Individuals with severe impairments may use brain-computer interface (BCI) technologies in order to interact with their external environment. One non-invasive brain-monitoring technology which may be suitable for this purpose is transcranial Doppler ultrasound (TCD). Previous research has shown that TCD is useful in detecting changes in cerebral blood flow velocities after the performance of cognitive tasks which are often lateralized towards a specific hemisphere of the brain. However, to date, TCD has not been used in a BCI system. This thesis first explores TCD in an offline study, showing that on average, accuracies of 80.0% are attainable with user-specific training data and 74.6% with user-independent training data. Furthermore, consecutive sequential lateralizations do not decrease classification accuracies. In a subsequent online experiment, a TCD-BCI system yielded an average accuracy of 61.4%, but revealed key findings about the effects of user motivation and error streaks in an online system.

Individuals with severe impairments may use brain-computer interface (BCI) technologies in order to interact with their external environment. One non-invasive brain-monitoring technology which may be suitable for this purpose is transcranial Doppler ultrasound (TCD). Previous research has shown that TCD is useful in detecting changes in cerebral blood flow velocities after the performance of cognitive tasks which are often lateralized towards a specific hemisphere of the brain. However, to date, TCD has not been used in a BCI system. This thesis first explores TCD in an offline study, showing that on average, accuracies of 80.0% are attainable with user-specific training data and 74.6% with user-independent training data. Furthermore, consecutive sequential lateralizations do not decrease classification accuracies. In a subsequent online experiment, a TCD-BCI system yielded an average accuracy of 61.4%, but revealed key findings about the effects of user motivation and error streaks in an online system.

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) technology can be important for those unable to communicate due loss of muscle control. The P300 Speller allows communication at a rate up to eight selections per minute. Given this relatively slow rate of communication highly accurate classification is of great importance. Previous studies have shown that alternative stimuli (e.g., faces) can improve BCI speed and accuracy. The present study uses two new alternative stimuli, locations and graspable tools in a two-stimulus paradigm. Functional MRI studies have shown that images of familiar locations produce brain responses in the parahippocampal place area and graspable tools produce brain responses in premotor cortex.The current study shows that location and tool stimuli produce unique brain responses that can be used for classification in the two-stimulus paradigm. This study shows proof of concept for using two unique stimuli to improve speed and accuracy of the P300 Speller.

A commercially available brain-body interface, the `Cyberlink' which was developed by a Dr Andrew Junker, has been evaluated as a potential interface device for persons with a severe disability such as traumatic brain injury. The literature concerning brain computer interfaces and other input devices is surveyed and it is shown there is a need to investigate the Cyberlink as an assistive technology device for persons with a disability. The investigation was carried out in four phases, using forty-four persons with and without physical, mental and sensory impairments as participants. The first phase consisted of a survey of common assistive technology devices along with the Cyberlink. This demonstrated that many users were able to operate alternative devices. The second phase identified a group of distinct users that could only use a Cyberlink to both recreate and communicate with the outside world. These participants formed the focus group. A modified contextual inquiry and design was performed at the same time as the phase two studies. The data collected from the contextual inquiry and design drove the design for a communication application, developed in phase three, that gave the focus group the opportunity to select yes and no answers to questions. Phase four was the testing phase of the new yes / no application. This identified some design flaws that were addressed following a target acquisition study which showed that some paths in the design were difficult to steer through. New prototypes were created and tested using this data. The final yes / no program allowed the focus group to select yes and no answers on prompting, albeit with a les's than 100% success rate. Success appeared to depend on the focus group not beirighampered by the inconsistent debilitation of their injuries and medications. The utility of the Cyberlink for the focus group for recreating and performing elementary communication is thus demonstrated for occasions when settings are relevant, medications are not dampening bio-signals, and the inconsistencies of the brain injury allow them to control the cursor.

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.

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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