In recent years, brain computer interfaces (BCIs) have gained popularity in non-medical domains such as the gaming, entertainment, personal health, and marketing industries. A growing number of companies offer various inexpensive consumer grade BCIs and some of these companies have recently introduced the concept of BCI "App stores" in order to facilitate the expansion of BCI applications and provide software development kits (SDKs) for other developers to create new applications for their devices. The BCI applications access to users' unique brainwave signals, which consequently allows them to make inferences about users' thoughts and mental processes. Since there are no specific standards that govern the development of BCI applications, its users are at the risk of privacy breaches. In this work, we perform first comprehensive analysis of BCI App stores including software development kits (SDKs), application programming interfaces (APIs), and BCI applications w.r.t privacy issues. The goal is to understand the way brainwave signals are handled by BCI applications and what threats to the privacy of users exist. Our findings show that most applications have unrestricted access to users' brainwave signals and can easily extract private information about their users without them even noticing. We discuss potential privacy threats posed by current practices used in BCI App stores and then describe some countermeasures that could be used to mitigate the privacy threats. Also, develop a prototype which gives the BCI app users a choice to restrict their brain signal dynamically.

<p>Temporal processing and temporal coherence processing are fundamental components of auditory processing and the focus of this thesis. Cortical temporal processing in particular is understudied in humans. This dissertation makes three contributions that help characterize auditory temporal processing, and its relationship to auditory perception in humans. Experiment 1 develops a novel systems identification approach utilizing modified maximum length sequences (m-seq) to robustly measure cortical temporal processing noninvasively. Using this technique, it is found that cortex's ability to track dynamic spatial auditory cues can explain the ability to utilize dynamic binaural information to do a spatial unmasking task. This result combined with behavioral data that shows FM tracking extends out to similar rates as spatial tracking demonstrates how auditory tracking broadly is constrained by the temporal properties of cortex. Experiment 2 develops a novel measure of temporal processing, called the modulation temporal response function (mod-TRF), which can separate the underlying sources contributing to modulation processing along the auditory system from short, middle, and late latency regions. The mod-TRF has a robust SNR at the individual level giving it the potential to become an ubiquitous tool to assess temporal processing and auditory activity generally across individuals. The utility of the mod-TRF is demonstrated by evaluating how attention affects different sources along the auditory pathway. Other studies utilizing the mod-TRF could explore how temporal processing in early and late areas of the auditory system changes with aging, hearing loss, musicianship, and neuropsychiatric disorders. Experiment 3 establishes a novel physiological measure of across channel temporal coherence processing to measure auditory binding ability across individuals. It  is demonstrated how this novel temporal coherence measure can explain performance on a behavioral temporal coherence detection task, speech-in-noise task, and comodulation masking release. Lastly, it is explored how social and communicative features in individuals, measured via the Autism Quotient, align with differences in auditory ability at various tasks. </p>

This thesis presents methods for detecting consciousness in patients with complete locked-in syndrome (CLIS). CLIS patients are unable to speak and have lost all muscle movement. Externally, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to be still conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is vital to develop alternative ways to re-establish communication with these patients during periods of awareness, and a possible platform is through brain–computer interface (BCI). Since consciousness is required to use BCI correctly, this study proposes a modus operandi to analyze not only in intracranial electrocorticography (ECoG) signals with greater signal-to-noise ratio (SNR) and higher signal amplitude, but also in non-invasive electroencephalography (EEG) signals. By applying three different time-domain analysis approaches sample entropy, permutation entropy, and Poincaré plot as feature extraction to prevent disease-related reductions of brainwave frequency bands in CLIS patients, and cross-validated to improve the probability of correctly detecting the conscious states of CLIS patients. Due to the lack a of 'ground truth' that could be used as teaching input to correct the outcomes, k-Means and DBSCAN these unsupervised learning methods were used to reveal the presence of different levels of consciousness for individual participation in the experiment first in locked-in state (LIS) patients with ALSFRS-R score of 0. The results of these different methods converge on the specific periods of consciousness of CLIS/LIS patients, coinciding with the period during which CLIS/LIS patients recorded communication with an experimenter. To determine methodological feasibility, the methods were also applied to patients with disorders of consciousness (DOC). The results indicate that the use of sample entropy might be helpful to detect awareness not only in CLIS/LIS patients but also in minimally conscious state (MCS)/unresponsive wakefulness syndrome (UWS) patients, and showed good resolution for both ECoG signals up to 24 hours a day and EEG signals focused on one or two hours at the time of the experiment. This thesis focus on consistent results across multiple channels to avoid compensatory effects of brain injury. Unlike most techniques designed to help clinicians diagnose and understand patients' long-term disease progression or distinguish between different disease types on the clinical scales of consciousness. The aim of this investigation is to develop a reliable brain-computer interface-based communication aid eventually to provide family members with a method for short-term communication with CLIS patients in daily life, and at the same time, this will keep patients' brains active to increase patients' willingness to live and improve their quality of life (QOL).

