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.

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.

Objective An electroencephalographic brain–computer interface (BCI) can provide a non-muscular means of communication for people with amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. We present a novel P300-based BCI stimulus presentation – the checkerboard paradigm (CBP). CBP performance is compared to that of the standard row/column paradigm (RCP) introduced by Farwell and Donchin (1988). Methods Using an 8 × 9 matrix of alphanumeric characters and keyboard commands, 18 participants used the CBP and RCP in counter-balanced fashion. With approximately 9–12 min of calibration data, we used a stepwise linear discriminant analysis for online classification of subsequent data. Results Mean online accuracy was significantly higher for the CBP, 92%, than for the RCP, 77%. Correcting for extra selections due to errors, mean bit rate was also significantly higher for the CBP, 23 bits/min, than for the RCP, 17 bits/min. Moreover, the two paradigms produced significantly different waveforms. Initial tests with three advanced ALS participants produced similar results. Furthermore, these individuals preferred the CBP to the RCP. Conclusions These results suggest that the CBP is markedly superior to the RCP in performance and user acceptability. Significance The CBP has the potential to provide a substantially more effective BCI than the RCP. This is especially important for people with severe neuromuscular disabilities.

Objective: The current study evaluates the efficacy of a P300-based brain-computer interface (BCI) communication device for individuals with advanced ALS. Methods: Participants attended to one cell of a N x N matrix while the N rows and N columns flashed randomly. Each cell of the matrix contained one character. Every flash of an attended character served as a rare event in an oddball sequence and elicited a P300 response. Classification coefficients derived using a stepwise linear discriminant function were applied to the data after each set of flashes. The character receiving the highest discriminant score was presented as feedback. Results: In Phase I, six participants used a 6 x 6 matrix on 12 separate days with a mean rate of 1.2 selections/min and mean online and offline accuracies of 62% and 82%, respectively. In Phase II, four participants used either a 6 x 6 or a 7 x 7 matrix to produce novel and spontaneous statements with a mean online rate of 2.1 selections/min and online accuracy of 79%. The amplitude and latency of the P300 remained stable over 40 weeks. Conclusions: Participants could communicate with the P300-based BCI and performance was stable over many months. Significance: BCIs could provide an alternative communication and control technology in the daily lives of people severely disabled by ALS.

This study examines the effects of expanding the classical P300 feature space on the classification performance of data collected from a P300 speller paradigm [Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroenceph Clin Neurophysiol 1988;70:510-23]. Using stepwise linear discriminant analysis (SWLDA) to construct a classifier, the effects of spatial channel selection, channel referencing, data decimation, and maximum number of model features are compared with the intent of establishing a baseline not only for the SWLDA classifier, but for related P300 speller classification methods in general. By supplementing the classical P300 recording locations with posterior locations, online classification performance of P300 speller responses can be significantly improved using SWLDA and the favorable parameters derived from the offline comparative analysis.

A brain-computer interface (BCI) speller provides non-muscular communication via detection of EEG features. In a non-disabled population, a Checkerboard (CB) stimulus presentation has been shown to improve BCI performance over the standard Row/Column (RC) paradigm. Another improvement is a gray-to-color (CL) paradigm that presents perceptually-salient targets defined by nine unique colors. The current study examines the RC, CB, and CL paradigms in an amyotrophic lateral sclerosis (ALS) population (N = 7). Pilot data suggest improved performance of CB and CL over RC. The results suggest matrices including CB and CL provide more efficient communication and higher user satisfaction in an ALS population.

Brain-Computer Interface is an alternative method of communication. The present BCI operates via eventrelated potentials (ERPs) extracted from the electroencephalograph (EEG). Items (i.e., alphanumeric characters and keyboard commands) attended to by the subject should produce a P300 ERP; unattended items should not. Participants are assigned to either a Motivation condition or a Non-motivation condition. We hypothesized that performance on a copy spelling task will be affected by an individual’s motivation, or drive, to perform well. Before the BCI task is introduced to the subjects in the motivation condition, they are read a paragraph describing the importance of the task. Subjects in the non-motivation condition are introduced to the BCI task and begin the experiment. Mean accuracy in the motivation group was 93%, significantly higher than accuracy in the nonmotivation group, 84% (t < .001). These results show that motivation can be an important factor to successful BCI use. Motivation should be considered as a factor that will influence BCI performance in disabled populatio

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