A comparison of univariate, vector, bilinear autoregressive, and band power features for brain–computer interfaces

Selecting suitable feature types is crucial to obtain good overall brain–computer interface performance. Popular feature types include logarithmic band power (logBP), autoregressive (AR) parameters, time-domain parameters, and wavelet-based methods. In this study, we focused on different variants of AR models and compare performance with logBP features. In particular, we analyzed univariate, vector, and bilinear AR models. We used four-class motor imagery data from nine healthy users over two sessions. We used the first session to optimize parameters such as model order and frequency bands. We then evaluated optimized feature extraction methods on the unseen second session. We found that band power yields significantly higher classification accuracies than AR methods. However, we did not update the bias of the classifiers for the second session in our analysis procedure. When updating the bias at the beginning of a new session, we found no significant differences between all methods anymore. Furthermore, our results indicate that subject-specific optimization is not better than globally optimized parameters. The comparison within the AR methods showed that the vector model is significantly better than both univariate and bilinear variants. Finally, adding the prediction error variance to the feature space significantly improved classification results

Parametric Modelling of EEG Data for the Identification of Mental Tasks

Electroencephalographic (EEG) data is widely used as a biosignal for the identification of different mental states in the human brain. EEG signals can be captured by relatively inexpensive equipment and acquisition procedures are non-invasive and not overly complicated. On the negative side, EEG signals are characterized by low signal-to-noise ratio and non-stationary characteristics, which makes the processing of such signals for the extraction of useful information a challenging task.peer-reviewe

Improving EEG-based driver fatigue classification using sparse-deep belief networks

© 2017 Chai, Ling, San, Naik, Nguyen, Tran, Craig and Nguyen. This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively

Event-related desynchronization during movement attempt and execution in severely paralyzed stroke patients: An artifact removal relevance analysis

The electroencephalogram (EEG) constitutes a relevant tool to study neural dynamics and to develop brain-machine interfaces (BMI) for rehabilitation of patients with paralysis due to stroke. However, the EEG is easily contaminated by artifacts of physiological origin, which can pollute the measured cortical activity and bias the interpretations of such data. This is especially relevant when recording EEG of stroke patients while they try to move their paretic limbs, since they generate more artifacts due to compensatory activity. In this paper, we study how physiological artifacts (i.e., eye movements, motion artifacts, muscle artifacts and compensatory movements with the other limb) can affect EEG activity of stroke patients. Data from 31 severely paralyzed stroke patients performing/attempting grasping movements with their healthy/paralyzed hand were analyzed offline. We estimated the cortical activation as the event-related desynchronization (ERD) of sensorimotor rhythms and used it to detect the movements with a pseudo-online simulated BMI. Automated state-of-the-art methods (linear regression to remove ocular contaminations and statistical thresholding to reject the other types of artifacts) were used to minimize the influence of artifacts. The effect of artifact reduction was quantified in terms of ERD and BMI performance. The results reveal a significant contamination affecting the EEG, being involuntary muscle activity the main source of artifacts. Artifact reduction helped extracting the oscillatory signatures of motor tasks, isolating relevant information from noise and revealing a more prominent ERD activity. Lower BMI performances were obtained when artifacts were eliminated from the training datasets. This suggests that artifacts produce an optimistic bias that improves theoretical accuracy but may result in a poor link between task-related oscillatory activity and BMI peripheral feedback. With a clinically relevant dataset of stroke patients, we evidence the need of appropriate methodologies to remove artifacts from EEG datasets to obtain accurate estimations of the motor brain activity.This study was funded by the fortüne-Program of the University of Tübingen (2422-0-1 and 2452-0-0), the Bundesministerium für Bildung und Forschung BMBF MOTORBIC (FKZ 13GW0053) and AMORSA (FKZ 16SV7754), the Deutsche Forschungsgemeinschaft (DFG), the Basque Government Science Program (EXOTEK: KK 2016/00083). The work of A. Insausti-Delgado was supported by the Basque Government's scholarship for predoctoral students

