Self-paced Brain computer interface (SBCI) systems allow individuals with motor disabilities to use their brain signals to control devices, whenever they wish. These systems are required to identify the user’s “intentional control (IC)” commands and they must remain inactive during all periods in which users do not intend control (called “no control (NC)” periods). This dissertation addresses three issues related to the design of SBCI systems: 1) their presently high false positive (FP) rates, 2) the presence of artifacts and 3) the identification of a suitable evaluation metric. To improve the performance of SBCI systems, the following are proposed: 1) a method for the automatic user-customization of a 2-state SBCI system, 2) a two-stage feature reduction method for selecting wavelet coefficients extracted from movement-related potentials (MRP), 3) an SBCI system that classifies features extracted from three neurological phenomena: MRPs, changes in the power of the Mu and Beta rhythms; 4) a novel method that effectively combines methods developed in 2) and 3 ) and 5) generalizing the system developed in 3) for detecting a right index finger flexion to detecting the right hand extension. Results of these studies using actual movements show an average true positive (TP) rate of 56.2% at the FP rate of 0.14% for the finger flexion study and an average TP rate of 33.4% at the FP rate of 0.12% for the hand extension study. These FP results are significantly lower than those achieved in other SBCI systems, where FP rates vary between 1-10%. We also conduct a comprehensive survey of the BCI literature. We demonstrate that many BCI papers do not properly deal with artifacts. We show that the proposed BCI achieves a good performance of TP=51.8% and FP=0.4% in the presence of eye movement artifacts. Further tests of the performance of the proposed system in a pseudo-online environment, shows an average TP rate =48.8% at the FP rate of 0.8%. Finally, we propose a framework for choosing a suitable evaluation metric for SBCI systems. This framework shows that Kappa coefficient is more suitable than other metrics in evaluating the performance during the model selection procedure.

We investigated an emerging brain-computer interface (BCI) modality, namely, transcranial Doppler ultrasonography (TCD), which measures cerebral blood flow velocity. We hypothesized that a bilateral TCD-driven online BCI would be able to dichotomously classify a user’s intentions with at least 70% accuracy. To test this hypothesis, we had three objectives: (1) to develop a signal classifier that yielded high (>80%) offline accuracies; (2) to develop an online TCD-BCI system with an onscreen keyboard; and, (3) to determine the achievable online accuracy with able-bodied participants. With a weighted, forward feature selection and a Naïve Bayes classifier, sensitivity and specificity of 81.44 ± 8.35% and 82.30 ± 7.39%, respectively, were achieved in the online differentiation of two mental tasks. The average information transfer rate and throughput of the system were 0.87 bits/min and 0.35 ± 0.18 characters/min, respectively. These promising online results encourage future testing of TCD-BCI systems with the target population.

We investigated an emerging brain-computer interface (BCI) modality, namely, transcranial Doppler ultrasonography (TCD), which measures cerebral blood flow velocity. We hypothesized that a bilateral TCD-driven online BCI would be able to dichotomously classify a user’s intentions with at least 70% accuracy. To test this hypothesis, we had three objectives: (1) to develop a signal classifier that yielded high (>80%) offline accuracies; (2) to develop an online TCD-BCI system with an onscreen keyboard; and, (3) to determine the achievable online accuracy with able-bodied participants. With a weighted, forward feature selection and a Naïve Bayes classifier, sensitivity and specificity of 81.44 ± 8.35% and 82.30 ± 7.39%, respectively, were achieved in the online differentiation of two mental tasks. The average information transfer rate and throughput of the system were 0.87 bits/min and 0.35 ± 0.18 characters/min, respectively. These promising online results encourage future testing of TCD-BCI systems with the target population.

Session ID: SA-B1-3 / SCIS & ISIS 2010, Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems. December 8-12, 2010, Okayama Convention Center, Okayama, Japan

Many individuals with severe motor disabilities may not be able to use conventional means of emotion expression (e.g. vocalization, facial expression) to make their emotions known to others. Lack of a means for expressing emotions may adversely affect the quality of life of these individuals and their families. The main objective of this thesis was to implement a non-invasive means of identifying emotional arousal (neutral vs. intense) and valence (positive vs. negative) by directly using brain activity. In this light, near infrared spectroscopy (NIRS), which optically measures oxygenated and deoxygenated hemoglobin concentrations ([HbO2] and [Hb], respectively), was used to monitor prefrontal cortex hemodynamics in 10 individuals as they listened to music excerpts. Participants provided subjective ratings of arousal and valence. With respect to valence and arousal, prefrontal cortex [HbO2] and [Hb] were characterized and significant prefrontal cortex hemodynamic modulations were identified due to emotions. These modulations were not significantly related to the characteristics of the music excerpts used for inducing emotions. These early investigations provided evidence for the use of prefrontal cortex NIRS in identifying emotions. Next, using features extracted from [HbO2] and [Hb] in the prefrontal cortex, an average accuracy of 71% was achieved in identifying arousal and valence. Novel hemodynamic features extracted using dynamic modeling and template-matching were introduced for identifying arousal and valence. Ultimately, the ability of autonomic nervous system (ANS) signals including heart rate, electrodermal activity and skin temperature to improve the identification results, achieved when using PFC [HbO2] and [Hb] exclusively, was investigated. For the majority of the participants, prefrontal cortex NIRS-based identification achieved higher classification accuracies than combined ANS and NIRS features. The results indicated that NIRS recordings of the prefrontal cortex during presentation of music with emotional content can be automatically decoded in terms of both valence and arousal encouraging future investigation of NIRS-based emotion detection in individuals with severe disabilities.

