Evidence suggests that cerebral blood flow patterns accompanying a mental activity are retained in many locked-in patients. Thus, real-time monitoring with functional transcranial Doppler (TCD) together with a specific mental task could control a brain-computer interface (BCI), thereby providing self-initiated interaction. The objective of this study was to create an automatic detection algorithm to differentiate hemodynamic responses coincident with one's performance of verbal fluency (VF) versus counting tasks. We recruited 10 healthy adults who each silently performed up to 30 VF tasks and counted between each. Both middle cerebral arteries were simultaneously imaged using TCD. Linear Discriminant Analyses (LDA) successfully differentiated between VF and both prior and post counting tasks. For every participant, LDA achieved the 70% classification accuracy sufficient for BCIs. Results demonstrate automatic detection of a VF task by TCD and warrant further investigation of TCD as a BCI.

Near-infrared spectroscopy (NIRS) in an imaging technique that has gained much attention in brain-computer interfaces (BCIs). Previous NIRS-BCI studies have primarily employed temporal features, derived from the time course of hemodynamic activity, despite potential value contained in the spatial attributes of a response. In an initial offline study, we investigated the value of using joint spatial-temporal pattern classification with dynamic NIR topograms to differentiate intentional cortical activation from rest. With the inclusion of spatiotemporal features, we demonstrated a significant increase in achievable classification accuracies from those obtained using temporal features alone (p < 10-4). In a second study, we evaluated the feasibility of implementing joint spatial-temporal pattern classification in an online system. We developed an online system-paced NIRS-BCI, and were able to differentiate two cortical states with high accuracy (77.4±10.5%). Collectively, these findings demonstrate the value of including spatiotemporal features in the classification of functional NIRS data for BCI applications.

The goal of the study was to use electrical signals from primary motor cortex to generate accurate predictions of the movement onset time of performed movements, for potential use in asynchronous brain-machine interface (BMI) systems. Four subjects, two with electroencephalogram and two with electrocorticogram electrodes, performed various movements while activity from their primary motor cortices was recorded. An analysis program used several criteria (change point, fractal dimension, spectral entropy, sum of differences, bandpower, bandpower integral, phase, and variance), derived from the neural recordings, to generate predictions of movement onset time, which it compared to electromyogram activity onset time, determining prediction accuracy by receiver operating characteristic curve areas. All criteria, excepting phase and change-point analysis, generated accurate predictions in some cases.

Diss. (sammanfattning) Stockholm : Karol. inst., 2005. / Härtill 4 uppsatser.

Diss. Linköping : Univ., 2003.

Diss. (sammanfattning) Umeå : Univ., 2007. / Härtill 4 uppsatser.

Thesis (Ph. D.)--Stanford University, 1996. / eContent provider-neutral record in process. Description based on print version record.

Thesis (Ph. D.)--Stanford University, 1996.

Online networks of individuals have been used to solve a number of problems in a scale that would not be possible if not within a connected, virtual and social environment such as the internet. However, the quality of solutions provided by individuals of an online network can vary significantly thus making work quality unreliable. This dissertation investigates factors that can influence the quality of the work output of individuals in online social networks. Specifically, we show that when solving tasks with small duration (under 5 minutes), also known as microtasks, individuals decision making will be strongly biased by costs of searching (and evaluating) options rather than financial or non-financial incentives. Indeed, we are able to show that we can influence individuals decisions, when solving problems, by rearranging elements visually to modify an the search sequence of an individual, be it by designing the virtual work environment or manipulating which options are first shown in non-controlled environments such as the Amazon Mechanical Turk labor market. We performed several experiments in online networks where individuals are invited to work on tasks with varying degrees of difficulty within three settings: mathematical games with objective truth (Sudoku and SAT instances), surveys with subjective evaluation (public policy polling) and labor markets (Amazon Mechanical Turk). We show that the time spent solving problems and the user interface are more relevant to the quality of work output than previous research have assumed and that individuals do not change this behavior while solving the sets of problems. Finally, to complement our study of online problem-solving, we present additional experiments in an online labor market (Amazon Mechanical Turk) that agrees with our networked experiments, shedding new light on how and why people solve problems.

Cette thèse a pour but le développement d’une Interface cerveau-machine (ICM) à partir de la mesure EEG,permettant à l’utilisateur de communiquer avec un dispositif externe directement par l’intermédiaire de son activité cérébrale. Ces travaux ont été menés avec comme ligne directrice le développement d'un système d'ICM utilisable dans un contexte de vie courante, le but étant de réaliser une ICM simple d'utilisation, robuste et ergonomique, permettant le contrôle d'un effecteur avec un temps de calibration minimal.Un brain-switch ou interrupteur cérébral a été réalisé et permet à l'utilisateur d'envoyer une commande binaire. La réalisation d'une telle ICM implique le développement d'algorithmes robustes et leurs mises en œuvre expérimentales. Les travaux réalisés comportent deux volets, l'un concerne le développement de nouveaux algorithmes, l'autre concerne la réalisation de campagne de tests. / This thesis presents the development of a Brain computer Interface (BCI) based on EEG signal, allowing its user to communicates with an external device solely by the mean of brain activity. This work as been conduct with the goal of designing a robust, ergonomic and easy to use BCI system for real life applications.In this context, a brain-switch has been developed, allowing it's user to send a binary command to a homeautomation system. This goal can only be achieved by developing new methodologies and algorithms, while testing them on real life experiments. Therefore, this works is two part, the first one is focus on the design of new algorithms, the secondon the design of experimental paradigm.