Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification

Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt's estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported

Support matrix machine: A review

Support vector machine (SVM) is one of the most studied paradigms in the realm of machine learning for classification and regression problems. It relies on vectorized input data. However, a significant portion of the real-world data exists in matrix format, which is given as input to SVM by reshaping the matrices into vectors. The process of reshaping disrupts the spatial correlations inherent in the matrix data. Also, converting matrices into vectors results in input data with a high dimensionality, which introduces significant computational complexity. To overcome these issues in classifying matrix input data, support matrix machine (SMM) is proposed. It represents one of the emerging methodologies tailored for handling matrix input data. The SMM method preserves the structural information of the matrix data by using the spectral elastic net property which is a combination of the nuclear norm and Frobenius norm. This article provides the first in-depth analysis of the development of the SMM model, which can be used as a thorough summary by both novices and experts. We discuss numerous SMM variants, such as robust, sparse, class imbalance, and multi-class classification models. We also analyze the applications of the SMM model and conclude the article by outlining potential future research avenues and possibilities that may motivate academics to advance the SMM algorithm

Brain electrical activity discriminant analysis using Reproducing Kernel Hilbert spaces

A deep an adequate understanding of the human brain functions has been an objective for interdisciplinar teams of scientists. Different types of technological acquisition methodologies, allow to capture some particular data that is related with brain activity. Commonly, the more used strategies are related with the brain electrical activity, where reflected neuronal interactions are reflected in the scalp and obtained via electrode arrays as time series. The processing of this type of brain electrical activity (BEA) data, poses some challenges that should be addressed carefully due their intrinsic properties. BEA in known to have a nonstationaty behavior and a high degree of variability dependenig of the stimulus or responses that are being adressed..

Study of Adaptation Methods Towards Advanced Brain-computer Interfaces

Ph.DDOCTOR OF PHILOSOPH

Developing robust movement decoders for local field potentials

textBrain Computer Interfaces (BCI) are devices that translate acquired neural signals to command and control signals. Applications of BCI include neural rehabilitation and neural prosthesis (thought controlled wheelchair, thought controlled speller etc.) to aid patients with disabilities and to augment human computer interaction. A successful practical BCI requires a faithful acquisition modality to record high quality neural signals; a signal processing system to construct appropriate features from these signals; and an algorithm to translate these features to appropriate outputs. Intracortical recordings like local field potentials provide reliable high SNR signals over long periods and suit BCI applications well. However, the non-stationarity of neural signals poses a challenge in robust decoding of subject behavior. Most BCI research focuses either on developing daily re-calibrated decoders that require exhaustive training sessions; or on providing cross-validation results. Such results ignore the variation of signal characteristics over different sessions and provide an optimistic estimate of BCI performance. Specifically, traditional BCI algorithms fail to perform at the same level on chronological data recordings. Neural signals are susceptible to variations in signal characteristics due to changes in subject behavior and learning, and variability in electrode characteristics due to tissue interactions. While training day-specific BCI overcomes signal variability, BCI re-training causes user frustration and exhaustion. This dissertation presents contributions to solve these challenges in BCI research. Specifically, we developed decoders trained on a single recording session and applied them on subsequently recorded sessions. This strategy evaluates BCI in a practical scenario with a potential to alleviate BCI user frustration without compromising performance. The initial part of the dissertation investigates extracting features that remain robust to changes in neural signal over several days of recordings. It presents a qualitative feature extraction technique based on ranking the instantaneous power of multichannel data. These qualitative features remain robust to outliers and changes in the baseline of neural recordings, while extracting discriminative information. These features form the foundation in developing robust decoders. Next, this dissertation presents a novel algorithm based on the hypothesis that multiple neural spatial patterns describe the variation in behavior. The presented algorithm outperforms the traditional methods in decoding over chronological recordings. Adapting such a decoder over multiple recording sessions (over 6 weeks) provided > 90% accuracy in decoding eight movement directions. In comparison, performance of traditional algorithms like Common Spatial Patterns deteriorates to 16% over the same time. Over time, adaptation reinforces some spatial patterns while diminishing others. Characterizing these spatial patterns reduces model complexity without user input, while retaining the same accuracy levels. Lastly, this dissertation provides an algorithm that overcomes the variation in recording quality. Chronic electrode implantation causes changes in signal-to-noise ratio (SNR) of neural signals. Thus, some signals and their corresponding features available during training become unavailable during testing and vice-versa. The proposed algorithm uses prior knowledge on spatial pattern evolution to estimate unknown neural features. This algorithm overcomes SNR variations and provides up to 93% decoding of eight movement directions over 6 weeks. Since model training requires only one session, this strategy reduces user frustration. In a practical closed-loop BCI, the user learns to produce stable spatial patterns, which improves performance of the proposed algorithms.Electrical and Computer Engineerin