Application of Wrapper Methods to Non-Invasive Brain-State Detection: An Opto-Electric Approach

Title from PDF of title page, viewed on January 20, 2011.Thesis advisor: Reza Derakhshani.Vita.Includes bibliographic references (pages 39-45).Thesis (M.S.)--School of Computing and Engineering . University of Missouri--Kansas City, 2010.Using a classification guided feature selection (wrapper method) in conjunction with a new performance metric, I present a solution to multi-class subject invariant Brain Computer Interface (BCIs) using electroencephalography (EEG) and near infrared spectroscopy (NIRS) signals, a complex problem known to be prone to trivial classification. In a data-driven multi-class BCI, evaluation of the one versus rest (OVR) classifier is a major challenge using error rate. The hence derived multiclass OVRs using wrapper methods with error rate as the classifier feedback can show degeneracy in terms of imbalance in sensitivity and specificity, leading to trivial classification. This imbalance can be removed by the usage of a scalar quality factor as the performance metric. The error rate is replaced by a simple scalar quality factor that adjusts the simple correct rate with the ratio of sensitivity and specificity. A 4-class subject invariant EEG-based BCI using signals from 10 untrained subjects is presented here to prove the efficacy of the quality metric. Left hand, right hand, left leg, and right leg movements are classified using Naïve Bayesian, Gaussian SVM, Polynomial SVM, and k-Nearest neighbor classifiers. Extending the same method to optical signals, here I present an NIRS-based BCI using signals from two subjects to classify left hand and right hand movements. The same quality-metric based wrapper methods could identify the salient time samples of oxy-hemoglobin (HbO) and deoxy-hemoglobin (Hb) channels from NIRS signals to achieve 100% classification rate, sensitivity, and specificity.Abstract -- List of Illustrations -- List of Tables -- Introduction -- Methods -- Results -- Discussion -- Conclusion -- Future Work -- References -- Vita

Learning EEG Biometrics for Person Identification and Authentication

EEG provides appealing biometrics by presenting some unique attributes, not possessed by common biometric modalities like fingerprints, retina and face scan, in terms of robustness against forgery, secrecy and privacy compliance, aliveness detection and potential of continuous authentication. Meanwhile, the use of EEG to provide cognitive indicators for human workload, fatigue and emotions has created an environment where EEG is well-integrated into systems, making it readily available for biometrics purposes. Yet, still, many challenges need to be properly addressed before any actual deployment of EEG-based biometric systems in real-life scenarios: 1) subjects' inconvenience during the signal acquisition process, 2) the relatively low recognition rates, and 3) the lack of robustness against diverse human states. To address the aforementioned issues, this thesis is devoted to learn biometric traits from EEG signals for stable person identification and authentication. State of the art studies of EEG biometrics are mainly divided into two categories, the event-related potential (ERP) category, which relies on a tight control of the cognitive states of the subjects, and the ongoing EEG category, which uses continuous EEG signals (mainly in resting state) naturally produced by the brain without any particular sensory stimulation. Studies in the ERP category focus more on the design of proper signal elicitation protocols or paradigms which usually require repetitive sensory stimulation. Ongoing EEG, on the contrary, is more flexible in terms of signal acquisition, but needs more advanced computational methods for feature extraction and classification. This study focuses on EEG biometrics using ongoing signals in diverse states. Such a flexible system could lead to an effective deployment in the real world. Specifically, this work focuses on ongoing EEG signals under diverse human states without strict task-specific controls in terms of brain response elicitation during signal acquisition. This is in contrast to previous studies that rely on specific sensory stimulation and synthetic cognitive tasks to tightly control the cognitive state of the subject being reflected in the resulting EEG activity, or to use resting state EEG signals. The relaxation of the reliance of the user's cognitive state makes the signal acquisition process streamlined, which in turn facilitates the actual deployment of the EEG biometrics system. Furthermore, not relying on sensory stimulation and cognitive tasks also allows for flexible and unobtrusive biometric systems that work in the background without interrupting the users, which is especially important in continuous scenarios. However, relaxing the system's reliance on the human state also means losing control of the EEG activity produced. As a result, EEG signals captured from the scalp may be contaminated by the active involvement of the tasks and cognitive states such as workload and emotion. Therefore, it becomes a challenge to learn identity-bearing information from the complicated signals to support high stability EEG biometrics. Possible solutions are proposed and investigated from two main perspectives, feature extraction and pattern classification. Specifically, graph features and learning models are proposed based on the brain connectivity, graph theory, and deep learning algorithms. A comprehensive investigation is conducted to assess the performance of proposed methods and existing methods in biometric identification and authentication, including in continuous scenarios. The methods and experiments are reported and detailed in the corresponding chapters, with the results obtained from data analysis

