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.

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

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. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

Les interfaces cerveau-machine (ICM) utilisent les signaux émis par le cerveau pour contrôler des machines ainsi que des appareils (claviers, voitures, neuro-prothèses). Après plusieurs décennies de développement, les techniques de ICM modernes montrent une maturité relative par rapport aux dernières décennies et reçoivent de plus en plus d'attention dans les applications grand public du monde réel, en particulier dans le domaine des interactions homme-machine pour personnes en bonne santé, par exemple les neuro-jeux. L'objectif de cette thèse est de développer un modèle d'ICM et des algorithmes de traitement de signaux EEG pour relever ces défis, donc conduire à une ICM non-invasive, portable et facile à utiliser, exploitant des rythmes EEG pour les applications grand public (non médicales). Pour atteindre cet objectif, un examen de l'état de l'art (prototypes existants et produits commerciaux, configurations expérimentales, algorithmes) a d'abord été effectué pour acquérir une bonne compréhension de ce domaine. Les contributions de cette thèse comprennent : 1) un paradigme ICM hybride avec peu d'électrodes, 2) la réduction de la dimensionnalité pour l'ICM multi-canal (avec un nombre élevé d'électrodes), 3) la réduction et la sélection de canal, 4) l'amélioration de la classification pour l'ICM avec des électrodes prédéterminées. Les résultats expérimentaux montrent que les méthodes proposées dans cette thèse peuvent améliorer les performances de classification et/ou augmenter l'efficacité du système (par exemple, réduire le temps d'apprentissage, réduire le coût du matériel), de manière à contribuer à des ICM pour des applications générales. / Brain-computer interfaces (BCIs) use signals from the brain to control machines and devices (keyboards , cars, neuro- prostheses) . After several decades of development, modern BCI techniques show a relative maturity compared to the past decades and receive more and more attention in real-world general public applications, in particular in the domain of BCI-based human-computer interactions for healthy people, such as neuro-games. The aim of this thesis is to develop an experimental setup and signal processing algorithms for non-invasive, portable and easy-to-use BCI systems for large public (non-medical) applications. To achieve this goal, a review of the state of the art (existing prototypes and commercial products, experimental setup, algorithms) is first performed to get a full scope and a good understanding in this field. The main contributions of this thesis include: 1) a hybrid BCI paradigm with a few electrodes , 2) dimensionality reduction for multi-channel BCI (with a high number of electrodes ), 3) reduction and selection channel , 4) improved classification for BCI with a few predetermined electrodes. The experimental results show that the methods proposed in this thesis can improve classification performance and / or increase the efficiency of the system ( for example, reduce the learning time, reduce the cost of equipment ) , so as to contribute to BCI for the general applications.

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