Advanced Signal Processing Solutions for Brain-Computer Interfaces: From Theory to Practice

As the field of Brain-Computer Interfaces (BCI) is rapidly evolving within both academia and industry, the necessity of improving the signal processing module of such systems becomes of significant practical and theoretical importance. Additionally, the employment of Electroencephalography (EEG) headset, which is considered as the best non-invasive modality for collecting brain signals, offers a relatively more user-friendly experience, affordability, and flexibility of design to the developers of a BCI system. Motivated by the aforementioned facts, the thesis investigates several venues through which an EEG-based BCI can more accurately interpret the users' intention. The first part of the thesis is devoted to development of theoretical approaches by which the dimensionality of the collected EEG signals can be reduced with minimum information loss. In this part, two novel frameworks are proposed based on graph signal processing theory, referred to as the GD-BCI and the GDR-BCI, where the geometrical structure of the EEG electrodes are employed to define and configure the underlying graphs. The second part of the thesis is devoted to seeking practical, yet facile-to-implement, solutions to improve the classification accuracy of BCI systems. Finally, in the last part of the thesis, inspired by the recent surge of interest in hybrid BCIs, a novel framework is proposed for cuff-less blood pressure estimation to be further coupled with an EEG-based BCI. Referred to as the WAKE-BPAT, the proposed framework simultaneously processes Electrocardiography (ECG) and Photoplethysmogram (PPG) signals via an adaptive Kalman filtering approach

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Exploiting physiological changes during the flow experience for assessing virtual-reality game design.

Immersive experiences are considered the principal attraction of video games. Achieving a healthy balance between the game's demands and the user's skills is a particularly challenging goal. However, it is a coveted outcome, as it gives rise to the flow experience – a mental state of deep concentration and game engagement. When this balance fractures, the player may experience considerable disinclination to continue playing, which may be a product of anxiety or boredom. Thus, being able to predict manifestations of these psychological states in video game players is essential for understanding player motivation and designing better games. To this end, we build on earlier work to evaluate flow dynamics from a physiological perspective using a custom video game. Although advancements in this area are growing, there has been little consideration given to the interpersonal characteristics that may influence the expression of the flow experience. In this thesis, two angles are introduced that remain poorly understood. First, the investigation is contextualized in the virtual reality domain, a technology that putatively amplifies affective experiences, yet is still insufficiently addressed in the flow literature. Second, a novel analysis setup is proposed, whereby the recorded physiological responses and psychometric self-ratings are combined to assess the effectiveness of our game's design in a series of experiments. The analysis workflow employed heart rate and eye blink variability, and electroencephalography (EEG) as objective assessment measures of the game's impact, and self-reports as subjective assessment measures. These inputs were submitted to a clustering method, cross-referencing the membership of the observations with self-report ratings of the players they originated from. Next, this information was used to effectively inform specialized decoders of the flow state from the physiological responses. This approach successfully enabled classifiers to operate at high accuracy rates in all our studies. Furthermore, we addressed the compression of medium-resolution EEG sensors to a minimal set required to decode flow. Overall, our findings suggest that the approaches employed in this thesis have wide applicability and potential for improving game designing practices