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..

Effective EEG analysis for advanced AI-driven motor imagery BCI systems

Developing effective signal processing for brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs) involves factoring in three aspects of functionality: classification performance, execution time, and the number of data channels used. The contributions in this thesis are centered on these three issues. Contributions are focused on the classification of motor imagery (MI) data, which is generated during imagined movements. Typically, EEG time-series data is segmented for data augmentation or to mimic buffering that happens in an online BCI. A multi-segment decision fusion approach is presented, which takes consecutive temporal segments of EEG data, and uses decision fusion to boost classification performance. It was computationally lightweight and improved the performance of four conventional classifiers. Also, an analysis of the contributions of electrodes from different scalp regions is presented, and a subset of channels is recommended. Sparse learning (SL) classifiers have exhibited strong classification performance in the literature. However, they are computationally expensive. To reduce the test-set execution times, a novel EEG classification pipeline consisting of a genetic-algorithm (GA) for channel selection and a dictionary-based SL module for classification, called GABSLEEG, is presented. Subject-specific channel selection was carried out, in which the channels are selected based on training data from the subject. Using the GA-recommended subset of EEG channels reduced the execution time by 60% whilst preserving classification performance. Although subject-specific channel selection is widely used in the literature, effective subject-independent channel selection, in which channels are detected using data from other subjects, is an ideal aim because it leads to lower training latency and reduces the number of electrodes needed. A novel convolutional neural network (CNN)-based subject-independent channels selection method is presented, called the integrated channel selection (ICS) layer. It performed on-a-par with or better than subject-specific channel selection. It was computationally efficient, operating 12-17 times faster than the GA channel selection module. The ICS layer method was versatile, performing well with two different CNN architectures and datasets.Developing effective signal processing for brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs) involves factoring in three aspects of functionality: classification performance, execution time, and the number of data channels used. The contributions in this thesis are centered on these three issues. Contributions are focused on the classification of motor imagery (MI) data, which is generated during imagined movements. Typically, EEG time-series data is segmented for data augmentation or to mimic buffering that happens in an online BCI. A multi-segment decision fusion approach is presented, which takes consecutive temporal segments of EEG data, and uses decision fusion to boost classification performance. It was computationally lightweight and improved the performance of four conventional classifiers. Also, an analysis of the contributions of electrodes from different scalp regions is presented, and a subset of channels is recommended. Sparse learning (SL) classifiers have exhibited strong classification performance in the literature. However, they are computationally expensive. To reduce the test-set execution times, a novel EEG classification pipeline consisting of a genetic-algorithm (GA) for channel selection and a dictionary-based SL module for classification, called GABSLEEG, is presented. Subject-specific channel selection was carried out, in which the channels are selected based on training data from the subject. Using the GA-recommended subset of EEG channels reduced the execution time by 60% whilst preserving classification performance. Although subject-specific channel selection is widely used in the literature, effective subject-independent channel selection, in which channels are detected using data from other subjects, is an ideal aim because it leads to lower training latency and reduces the number of electrodes needed. A novel convolutional neural network (CNN)-based subject-independent channels selection method is presented, called the integrated channel selection (ICS) layer. It performed on-a-par with or better than subject-specific channel selection. It was computationally efficient, operating 12-17 times faster than the GA channel selection module. The ICS layer method was versatile, performing well with two different CNN architectures and datasets

