A brain-computer interface is a device that uses signals recorded from the brain to directly control a computer. In the last few years, P300-based brain-computer interfaces (BCIs) have proven an effective and reliable means of communication for people with severe motor disabilities such as amyotrophic lateral sclerosis (ALS). Despite this fact, relatively few individuals have benefited from currently available BCI technology. Independent BCI use requires easily acquired, good-quality electroencephalographic (EEG) signals maintained over long periods in less-than-ideal electrical environments. Conventional, wet-sensor, electrodes require careful application. Faulty or inadequate preparation, noisy environments, or gel evaporation can result in poor signal quality. Poor signal quality produces poor user performance, system downtime, and user and caregiver frustration. This study demonstrates that a hybrid dry electrode sensor array (HESA) performs as well as traditional wet electrodes and may help propel BCI technology to a widely accepted alternative mode of communication.

An EEG-based brain-computer system can be used to control external devices such as computers, wheelchairs or Virtual Environments. One of the most important applications is a spelling device to aid severely disabled individuals with communication, for example people disabled by amyotrophic lateral sclerosis (ALS). P300-based BCI systems are optimal for spelling characters with high speed and accuracy, as compared to other BCI paradigms such as motor imagery. In this study, 100 subjects tested a P300-based BCI system to spell a 5-character word with only 5 min of training. EEG data were acquired while the subject looked at a 36-character matrix to spell the word WATER. Two different versions of the P300 speller were used: (i) the row/column speller (RC) that flashes an entire column or row of characters and (ii) a single character speller (SC) that flashes each character individually. The subjects were free to decide which version to test. Nineteen subjects opted to test both versions. The BCI system classifier was trained on the data collected for the word WATER. During the real-time phase of the experiment, the subject spelled the word LUCAS, and was provided with the classifier selection accuracy after each of the five letters. Additionally, subjects filled out a questionnaire about age, sex, education, sleep duration, working duration, cigarette consumption, coffee consumption, and level of disturbance that the flashing characters produced. 72.8% (N = 81) of the subjects were able to spell with 100% accuracy in the RC paradigm and 55.3% (N = 38) of the subjects spelled with 100% accuracy in the SC paradigm. Less than 3% of the subjects did not spell any character correctly. People who slept less than 8 h performed significantly better than other subjects. Sex, education, working duration, and cigarette and coffee consumption were not statistically related to differences in accuracy. The disturbance of the flashing characters was rated with a median score of 1 on a scale from 1 to 5 (1, not disturbing; 5, highly disturbing). This study shows that high spelling accuracy can be achieved with the P300 BCI system using approximately 5 min of training data for a large number of non-disabled subjects, and that the RC paradigm is superior to the SC paradigm. 89% of the 81 RC subjects were able to spell with accuracy 80-100%. A similar study using a motor imagery BCI with 99 subjects showed that only 19% of the subjects were able to achieve accuracy of 80-100%. These large differences in accuracy suggest that with limited amounts of training data the P300-based BCI is superior to the motor imagery BCI. Overall, these results are very encouraging and a similar study should be conducted with subjects who have ALS to determine if their accuracy levels are similar.

The objective of this study was to investigate the performance of 3 brain-computer interface (BCI) paradigms in an amyotrophic lateral sclerosis (ALS) population (n = 11). Using a repeated-measures design, participants completed 3 BCI conditions: row/column (RCW), checkerboard (CBW), and gray-to-color (CBC). Based on previous studies, it is hypothesized that the CBC and CBW conditions will result in higher accuracy, information transfer rate, waveform amplitude, and user preference over the RCW condition. An offline dynamic stopping simulation will also increase information transfer rate. Higher mean accuracy was observed in the CBC condition (89.7%), followed by the CBW (84.3%) condition, and lowest in the RCW condition (78.7%); however, these differences did not reach statistical significance (P =.062). Eight of the eleven participants preferred the CBC and the remaining three preferred the CBW conditions. The offline dynamic stopping simulation significantly increased information transfer rate (P =.005) and decreased accuracy (P <.000). The findings of this study suggest that color stimuli provide a modest improvement in performance and that participants prefer color stimuli over monochromatic stimuli. Given these findings, BCI paradigms that use color stimuli should be considered for individuals who have ALS.

Objective Current Brain-Computer Interface (BCI) systems typically flash an array of items from grey to white (GW). The objective of this study was to evaluate BCI performance using uniquely colored stimuli. Methods In addition to the GW stimuli, the current study tested two types of color stimuli (grey to color [GC] and color intensification [CI]). The main hypotheses were that in a checkboard paradigm, unique color stimuli will: (1) increase BCI performance over the standard GW paradigm; (2) elicit larger event-related potentials (ERPs); and, (3) improve offline performance with an electrode selection algorithm (i.e., Jumpwise). Results Online results (n = 36) showed that GC provides higher accuracy and information transfer rate than the CI and GW conditions. Waveform analysis showed that GC produced higher amplitude ERPs than CI and GW. Information transfer rate was improved by the Jumpwise-selected channel locations in all conditions. Conclusions Unique color stimuli (GC) improved BCI performance and enhanced ERPs. Jumpwise-selected electrode locations improved offline performance. Significance These results show that in a checkerboard paradigm, unique color stimuli increase BCI performance, are preferred by participants, and are important to the design of end-user applications; thus, could lead to an increase in end-user performance and acceptance of BCI technology.

