How Can Speech Recognisers Help Applied Research in the Civil Engineering, Transport and Related Industries

BACKGROUND Speech recognition technology is rapidly advancing to the point here it can be usefully applied in a wide range of mtexts. For applications within the SERC Environment Committee's area of interest -civil engineering; construction; building; transport; water resources there are a number of kinds of recording situation in which one needs to keep one's eyes on the situation being studied; or in which the recording conditions (eg moving around with instruments) are unfavourable. The limitations of conventional pen and paper recording for these situations are obvious; and the limitations of hand-held data capture devices are also becoming apparent. Speech is therefore an easier medium to use; and a tape recorder a convenient means of recording the observations. For well defined recording tasks; speech recognisers might be a helpful way of transcribing the record. This seminar was convened to enable those who are potentially interested in such an application of information technology to hear of the latest developnents and assessments of the suitability of the technology

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific regions of the time series, known as events. In order to deal with events, each event is characterized by a set of attributes. ii) Discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time- that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. The performance of the model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals

Word contexts enhance the neural representation of individual letters in early visual cortex

Visual context facilitates perception, but how this is neurally implemented remains unclear. One example of contextual facilitation is found in reading, where letters are more easily identified when embedded in a word. Bottom-up models explain this word advantage as a post-perceptual decision bias, while top-down models propose that word contexts enhance perception itself. Here, we arbitrate between these accounts by presenting words and nonwords and probing the representational fidelity of individual letters using functional magnetic resonance imaging. In line with top-down models, we find that word contexts enhance letter representations in early visual cortex. Moreover, we observe increased coupling between letter information in visual cortex and brain activity in key areas of the reading network, suggesting these areas may be the source of the enhancement. Our results provide evidence for top-down representational enhancement in word recognition, demonstrating that word contexts can modulate perceptual processing already at the earliest visual regions

Supervised ANN vs. unsupervised SOM to classify EEG data for BCI: why can GMDH do better?

Construction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique. One of the most major applications of measuring and understanding EGG is the brain-computer interface (BCI) technology. In this paper, ANNs (feedforward back -prop and Self Organising Maps) for EEG data classification will be implemented and compared to abductive-based networks, namely GMDH (Group Methods of Data Handling) to show how GMDH can optimally (i.e. noise and accuracy) classify a given set of BCI’s EEG signals. It is shown that GMDH provides such improvements. In this endeavour, EGG classification based on GMDH will be researched for comprehensible classification without scarifying accuracy. GMDH is suggested to be used to optimally classify a given set of BCI’s EEG signals. The other areas related to BCI will also be addressed yet within the context of this purpose