Classification Methods For Motor Imagery Based Brain Computer Interfaces

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2016Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2016Beyin bilgisayar ara yüzü (BBA), son yıllarda oldukça gelişme sağlayan bir araştırma konusudur. Oyun ekipmanlarından yapay organlara kadar çok çeşitli alanlarda kullanım alanlarına sahip BBA teknolojisinin temel amacı, BBA kullanıcısının beyni ve elektronik bir cihaz arasında herhangi bir çevresel sinir yollarına bağlı olmayan aracısız bir haberleşme kanalı kurmaktır. Motor hareket hayali (MHH), kullanıcının, motor bir hareketi hayal etmesi sırasında alınan beyin sinyallerinden o hareketin tahmin edilmesi esasına dayanan bir BBA yöntemidir. Bağımsız bir BBA türü olması ve pratik olması gibi nedenlerden dolayı, motor hayali çeşitli BBA türleri arasında en popüler olanıdır. Motor hareket hayali sinyalleri beyinin motor korteks olarak adlandırılan, istemli hareketlerden sorumlu bölgesinden elde edilir. Bu sinyallerin alınması için fonksiyonel manyetik rezonans görüntüleme (fMRI), pozitron emisyon tomografi (PET), Elektrokortikogram (EKoG) ya da Elektroansefalografi (EEG) gibi işaret alma metotları kullanılabilir. Bu sinyal türleri içerisinde pratik, ucuz, hızlı ve girişimsiz bir yöntem olduğundan, genellikle EEG tercih edilir. Popüler olmasına rağmen, motor hareket hayali işaretlerinin sınıflandırılması oldukça zordur. Bunun temel nedeni ise, düşük uzamsal çözünürlüktür. Düşük uzamsal çözünürlük nedeniyle motor hareket hayali ile ilişkili sinyaller beynin farklı bölgelerinde bulunan başka sinyal kaynakları ile karışır ve bu, elde edilen EEG sinyalinden motor hareket hayali sinyallerinin ortaya çıkarılmasını güçleştirir. Ayrıca motor hareket hayali sinyal karakteristiklerinin kişiden kişiye hatta aynı kişi için zamanla değişebilir olması, sınıf sayısının sınırlı olması, EEG işaretinin durağan olmaması ve deneklerin motor hareketlerin hayal edilmesi konusunda tecrübesiz olması da bu tarz işaretlerin sınıflandırılmasını güçleştiren unsurlardandır. Tezin giriş kısmında BBA hakkında temel bilgiler ve önemli BBA metotlarından bahsedilmiştir. Bu BBA metotları şu şekilde sıralanabilir: i) Durağan görsel uyarılmış potansiyel (Steady state visual evoked potentials) tabanlı BBA, ii) P300 tabanlı BBA, iii) Yavaş kortikal potansiyeller (Slow cortical potentials) tabanlı BBA, iv) Kortex-neron aktivasyon potansiyeli (Cortical-neuronal activation potentials) tabanlı BBA, v) Motor hareket hayali (Motor imagery) tabanlı BBA. Tez çalışması konusu motor hareket hayali olduğu için, MH hakkında detaylı bilgiler verilmiştir. MH sinyallerinin fizyolojik temelleri, sinyal karakteristikleri, MH sinyallerinin işlenmesi sırasında karşılaşılan zorluklar gibi konulara değinilmiştir. Ardından, motor hareket hayali işaretlerinin sınıflandırılmasına yönelik ayrıntılı bir literatür araştırması sunulmuştur. Motor hareket hayali sırasında, motor korteks bölgesinde olay ilişki senkronizasyon (event related synchronisation, ERS) ve olay ilişkili desenkronizasyon (event related desynchronisation, ERD) olarak adlandırılan güç değişimleri meydana gelir. ERD, belirli bir frekans bandında ölçülen işaretteki güç düşümüne, ERS ise belirli bir frekansta ölçülen işaretteki güç artışına karşılık gelir. Motor hareket hayali sırasında en