Discriminative methods for classification of asynchronous imaginary motor tasks from EEG data
Delgado Saa, Jaime Fernando and Çetin, Müjdat (2012) Discriminative methods for classification of asynchronous imaginary motor tasks from EEG data. (Submitted)
In this work, two methods based on statistical models that address temporal changes in the Electroencephalographic (EEG) signal are proposed for asynchronous brain computer interfaces (BCI) based on imaginary motor tasks. Unlike current approaches to asynchronous BCI systems that use windowed versions of 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 (CRF) and Latent Dynamic CRFs (LDCRF), 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, enabling modeling the transitions between classes, which in this context correspond to distinct tasks in an asynchronous BCI system. On the other hand, LDCRF surpasses 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. Results are compared to the top algorithm in the BCI competition as well as to methods based on Hierarchical Hidden Markov Models (HHMM), 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.
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