京都大学 / 新制・課程博士 / 博士(教育学) / 甲第24353号 / 教博第283号 / 新制||教||214(附属図書館) / 京都大学大学院教育学研究科教育科学専攻 / (主査)教授 MANALO Emmanuel, 教授 楠見 孝, 准教授 野村 理朗 / 学位規則第4条第1項該当 / Doctor of Philosophy (Education) / Kyoto University / DGAM

Approximately 20 million people in the United States suffer from irreversible nerve damage and would benefit from a neuroprosthetic device modulated by a Brain-Computer Interface (BCI). These devices restore independence by replacing peripheral nervous system functions such as peripheral control. Although there are currently devices under investigation, contemporary methods fail to offer adaptability and proper signal recognition for output devices. Human anatomical differences prevent the use of a fixed model system from providing consistent classification performance among various subjects. Furthermore, notoriously noisy signals such as Electroencephalography (EEG) require complex measures for signal detection. Therefore, there remains a tremendous need to explore and improve new algorithms. This report investigates a signal-processing model that is better suited for BCI applications because it incorporates machine learning and fuzzy logic. Whereas traditional machine learning techniques utilize precise functions to map the input into the feature space, fuzzy-neuro system apply imprecise membership functions to account for uncertainty and can be updated via supervised learning. Thus, this method is better equipped to tolerate uncertainty and improve performance over time. Moreover, a variation of this algorithm used in this study has a higher convergence speed. The proposed two-stage signal-processing model consists of feature extraction and feature translation, with an emphasis on the latter. The feature extraction phase includes Blind Source Separation (BSS) and the Discrete Wavelet Transform (DWT), and the feature translation stage includes the Takagi-Sugeno-Kang Fuzzy-Neural Network (TSKFNN). Performance of the proposed model corresponds to an average classification accuracy of 79.4 % for 40 subjects, which is higher than the standard literature values, 75%, making this a superior model.

No description available.

Dans un environnement imprévisible, l'homme n'est pas toujours capable d'adapter son comportement à une situation donnée et commet alors des erreurs. Dans environ 95% des cas, ces erreurs sont commises consciemment. Cependant, le traitement de l'erreur par le cerveau ne s'opère pas de façon binaire. En effet, l'enregistrement de l'activité électromyographique (EMG) des muscles effecteurs des réponses, révèle, dans environ 15% des essais, une amorce de réponse incorrecte, une ébauche d'erreur. Dans ces essais, les sujets ont été capables de détecter, d'inhiber et de corriger leurs ébauches d'erreurs avant de produire la réponse correcte. Ces processus nécessitent-ils l'intervention de la conscience ? Quels en sont les marqueurs ?Nous montrons dans une première étude que les sujets sont capables d'une détection consciente de leurs ébauches d'erreurs dans un faible nombre de cas seulement (environ 30%). Nous mettons en évidence deux prédicteurs d'une telle détection : la taille de la bouffée EMG associée à l'ébauche d'erreur ainsi que le temps mis par les sujets, depuis le début de cette bouffée, pour la corriger et fournir la réponse correcte. Dans une deuxième étude, nous montrons qu'un indice électroencéphalographique (EEG), la Négativité d'erreur (Ne), pourrait servir de stimulus interne pour le cerveau, à la détection consciente des ébauches d'erreurs et des erreurs. Leur accès conscient interviendrait plus tardivement dans les ébauches d'erreurs que dans les erreurs, mais serait reflété dans les deux cas par des activités EEG similaires, la Positivité d'erreur (Pe). Ainsi, la correction des ébauches d'erreurs interfère avec leur accès conscient en le ralentissant. / In an unpredictable environment, man is not always able to adapt its behavior to a given situation and then makes mistakes. In about 95% of cases, these mistakes are made consciously. However, error processing in the brain does not occur in binary mode . Indeed, the recording of the electromyographic (EMG) activity of muscles involved in responses, reveals that, in about 15 % of the trials, there is a subthreshold incorrect EMG activity, called partial error, that precede the correct one. In these trials, the subjects were able to detect, inhibit their partial errors and correct them to produce the correct response.Does these processes require intervention of consciousness? What are the related markers ?We show in a first study that subjects are capable of conscious detection of their partial errors in a small number of cases (about 30 %).We highlight two predictors of such detection : the size of the EMG burst associated with the partial error and the time taken by the subjects, since EMG onset , to correct and to provide the correct response.In a second study , we show that an electroencephalographic (EEG) index, the error negativity (Ne) , could serve as an internal stimulus to the brain, for conscious detection of errors and partial errors. Their conscious access would occur later in partial errors than errors but would be reflected in both cases by similar a similar EEG activity, the error positivity (Pe). Thus, correction of partial errors interfere with their conscious access by slowing it.