belirleyici işaret, 8-16 Hz arasındaki µ bandındaki güç düşümüdür. Ayrıca 20-30 Hz arasında da ERS işaretleri motor hareket hayali ile birlikte görülmektedir. Çalışmada motor hareket hayali olarak adlandırılan, kişinin kaslarını hareket ettirmesi ya da ettirmeye niyetlenmesi sırasında beynin motor korteks bölgesinde ortaya çıkan güç değişimlerini analiz eden beyin bilgisayar ara yüzü konusunda mevcut sınıflandırma metotları araştırılmış ve tez çalışmasında yeni metotlar geliştirilmiştir. Bu çalışmada, motor hareket hayali işaretlerinin sınıflandırılması için yeni metotlar geliştirilmiştir. Bu amaçla literatürdeki mevcut metotlar ile beraber, tez kapsamında geliştirilen metotlar sunulmuş ve tüm bu metotların sınıflandırma performansları incelenmiştir. Metotlar kısmında, MH sınıflandırmasına yönelik literatürdeki belli başlı yöntemler anlatılmıştır. Öncelikle, MH sınıflandırmasına yönelik genel bir çerçeve çizilmiş, ardından, her bir işlem adımı detaylı bir biçimde, literatürdeki mevcut yayınlardan bahsedilerek anlatılmıştır. MH sınıflandırmada çok önemli bir uzamsal sınıflandırma metodu olan "Ortak uzamsal örüntüler" (Common Spatial Patterns, CSP) metodu anlatılmış ve CSP metoduna yapılan iyileştirmelerden bahsedilmiştir. Metotlar kısımda, Tezin katkılarından ilki olan "Görev ilişkili & uzamsal düzenlemeli ortak uzamsal örüntüler" (Task Related & Spatially Regulaized Common Spatial Patterns, TR&SR-CSP) isimli çalışma anlatılmıştır. Bu çalışmada düzenlenmiş bir CSP metodu önerilmiştir. Metot motor hareket hayali sinyallerinin beyindeki oluşum noktalarını kullanan bir düzenlenmiş (regularized) CSP metodudur. Bu metotta, uzamsal filtrelerin eğitimi sırasında özel olarak hazırlanmış bir ceza matrisi oluşturma algoritması tanıtılmıştır. Bu ceza matrisi, verilen görevlere ilişkin motor korteksteki konumları göz önünde bulundurarak uzamsal filtrelerin korteks üzerinde bu bölgelere odaklanmasını sağlamıştır. Çalışma sonuçları incelendiğinde, fizyolojik verilerle uyumlu sonuçların elde edildiği gözlemlenir. Çalışma 2014 senesinde biyo-informatik ve biyomedikal mühendisliği uluslar arası konferansı" (IWBBIO) konferansında sunulmuştur. Metotlar kısmında ikinci olarak CSP'nin eksikliklerine değinilerek "Uzamsal filtre ağı" (Spatial Filter Network, SFN) metodu sunulmuştur. Bu metot, bir uzamsal filtre ve bir sınıflandırıcının birlikte optimizasyonunu sağlayan çok katmanlı bir yapıdır. Önerilen yöntem, CSP metodunun iki problemini adresler ve bunlara çözüm arar. Bu problemler, i) CSP metodunun yalnızca sınıflar arası saçılımları iyileştirmesi, buna rağmen, sınıf içi saçılımlar ile ilgilenmemesi, ii)CSP metodunun sınıflandırma performansı ile değil, verilen optimizasyon fonksiyonunu iyileştirmeye çalışmasıdır. SFN ise eğitim kümesindeki her elemanı tek tek ağa sunarak, hem uzamsal filtreyi, hem de sınıflandırıcıyı eğitir. SFN ağının eğitimi için yapay sinir ağlarında kullanılan geriye yayılım yöntemi kullanılmıştır. Bunun için ağa sunulan her eğitim kümesi elemanı için ağın oluşturduğu çıkış incelenmiş ve hem uzamsal filtre ağırlıkları, hem de sınıflandırıcı ağrırlıkları güncellenmiştir. Optimizasyon yöntemi olarak yapay sinir ağlarının eğitiminde kullanılan Levenberg-Marquardt (LM) ve back propogation (BP) metotlarından yararlanılmıştır. Tez içersinde SFN metodunun çalıştırılmasına ve eğitimine