High levels of speech recognition have been obtained with cochlear implant users in quiet conditions. In noisy environments, speech recognition deteriorates considerably, especially in speech-like noise. The aim of this study was to determine what underlies measured speech recognition in cochlear implantees, and furthermore, what underlies perception of speech in noise. Vowel and consonant recognition was determined in ten normal-hearing listeners using acoustic simulations. An acoustic model was developed in order to process vowels and consonants in quiet and noisy conditions; multi-talker babble and speech-like noise were added to the speech segments for the noisy conditions. A total of seven conditions were simulated acoustically; namely for recognition in quiet and as a function of signal-to-noise ratio (0 dB, 20 dB and 40 dB speech-like noise and 0 dB, 20 dB and 40 dB multi-talker babble). An eight- channel SPEAK processor was modelled and used to process the speech segments. A number of biophysical interactions between simulated nerve fibres and the cochlear implant were simulated by including models of these interactions in the acoustic model. Biophysical characteristics that were modelled included dynamic range compression and current spread in the cochlea. Recognition scores deteriorated with increasing noise levels, as expected. Vowel recognition was better than consonant recognition in general. In quiet conditions, the features transmitted most efficiently for recognition of speech segments were duration and F2 for vowels and burst and affrication for consonants. In noisy conditions, listeners mainly depended on the duration of vowels for recognition and the burst of consonants. As the SNR decreased, the number of features used to recognise speech segments also became fewer. This suggests that the addition of noise reduces the number of acoustic features available for recognition. Efforts to improve the transmission of important speech features m cochlear implants should improve recognition of speech in noisy conditions. / Dissertation (MEng (Bio-Engineering))--University of Pretoria, 2008. / Electrical, Electronic and Computer Engineering / unrestricted

his project aims to find a method that will help fill the information gaps in electroencephalography (EEG) brain-computer interfaces (BCI) research, by creating a pipeline method that allows for quicker research iterations than current state-of-the-art methods. The pipeline method is a multi-step processstarting from the recording EEG data from a subject performing a thought paradigm action, continuing with processing and decoding of the data, and ending with visualization and analysis the decoded results. Thought paradigms are in this project defined as different ways that the subject can think, with different words and different ways of thinking of those words. The pipeline will utilize various machine learning methods to be able to reach the two main goals of quickly being able to analyze and compare different paradigms and methods. Regarding the accuracy of the models, a minimum level of higher than random chance accuracies is needed if the pipeline should be considered to be useful for analyzing and comparing paradigms and methods, while a higher level of having accuracies comparable with state-of-the-art methods will allow for comparisons with paradigms and methods from other research methods as well. In the pipeline, various simple feature extraction methods are tested, such as the Fourier transform (FT) and low pass filtering. As well as features based on covariance between channels and data gradients. A specific way to baseline correct the features is also proposed and tested. The results of the project show that the pipeline method is a viable way of quickly testing and comparing paradigms and methods. With results that are comparable to state of the art methods. While also allowing for quick iteration and comparison. Future possibilities using this method are discussed