Doctor of Philosophy (PhD) / Schizophrenia is a severe mental illness that affects approximately 1% of the population worldwide, and which typically has a devastating effect on the lives of its sufferers. The characteristic symptoms of the disease include hallucinations, delusions, disorganized thought and reduced emotional expression. While many of the early theories of schizophrenia focused on its psychosocial foundations, more recent theories have focused on the neurobiological underpinnings of the disease. This thesis has four primary aims: 1) to use magnetic resonance imaging (MRI) to identify the structural brain abnormalities present in patients suffering from their first episode of schizophrenia (FES), 2) to elucidate whether these abnormalities were static or progressive over the first 2-3 years of patients’ illness, 3) to identify the relationship between these neuroanatomical abnormalities and patients’ clinical profile, and 4) to identify the normative relationship between longitudinal changes in neuroanatomy and electrophysiology in healthy participants, and to compare this to the relationship observed between these two indices in patients with FES. The aim of Chapter 2 was to use MRI to identify the neuroanatomical changes that occur over adolescence in healthy participants, and to identify the normative relationship between the neuroanatomical changes and electrophysiological changes associated with healthy periadolescent brain maturation. MRI and electroencephalographic (EEG) scans were acquired from 138 healthy participants between the ages of 10 and 30 years. The MRI scans were segmented into grey matter (GM) and white matter (WM) images, before being parcellated into the frontal, temporal, parietal and occipital lobes. Absolute EEG power was calculated for the slow-wave, alpha and beta frequency bands, for the corresponding cortical regions. The age-related changes in regional tissue volumes and regional EEG power were inferred with a regression model. The results indicated that the healthy participants experienced accelerated GM loss, EEG power loss and WM gain in the frontal and parietal lobes between the ages of 10 and 20 years, which decelerated between the ages of 20 and 30 years. A linear relationship was also observed between the maturational changes in regional GM volumes and EEG power in the frontal and parietal lobes. These results indicate that the periadolescent period is a time of great structural and electrophysiological change in the healthy human brain. The aim of Chapter 3 was to identify the GM abnormalities present in patients with FES, both at the time of their first presentation to mental health services (baseline), and over the first 2-3 years of their illness (follow-up). MRI scans were acquired from 41 patients with FES at baseline, and 47 matched healthy control subjects. Of these participants, 25 FES patients and 26 controls returned 2-3 years later for a follow-up scan. The analysis technique of voxel-based morphometry (VBM) was used in conjunction with the Statistical Parametric Mapping (SPM) software package in order to identify the regions of GM difference between the groups at baseline. The related analysis technique of tensor-based morphometry (TBM) was used to identify subjects’ longitudinal GM change over the follow-up interval. Relative to the healthy controls, the FES patients were observed to exhibit widespread GM reductions in the frontal, parietal and temporal cortices and cerebellum at baseline, as well as more circumscribed regions of GM increase, particularly in the occipital lobe. Furthermore, the FES patients lost considerably more GM over the follow-up interval than the controls, particularly in the parietal and temporal cortices. These results indicate that patients with FES exhibit significant structural brain abnormalities very early in the course of their illness, and that these abnormalities progress over the first few years of their illness. Chapter 4 employed the same methodology to investigate the white matter abnormalities exhibited by the FES subjects relative to the controls, both at baseline and over the follow-up interval. Compared to controls, the FES patients exhibited volumetric WM deficits in the frontal and temporal lobes at baseline, as well as volumetric increases at the fronto-parietal junction bilaterally. Furthermore, the FES patients lost considerably more WM over the follow-up interval than did the controls in the middle and inferior temporal cortex bilaterally. While there is substantial evidence indicating that abnormalities in the maturational processes of myelination play a significant role in the development of WM abnormalities in FES, the observed longitudinal reductions in WM were consistent with the death of a select population of temporal lobe neurons over the follow-up interval. The aim of Chapter 5 was to investigate the clinical correlates of the GM abnormalities exhibited by the FES patients at baseline. The volumes of four distinct cerebral regions where 31 patients with FES exhibited reduced GM volumes relative to 30 matched controls were calculated and correlated with patients’ scores on three primary symptom dimensions: Disorganization, Reality Distortion and Psychomotor Poverty. The results indicated that the greater the degree of atrophy exhibited by the FES patients in three of these four ‘regions-of-reduction’, the less severe their degree of Reality Distortion. These results suggest that an excessive amount of GM atrophy may in fact preclude the formation of hallucinations or highly systematized delusions in patients with FES. The aim of Chapter 6 was to identify the relationship between the longitudinal changes in brain structure and brain electrophysiology exhibited by 19 FES patients over the first 2-3 years of their illness, and to compare it to the normative relationship between the two indices reported in Chapter 2. The methodology employed for the parcellation of the MRI and EEG data was identical to Chapter 2. The results indicated that, in contrast to the healthy controls, the longitudinal reduction in GM volume exhibited by the FES patients was not associated with a corresponding reduction in EEG power in any brain lobe. In contrast, EEG power was observed to be maintained or even to increase over the follow-up interval in these patients. These results were consistent with the FES patients experiencing an abnormal elevation of neural synchrony. Such an abnormality in neural synchrony could potentially form the basis of the dysfunctional neural connectivity that has been widely proposed to underlie the functional deficits present in patients with schizophrenia. The primary aim of Chapter 7 was to assimilate the findings from the preceding empirical chapters with the theoretical framework provided in the literature, into an integrated and testable model of schizophrenia. The model emphasized dysfunctions in brain maturation, specifically in the normative processes of synaptic ‘pruning’ and axonal myelination, as playing a key role in the development of disintegrated neural activity and the subsequent onset of schizophrenic symptoms. The model concluded with the novel proposal that disintegrated neural activity arises from abnormal elevations in the synchrony of synaptic activity in patients with first-episode schizophrenia.

