in the past 10 years that a recognizable field of BCI research, populated by a rapidly growing number of research groups with increasing number of publications, has developed. Early BCI efforts have been developed from the field of clinical neurophysiology in humans (using mostly scalp-recorded electroencephalography [EEG]) and basic neuroscience investigations in animals (using mostly single-neuron recordings). Thus, initial efforts began with expertise in neuroscience, neurophysiology, and psychology. In parallel with the establishment of dedicated BCI research groups throughout the 1990s, these groups began to also seek specialists in signal processing, machine learning, and software engineering. This relatively narrow focus on the technical aspects of BCI research and development served the field well in its initial stages of method development. As the field has begun to mature, its scope has expanded to focus on application of BCI technology to the needs of people with disabilities—recent efforts are now

Designing pervasive brain-computer interfaces

The following paper reports on a prototype Brain-computer Interface designed for pervasive control by paralyzed users. Our study indicates that current control and communication devices for users with severe physical disabilities restrict control and independence, offer little articulation and communication capabilities. Integrating multiple devices and services, our application is based on the functional Near-Infrared Imaging technology. Based on the overarching Value-sensitive design framework, our solution is informed by the usage patterns of technology, living habits and daily activities of the disabled users. By designing the context-aware pervasive control solution, we create a venue for communication, environmental control, recreation, assistance and expression among physically disabled patients. The evaluations results of the prototype are also discussed.

Neural Control Interfaces

The control interface is the primary component of a Brain-Computer Interface (BCI) system that provides user interaction. The control interface supplies cues for performing mental tasks, reports system status and task feedback, and often displays representations of the user’s brain signals. Control interfaces play a significant role in determining the usability of a BCI, and some of the traditional human-computer interaction design methods apply. However, the very specialized input methods and display paradigms of a BCI require consideration to create optimal usability for a BCI system. This chapter outlines some of the issues and challenges that make designing control interfaces for BCIs unique.

A visual feedback design based on a brain-computer interface to assist users regulate their emotional state

In situations where there is a stressful workload or when unexpected things occur, people often find it difficult to regulate their emotions. To assist them in effective regulation, this design utilizes neurofeedback, providing users real-time emotion feedback to augment their emotional states through the use of a tangible interface. The visual feedback incorporates a series of colored LEDs that map an individuals affective state. This user study is structured to examine the effect of this training tool in a lab setting. The users are asked to watch several video clips to evoke an agitated state and then try to be calm by using this device. The results will be compared to the users ability to regulate their emotions without any visual tools. The longer term goal of this project is to develop a training tool, to teach people how to regulate their emotions more effectively in stressful situations.

Optimal Control Strategies for an SSVEP-Based Brain-Computer Interface

We evaluate the performance of 18 healthy subjects on a steady-state visually evoked potential brain–computer interface (BCI) under variation of two general control parameters. The BCI is a simple game amenable to performance measures such as the bitrate, decision accuracy, and optimality ratios based on an ideal human–machine system. The two parameters studied are the electroencephalography recording history length used to form a decision and the number of consecutive identical decisions that must be recognized before feedback is provided. To maximize the bitrate, it appears optimal to minimize the number of consecutive identical decisions required for feedback. When the task of interest often requires making the same decision multiple times in a row, a larger history of data seems preferable. When good performance on a task demands that decisions change rapidly, a smaller history seems optimal. Ultimately, we plan to connect this work to choosing appropriate control parameters for efficient wheelchair control by a BCI.