It was suggested several decades ago that computers would be the single biggest step forward in integrating people with physical disabilities into "normal" society. At that stage, much work was done in writing software and designing hardware that allowed computer operators with disabilities to use packages effectively, in certain cases as efficiently as people without disabilities. Since those days, judging by the lack of references on this subject the interest in dealing with disabled people has waned. It is only very recently that the spotlight has been focused on these potentially very productive persons. Unfortunately, the backlog is large and most existing applications software offers little or no support for users with disabilities. In this thesis, I have examined some of the hardware and software limitations of current desktop computer technology, focusing on the IBM PC and compatibles. I have also written a computer program that attempts to relieve some of the difficulties faced by a limited number of disabled users. In evaluating the results, I considered it important to relate the ensuing data with the real problems faced by a far wider spectrum of users than I attempted to cater for with the program and to suggest ways in which software products could be made to have wider applicability in the future.

This thesis project presents a research through design process that has aimed to explore how we can design interactive technologies that can support mindfulness practices in beginners. This has been done by designing a meditative lamp, Lumina, which can be controlled by the user’s neural oscillations. The user interacts with the lamp by changing alpha and beta brain waves levels through meditation. The prototype has been evaluated with four participants and the testing sessions had three stages: a preliminary questionnaire to learn about participants' mindfulness level, interaction with my prototype providing the opportunity to explore mindfulness and electroencephalography (EEG), and a post-interview to reflect on their experience with the prototype. The biggest challenge of this project was to support mindfulness without disrupting the practice. Lumina has been developed through an iterative process, guided by relevant literature and works in the field of meditation, neurology, and calm technology.

The aim of this work is to design and implement a novel computer interface based on detection and tracking of a hand in an image from a single camera. The created interface doesn't require any special hardware and it is possible to use it on a common computer with standard web-camera. The implemented interface was used to create an application, which is able to synthesize keyboard and mouse input events and this way it is able to control existing programs without the need to change their source code. Another contribution of this work is a novel method of automatic data acquisition for training of hand detectors. By using this method it is possible to collect thousands of training examples in a few hours.

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The goal of study 1 was to use a remote eye tracker to understand how eye movements change with 7 geometrically varied remote controls to determine design element saliency. 20 participants were used to measure the following eye metrics: number of fixations prior to first fixation of any AOI, time to first fixation of an AOI, number of fixations on an AOI, dwell time of the first fixation on an AOI, total dwell time of an AOI, and the percentage of time spent on an AOI. The results of the study showed that all participants spent between 75-85% of their time fixated on the button layout which was not defined as an AOI. No statistical differences were found in the values measured for all eye tracking metrics across similarly defined AOIs. In study 2, the objective was to determine attitudes towards appearance and usability of the 7 remote control designs using the participants from study 1. Participants were asked to rate their attitudes and preferences, using a Likert-based questionnaire, about the qualities of appearance and usability for the attributes of proportion, shape, and configuration. They were asked open-ended questions about their likes and dislikes regarding the qualities of appearance and usability. Lastly, participants were given a pairwise comparison survey where they chose their preferred remote design, based on appearance, for 10 paired sets of contrasting remote designs. The hourglass subjacent and hourglass round designs were rated highest for appearance and usability from the Likert questionnaire. The hourglass round design was ranked highest for the pairwise comparison survey. For study 3, the goal was to determine attitudes towards appearance and usability of the 7 remote designs with online participants. 300 participants were asked to rate their attitudes and preferences using the same Likert-based questionnaire from study 2. They were asked the same open-ended questions and administered the same pairwise comparison survey as in study 2. The results of the Likert questionnaire showed that the hourglass subjacent and hourglass round designs were rated highest for appearance and usability. From the pairwise comparison survey, the hourglass round design was ranked the highest.

Practicing Motor Imagery (MI) with a Brain-Computer Interface (BCI) has shown promise in promoting motor recovery in stroke patients. A BCI records a person’s brain activity and provides feedback to the person in real time, which allows the person to practice his or her brain activity. By imagining a movement (performing MI) such as gripping with their hand, cortical areas in the brain are activated that largely overlaps with those activated during the actual hand movement. A BCI can provide positive feedback when the hand-related cortical areas are activated during MI, which helps a person to learn how to perform MI. Despite evidence that stroke patients may recover some motor function from practicing MI with BCI feedback thanks to the feedback provided from a BCI, the effectiveness and reliability of BCI-based rehabilitation are still poor.  A BCI can detect MI by analyzing patterns of features from the brain activity. The most common features are extracted from the oscillatory activity in the brain.  In BCI research, MI is often treated as a static pattern of features, which is detected by using machine learning algorithms to assign activity into a binary state. However, this model of MI may be inaccurate. Analyzing brain activity as dynamically varying over time and with a continuous measure of strength could better represent the cortical activity related to MI.  In this Licentiate thesis, I explore a method for analyzing the temporal dynamic of MI-activity with a continuous measure of strength. Brain activity was recorded with electroencephalography (EEG) and subject-specific feature patterns were extracted from a group of healthy subjects while they performed MI of two opposing hand movements: opening and closing the hand. Although MI of the two same-hand movements could not be discriminated, the continuous output from a machine learning algorithm was shown to correlate well with MI-related feature patterns. The temporal analysis also revealed that MI is dynamically encoded early, but later stabilizes into a more static pattern of brain activity. Last, to accommodate for higher temporal resolution of MI, I designed and evaluated a BCI framework by its feedback delay and uncertainty as a function of the stress on the system and found a non-linear correlation. These results could be essential for developing a BCI with time-critical feedback. To summarize, in this Licentiate thesis I propose a promising method for analyzing and extracting a temporal representation of MI, enabling relevant and continuous neurofeedback which may contribute to clinical advances in BCI-based stroke rehabilitation.