The problem addressed in this research is the need for supervisory or system summary displays for the Advanced Tomahawk Weapons Control System (ATWCS). These displays are needed to accurately depict the current system state and weapon status in order to aid strike supervisory personnel in making correct and timely decisions. This research examined the problem in the context of designing a set of graphical displays that extracts information relevant to the strike supervisor from ATWCS and displays it in a manner that allows both rapid and accurate interpretation. The approach used to solve the problem progressed in four distinct phases. The first phase, Requirements Analysis, consisted of gathering system requirements through interviews with U.S. Navy officers who have experience as strike warfare supervisors. In the second phase, an initial design was produced using Century Computing's rapid prototyping tool TAE Plus Workbench(TM). The third phase involved the heuristic and guideline evaluation of the prototype based on accepted user interface design principles and ATWCS user interface requirement specifications. This evaluation produced a second iteration prototype that was used in the final phase, Usability Testing. The prototype was tested by U.S. Navy Officers with Tomahawk strike experience and test results were recorded. Changes were then made to the prototype to correct usability problems discovered by the user testing, yielding a third iteration prototype. The final result of this research is a set of prototype displays, in both paper and TAE Plus Workbench(TM) resource file formats, that will be provided to Naval Command, Control, and Ocean Surveillance Center (NCCOSC) Research, Development, Test and Evaluation Division (NRaD) for consideration during system design and implementation.

xiv, 231 leaves : ill. ; 27 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical Engineering, 1973

Brain computer interface (BCI) systems measure brain signal and translate it into control commands in an attempt to mimic specific human thinking activities. In recent years, many researchers have shown their interests in BCI systems, which has resulted in many experiments and applications. However, most methods are just based on a specific selected dataset or a typical feature. As a result, there are questions about whether some methods generalise well on other datasets. Therefore, the major motivation of this thesis is to compare various features and classifiers described in the literature. Pattern recognition is considered as the core part of a BCI system in our research. In this thesis, a number of different features and classifiers are compared in terms of classification accuracy and computation time. The studied features are: time series waveform, autoregressive (AR) components, spectral components; these are used with different classifiers: such as template matching, nearest neighbour, linear discriminant analysis (LDA), Bayesian statistical and fuzzy logic decision classifiers. In order to assess and compare these different features and classifiers, an extensive investigation was carried out on a public dataset (imagined left or right hand movement) from an international BCI competition and the results are reported in this thesis. The classification was done in a continuous fashion, to match a real time application. In this process, the average and best accuracy, as well as the computation time, were analysed and compared. The results showed that most classifiers achieved very high accuracies and short computation times for most features. A BCI experiment based on imagined left or right hand movement was carried out at the University of Adelaide and some investigations on the data from this experiment are discussed. The result shows that the selected classifiers can work well with this new dataset without much additional preprocessing or modifications. Finally, this thesis culminates with some conclusions based on our research, and discusses some further potential work. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1415396 / Thesis (M.Eng.Sc.) - University of Adelaide, School of Electrical and Electronic Engineering, 2010

Personas con discapacidad motora presentan inconvenientes en el desarrollo de actividades tales como caminar, correr, comer. Además, en su mayoría, la visión y el intelecto no se ven afectados. Estas deficiencias no le permiten al manipular dispositivos tecnológicos que podrían ayudarlo a mejorar su calidad de vida como los smartphones. Presentamos una solución que permite superar esta limitación apoyada en la tecnología Brain Computer Interface). / Revisión por pares

This thesis describes the development and evaluation of a theoretical framework to account for the dynamic aspects of behaviour at the Human-Computer Interface (HCIF). The purpose behind this work is to allow for the consideration of dynamic Human-Computer Interaction (HCI) in the design of interactive computer systems, and to facilitate the generation of design tools for this purpose. The work describes an example of a design tool which demonstrates how designers of interactive computer systems may account for some aspects of the dynamics of behaviour, involved with the use of computers, in the design of new interactive systems. The thesis offers empirical and literary evidence to support the validity of the dynamic factors governing the interaction of humans with computers.