Large-scale Functional Connectivity in the Human Brain Reveals Fundamental Mechanisms of Cognitive, Sensory and Emotion Processing in Health and Psychiatric Disorders

Functional connectivity networks that integrate remote areas of the brain as working functional units are thought to underlie fundamental mechanisms of perception and cognition, and have emerged as an active area of investigation. However, traditional approaches of measuring functional connectivity are limited in that they rely on a priori specification of one or a few brain regions. Therefore, the development of data-driven and exploratory approaches that assess functional connectivity on a large-scale are required in order to further understand the functional network organization of these processes in both health and disease. In this thesis project, I investigate the roles of functional connectivity in visual search (Chapter 2, (Pantazatos, Yanagihara et al., 2012)) and bistable perception (Chapter 3, (Karten et al., 2013)) using traditional functional connectivity approaches, and develop and apply new approaches to characterize the large-scale networks underlying the processing of supraliminal (Chapter 4, (Pantazatos et al., 2012a)) and subliminal (Chapter 5, (Pantazatos, Talati et al., 2012b)) emotional threat signals, speech and song processing in autism (Chapter 6, (Lai et al., 2012)), and face processing in social anxiety disorder (Chapter 7, (Pantazatos et al., 2013)). Finally, I complement the latter study with an investigation of structural morphological abnormalities in social anxiety disorder (Chapter 8, (Talati et al., 2013)). Each of these chapters has been or is about to be published in peer reviewed journals and this thesis provides an overview of the entire body of investigation, based on advances in understanding the role of large-scale neural processes as fundamental organizational units that underlie behavior. In Chapter 2, Independent Components Analysis (ICA), Psychophysiological Interactions (PPI) and Dynamic Causal Modeling (DCM) analyses were used to investigate the hypothesis that expectation and attention-related interactions between ventral and medial prefrontal cortex and association visual cortex underlie visual search for an object. Results extend previous models of visual search processes to include specific frontal-occipital neuronal interactions during a natural and complex search task. In Chapter 3, PPI analyses revealed percept-dependent changes in connectivity between visual cortex, frontoparietal attention and default mode networks during bistable image perception. These findings advance neural models of bistable perception by implicating the default mode and frontoparietal networks during image segmentation. In Chapters 4 and 5, an exploratory approach based on multivariate pattern analysis of large-scale, condition-dependent functional connectivity was developed and applied in order to further understand the neural mechanisms of threat-related emotion processing. This approach was successful in extracting sufficient information to "brain-read" both unattended supraliminal (Chapter 4) and subliminal (Chapter 5) fear perception in healthy subjects. Informative features for supraliminal fear perception included functional connections between thalamus and superior temporal gyrus, angular gyrus and hippocampus, and fusiform and amygdala, while informative features for subliminal fear perception included middle temporal gyrus, cerebellum and angular gyrus. In psychiatric disorders, large-scale functional connectivity is typically assessed during resting-state (i.e. no task or stimulus). However, disorder-dependent alterations in functional network architecture may be more or less prominent during a stimulus or task that is behaviorally relevant to the disorder, as is exemplified by enhanced long-range, frontal-posterior connectivity during song (vs. speech) perception in autism (Chapter 6). In the case of social anxiety disorder (SAD), pattern analysis of large-scale, functional connectivity during neutral face perception was sensitive enough to discriminate individual subjects with SAD from both healthy controls and panic disorder (Chapter 7). The most informative feature was functional connectivity between left hippocampus and left temporal pole, which was reduced in medication-free SAD subjects, and which increased following 8-weeks SSRI treatment, with greater increases correlating with greater decreases in symptom severity. This finding parallels results from observed neuroanatomical abnormalities in SAD, which include reduced grey matter volume in the temporal pole, in addition to increased grey matter volume in cerebellum and fusiform (Chapter 8). The above findings suggest promise for emerging functional connectivity and structural-based neurobiomarkers for SAD diagnosis and treatment effects