yönelik matematiksel denklemler sunulmuştur. SFN metoduna ilişkin yayın, PLoS One isimli dergide yayınlanmıştır. Metotlar kısmında son olarak uzamsal – spektral filtreleme metotlarına değinilmiştir. Bu metotlar hem uzamsal hem de spektral düzlemde optimizasyonlar yapmaktadırlar. CSP basitliği ile beraber güçlü bir metot olmasına karşın, bazı eksiklikleri vardır. Motor hareket hayali tabanlı beyin bilgisayar ara yüzlerinde CSP'nin başarısı büyük oranda ERD (olay tabanlı desenkronizasyon) ve ERS (olay tabanlı senkronizasyon) olarak adlandırılan fizyolojik fenomenlere bağlıdır. Halbuki pratikte ERD'nin bulunduğu frekans bandı kişiden kişiye farklılık gösterir. Bu, pratik bir BCI tasarlarken karşılaşılan en büyük problemlerden biridir. Yakın zamana kadar CSP kullanılırken frekans bandı ya geniş bant kullanılarak tanımsız bırakılmaktaydı ya da manüel ayarlanmaktaydı. Genel olarak, CSP'yi EEG işaretini filtrelemeden ya da uygun olmayan bir frekans bandında filtreleyerek uygulamak düşük bir sınıflandırma başarımı verecektir. Bu durumda yapılacak bir iş, zaman harcayıcı bir araştırmalar ve bazı manüel ayarlamalar ile her bir denek için en iyi frekans bandını bulmak olacaktır. Bu şekilde sınıflandırmanın başarımı artırdığı gösterilmiş olsa da, zaman harcayıcı ve zahmetli bir iştir. Bu nedenle son zamanlarda uzamsal filtrelerin frekans filtreleri ile eş zamanlı optimizasyonuna ilişkin yöntemlerin araştırılması oldukça önem kazanmıştır. Bu nedenlerden dolayı, CSP gibi sadece uzamsal düzlemde çalışan metotlar yerine filtrelerin spektral karakteristiklerinin de otomatik olarak iyileştirilmesi amaçlanıştır. Literatürdeki mevcut spatio-spectral metotlar anlatılmış ve tezin son çıktısı olan "Filtre bankası temelli ortak uzamsal örüntüler" (Filter bank common spatio spectral patterns, FBCSSP) isimli, hem spektral hem de uzamsal düzlemde filtre iyileştirilmesi yapan bir metot geliştirilmiştir. Sunulan metot, çeşitli frekanslarda filtreleme yapan bir filtre bankası ve arka arkaya dizilmiş iki adet CSP katmanından oluşur. İlk CSP katmanı, her bir filtre bankası çıkışını uzamsal olarak filtreler böylece, EEG işareti dar bantlarda uzamsal filtrelenmiş olur. İkinci CSP katmanı ise ilk katmandan gelen uzamsal filtrelenmiş işaretleri alarak en önemli işaretleri ortaya çıkartmaya çalışır. Bu nedenle ikinci katman bir nevi frekans seçimi yapmaktadır. İki CSP katmanı ise spatio-spektral bir filtre yapısı oluşturmuş olur. Sonuçlar incelendiğinde, yüksek sınıflandırma başarımlarına ulaşılabildiği görülmektedir. Sunulan çalışma "Biyo-medikal ve biyo informatik alanlarında bilgi teknolojileri" (ITBAM 2016) isimli konferansta sunulmak üzere kabul almıştır. Çalışma "Bilgisayar bilimlerinde konferans notları" (LNCS) isimli dergide yayınlanacaktır. Sonuçlar kısmında, kullanılan veri kaynaklarından bahsedilmiş, veri kümelerinin özelliklerinden bahsedilmiştir. Daha sonra, sonuçların elde edilmesine yönelik bir çerçeve sunulmuş ve yapılacak değerlendirmeler anlatılmıştır. Ayrıca sonuçlar elde edilirken kullanılan metotlara ilişkin bütün parametre ayarlamaları detaylıca sunulmuştur. Sonuçlar kısmında hem sayısal hem de görsel sonuçlar karşılaştırmalı olarak verilmiştir. Sonuçlar incelendiğinde, önerilen metotların başarılı sonuçlar elde ettiği görülmüştür. Literatürdeki diğer metotlara ilişkin sonuçlar ile