AR-PCA-HMM approach for sensorimotor task classification in EEG-based brain-computer interfaces

We propose an approach based on Hidden Markov models (HMMs) combined with principal component analysis (PCA) for classification of four-class single trial motor imagery EEG data for brain computer interfacing (BCI) purposes. We extract autoregressive (AR) parameters from EEG data and use PCA to decrease the number of features for better training of HMMs. We present experimental results demonstrating the improvements provided by our approach over an existing HMM-based EEG single trial classification approach as well as over state-of-the-art classification methods

Discriminative Tandem Features for HMM-based EEG Classification

Abstract—We investigate the use of discriminative feature extractors in tandem configuration with generative EEG classification system. Existing studies on dynamic EEG classification typically use hidden Markov models (HMMs) which lack discriminative capability. In this paper, a linear and a non-linear classifier are discriminatively trained to produce complementary input features to the conventional HMM system. Two sets of tandem features are derived from linear discriminant analysis (LDA) projection output and multilayer perceptron (MLP) class-posterior probability, before appended to the standard autoregressive (AR) features. Evaluation on a two-class motor-imagery classification task shows that both the proposed tandem features yield consistent gains over the AR baseline, resulting in significant relative improvement of 6.2% and 11.2 % for the LDA and MLP features respectively. We also explore portability of these features across different subjects. Index Terms- Artificial neural network-hidden Markov models, EEG classification, brain-computer-interface (BCI)

Hidden conditional random fields for classification of imaginary motor tasks from EEG data

Brain-computer interfaces (BCIs) are systems that allow the control of external devices using information extracted from brain signals. Such systems find application in rehabilitation of patients with limited or no muscular control. One mechanism used in BCIs is the imagination of motor activity, which produces variations on the power of the electroencephalography (EEG) signals recorded over the motor cortex. In this paper, we propose a new approach for classification of imaginary motor tasks based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they involve learned statistical models matched to the classification problem; they do not suffer from some of the limitations of generative models; and they include latent variables that can be used to model different brain states in the signal. Our approach involves auto-regressive modeling of the EEG signals, followed by the computation of the power spectrum. Frequency band selection is performed on the resulting time-frequency representation through feature selection methods. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV and the results show that our approach overperforms all methods proposed in the competition. In addition, we present a comparison with an HMM-based method, and observe that the proposed method produces better classification accuracy

A latent discriminative model-based approach for classification of imaginary motor tasks from EEG data

We consider the problem of classification of imaginary motor tasks from electroencephalography (EEG) data for brain-computer interfaces (BCIs) and propose a new approach based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they (1) exploit the temporal structure of EEG; (2) include latent variables that can be used to model different brain states in the signal; and (3) involve learned statistical models matched to the classification task, avoiding some of the limitations of generative models. Our approach involves spatial filtering of the EEG signals and estimation of power spectra based on auto-regressive modeling of temporal segments of the EEG signals. Given this time-frequency representation, we select certain frequency bands that are known to be associated with execution of motor tasks. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV as well as a number of more recent methods and observe that our proposed method yields better classification accuracy

Discriminative methods for classification of asynchronous imaginary motor tasks from EEG data

In this work, two methods based on statistical models that take into account the temporal changes in the electroencephalographic (EEG) signal are proposed for asynchronous brain-computer interfaces (BCI) based on imaginary motor tasks. Unlike the current approaches to asynchronous BCI systems that make use of windowed versions of the EEG data combined with static classifiers, the methods proposed here are based on discriminative models that allow sequential labeling of data. In particular, the two methods we propose for asynchronous BCI are based on conditional random fields (CRFs) and latent dynamic CRFs (LDCRFs), respectively. We describe how the asynchronous BCI problem can be posed as a classification problem based on CRFs or LDCRFs, by defining appropriate random variables and their relationships. CRF allows modeling the extrinsic dynamics of data, making it possible to model the transitions between classes, which in this context correspond to distinct tasks in an asynchronous BCI system. On the other hand, LDCRF goes beyond this approach by incorporating latent variables that permit modeling the intrinsic structure for each class and at the same time allows modeling extrinsic dynamics. We apply our proposed methods on the publicly available BCI competition III dataset V as well as a data set recorded in our laboratory. Results obtained are compared to the top algorithm in the BCI competition as well as to methods based on hierarchical hidden Markov models (HHMMs), hierarchical hidden CRF (HHCRF), neural networks based on particle swarm optimization (IPSONN) and to a recently proposed approach based on neural networks and fuzzy theory, the S-dFasArt. Our experimental analysis demonstrates the improvements provided by our proposed methods in terms of classification accuracy

Saklı Markov modelleri ve boyut indirgemeye dayalı bir beyin-bilgisayar arayüzü algoritması (A brain-computer interface algorithm based on hidden Markov models and dimensionality reduction)

Beyin-bilgisayar arayüzleri (BBA) bağlamında zihinde hareket canlandırma sürecinde toplanan EEG verilerinin sınıflandırılması problemini ele alıyoruz. Saklı Markov Modelleri (HMM) üzerine kurulu bir yaklaşım öneriyoruz. Yaklaşımımız özbağlanımlı parametrelere dayalı öznitelikleri temel bileşen analizi (PCA) tabanlı boyut indirgeme ile birlikte kullanması bakımından mevcut HMM yöntemlerinden farklıdır. Yaklaşımımızın etkinliğini genel kullanıma açık bir veri kümesi ve kendi laboratuvarımızda topladığımız veriler üzerinde, iki ve dört sınıflı problemlerdeki deneysel sonuçlar ile gösteriyoruz. -- (We consider the problem of motor imagery EEG data classification within the context of brain-computer interfaces. We propose an approach based on Hidden Markov models (HMMs). Our approach is different from existing HMM-based techniques in that it uses features based on autoregressive parameters together with dimensionality reduction based on principal component analysis (PCA). We demonstrate the effectiveness of our approach through experimental results for two and four-class problems based on a public dataset, as well as data collected in our laboratory.

An HMM-PCA approach for EEG-based brain computer interfaces (BCIs)

Electroencephalography (EEG) based Brain-Computer Interface (BCI) systems are a new development in the field of applied neurophysiology. This new approach has been made possible thanks to progress in EEG analysis and in information technology which has led to a better understanding of psychophysical aspects of the EEG signals. BCI systems enable information flow from the brain directly to the outside world. For widespread use of brain signals for such objectives, effective signal analysis and pattern recognition techniques are needed. In this thesis, we have developed a new technique based on hidden Markov models, and have demonstrated the effectiveness of our algorithms both on a standard dataset and on the data that we have collected in our laboratory. We have used HMMs with AR features combined with PCA to classify two and four class single trial EEG data recorded during imagination of motor actions type of BCI experiments. Results were compared with previous HMM based BCI classifiers and Mahalanobis distance classifier fed with two different state-of-theart EEG features