In this study, a P300 based Brain-Computer Interface (BCI) system design is realized by the implementation of the Spelling Paradigm. The main challenge in these systems is to improve the speed of the prediction mechanisms by the application of different signal processing and pattern classification techniques in BCI problems. The thesis study includes the design and implementation of a 10 channel Electroencephalographic (EEG) data acquisition system to be practically used in BCI applications. The electrical measurements are realized with active electrodes for continuous EEG recording. The data is transferred via USB so that the device can be operated by any computer. v Wiener filtering is applied to P300 Speller as a signal enhancement tool for the first time in the literature. With this method, the optimum temporal frequency bands for user specific P300 responses are determined. The classification of the responses is performed by using Support Vector Machines (SVM&rsquo / s) and Bayesian decision. These methods are independently applied to the row-column intensification groups of P300 speller to observe the differences in human perception to these two visual stimulation types. It is observed from the investigated datasets that the prediction accuracies in these two groups are different for each subject even for optimum classification parameters. Furthermore, in these datasets, the classification accuracy was improved when the signals are preprocessed with Wiener filtering. With this method, the test characters are predicted with 100% accuracy in 4 trial repetitions in P300 Speller dataset of BCI Competition II. Besides, only 8 trials are needed to predict the target character with the designed BCI system.

This thesis study focuses on the realization of an online cue based Motor Imagery (MI) Brain Computer Interface (BCI). For this purpose, some signal processing and classification methods are investigated. Specifically, several time-spatial-frequency methods, namely the Short Time Fourier Transform (STFT), Common Spatial Frequency Patterns (CSFP) and the Morlet Transform (MT) are implemented on a 2-class MI BCI system. Distinction Sensitive Learning Vector Quantization (DSLVQ) method is used as a feature selection method. The performance of these methodologies is evaluated with the linear and nonlinear Support Vector Machines (SVM), Multilayer Perceptron (MLP) and Naive Bayesian (NB) classifiers. The methodologies are tested on BCI Competition IV dataset IIb and an average kappa value of 0.45 is obtained on the dataset. According to the classification results, the algorithms presented here obtain the 4th level in the competition as compared to the other algorithms in the competition. Offline experiments are performed in METU Brain Research Laboratories and Hacettepe Biophysics Department on two subjects with the original cue-based MI BCI paradigm. Average prediction accuracy of the methods on a 2-class BCI is evaluated to be 76.26% in these datasets. Furthermore, two online BCI applications are developed: the ping-pong game and the electrical wheelchair control. For these applications, average classification accuracy is found to be 70%. During the offline experiments, the performance of the developed system is observed to be highly dependent on the subject training and experience. According to the results, the EEG channels P3 and P4, which are considered to be irrelevant with the motor imagination, provided the best classification performance on the offline experiments. Regarding the observations on the experiments, this process is related to the stimulation mechanism in the cue based applications and consequent visual evoking effects on the subjects.