değerlendirildiğinde, önerilmiş metotlardan elde edilen sınıflandırma performansları ümit vericidir. Önerilen metotların çalışılan veri kümelerinde performansı yukarı çektiği görülmektedir. Sayısal performans değerlendirmesinin yanında ayrıca, önerilen metotların motor hareket hayali fizyolojisi ile uygunluğu elde edilen uzamsal ve spektral filtrelerin analiz edilmesi ile gözlemlenmiştir. Bütün bu sonuçlar önerilen metotların etkili ve başarılı olduğunu göstermektedir.Brain computer interfacing (BCI) is an emerging topic which is applied to several areas from gaming equipment to health assistive devices. BCI technology aims establishing a direct communication pathway between the user's brain and any electronic device. Motor imagery is a BCI methodology in which the user's imagining of moving a limb is detected without any actual physical movement. Among different BCI techniques, motor imagery is the most popular BCI methodology because of its practicality and being an independent BCI method. Generally, electroencephalogram (EEG) is used for acquiring motor imagery signals since it is a practical, cheap, fast and non-invasive technique for analyzing brain signals. However, classification of motor imagery signals is a challenging topic. Poor spatial resolution of EEG signal makes it difficult to clearly extract motor imagery signals directly. Poor spatial resolution causes motor imagery signals to be mixed up with the signals from the signal sources in the brain which are much stronger. In this study, novel methods for classification of motor imagery signals were developed. For this purpose, existing methods and proposed methods were presented and their classification performances were analyzed. In this thesis, firstly, BCI concept and main BCI methodologies were presented. Motor imagery paradigm and physiological sources and main properties of motor imagery signals were described. Then, an extensive literature review about classification of motor imagery signals was exhibited. Next, the state of art method in the motor imagery classification called common spatial patterns (CSP) method was analyzed and then, regularized CSP methods which addresses some drawbacks of CSP were described. Next, the first contribution of this thesis, task related & spatially regularized CSP method was presented as a regularized CSP algorithm. After that, the second contribution of this thesis, a spatial filtering and classification structure named spatial filter network (SFN) method was presented. After presenting the spatial filtering algorithms, spectral and spatial filtering methodologies were presented. In this manner, a spatio-spectral filtering method called filter bank common spatio-spectral patterns (FBCSSP) method was proposed. Before running the proposed methods, datasets used in the study were introduced. Then, selected configurations of the methods were described. Obtained results of the proposed methods of this study are promising. Their performance evaluations were reported along with important methods from the literature. Developed methods increased the classification performance of the given datasets. Also the physiological suitability of the proposed methods was demonstrated by analyzing obtained spatial and spectral filters. Results showed the effectiveness of the proposed methods.DoktoraPh