Development of a Practical Visual-Evoked Potential-Based Brain-Computer Interface

There are many different neuromuscular disorders that disrupt the normal communication pathways between the brain and the rest of the body. These diseases often leave patients in a `locked-in state, rendering them unable to communicate with their environment despite having cognitively normal brain function. Brain-computer interfaces (BCIs) are augmentative communication devices that establish a direct link between the brain and a computer. Visual evoked potential (VEP)- based BCIs, which are dependent upon the use of salient visual stimuli, are amongst the fastest BCIs available and provide the highest communication rates compared to other BCI modalities. However. the majority of research focuses solely on improving the raw BCI performance; thus, most visual BCIs still suffer from a myriad of practical issues that make them impractical for everyday use. The focus of this dissertation is on the development of novel advancements and solutions that increase the practicality of VEP-based BCIs. The presented work shows the results of several studies that relate to characterizing and optimizing visual stimuli. improving ergonomic design. reducing visual irritation, and implementing a practical VEP-based BCI using an extensible software framework and mobile devices platforms

Development of a practical and mobile brain-computer communication device for profoundly paralyzed individuals

Thesis (Ph.D.)--Boston UniversityBrain-computer interface (BCI) technology has seen tremendous growth over the past several decades, with numerous groundbreaking research studies demonstrating technical viability (Sellers et al., 2010; Silvoni et al., 2011). Despite this progress, BCIs have remained primarily in controlled laboratory settings. This dissertation proffers a blueprint for translating research-grade BCI systems into real-world applications that are noninvasive and fully portable, and that employ intelligent user interfaces for communication. The proposed architecture is designed to be used by severely motor-impaired individuals, such as those with locked-in syndrome, while reducing the effort and cognitive load needed to communicate. Such a system requires the merging of two primary research fields: 1) electroencephalography (EEG)-based BCIs and 2) intelligent user interface design. The EEG-based BCI portion of this dissertation provides a history of the field, details of our software and hardware implementation, and results from an experimental study aimed at verifying the utility of a BCI based on the steady-state visual evoked potential (SSVEP), a robust brain response to visual stimulation at controlled frequencies. The visual stimulation, feature extraction, and classification algorithms for the BCI were specially designed to achieve successful real-time performance on a laptop computer. Also, the BCI was developed in Python, an open-source programming language that combines programming ease with effective handling of hardware and software requirements. The result of this work was The Unlock Project app software for BCI development. Using it, a four-choice SSVEP BCI setup was implemented and tested with five severely motor-impaired and fourteen control participants. The system showed a wide range of usability across participants, with classification rates ranging from 25-95%. The second portion of the dissertation discusses the viability of intelligent user interface design as a method for obtaining a more user-focused vocal output communication aid tailored to motor-impaired individuals. A proposed blueprint of this communication "app" was developed in this dissertation. It would make use of readily available laptop sensors to perform facial recognition, speech-to-text decoding, and geo-location. The ultimate goal is to couple sensor information with natural language processing to construct an intelligent user interface that shapes communication in a practical SSVEP-based BCI

A brain-machine interface for assistive robotic control

Brain-machine interfaces (BMIs) are the only currently viable means of communication for many individuals suffering from locked-in syndrome (LIS) – profound paralysis that results in severely limited or total loss of voluntary motor control. By inferring user intent from task-modulated neurological signals and then translating those intentions into actions, BMIs can enable LIS patients increased autonomy. Significant effort has been devoted to developing BMIs over the last three decades, but only recently have the combined advances in hardware, software, and methodology provided a setting to realize the translation of this research from the lab into practical, real-world applications. Non-invasive methods, such as those based on the electroencephalogram (EEG), offer the only feasible solution for practical use at the moment, but suffer from limited communication rates and susceptibility to environmental noise. Maximization of the efficacy of each decoded intention, therefore, is critical. This thesis addresses the challenge of implementing a BMI intended for practical use with a focus on an autonomous assistive robot application. First an adaptive EEG- based BMI strategy is developed that relies upon code-modulated visual evoked potentials (c-VEPs) to infer user intent. As voluntary gaze control is typically not available to LIS patients, c-VEP decoding methods under both gaze-dependent and gaze- independent scenarios are explored. Adaptive decoding strategies in both offline and online task conditions are evaluated, and a novel approach to assess ongoing online BMI performance is introduced. Next, an adaptive neural network-based system for assistive robot control is presented that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. Exploratory learning, or “learning by doing,” is an unsupervised method in which the robot is able to build an internal model for motor planning and coordination based on real-time sensory inputs received during exploration. Finally, a software platform intended for practical BMI application use is developed and evaluated. Using online c-VEP methods, users control a simple 2D cursor control game, a basic augmentative and alternative communication tool, and an assistive robot, both manually and via high-level goal-oriented commands