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)

Video-Tutorials for Tech Sign Vocabulary in Astronomy

This article describes the mediated American Sing Language (ASL) presentation of technical vocabulary and definitions within the context of a web-based astronomy course for first year students at the National Technical Institute for the Deaf at Rochester Institute of Technology (Rochester, NY). Deaf students showed achievement gains with fewer astronomy misconceptions. Also, student ratings of instructor ASL skill, as reported on the student rating system, were higher after students used the ASL vocabulary videos