4 research outputs found
Binaural sound source localization using machine learning with spiking neural networks features extraction
Human and animal binaural hearing systems are able take advantage of a variety of cues to localise sound-sources in a 3D space using only two sensors. This work presents a bionic system that utilises aspects of binaural hearing in an automated source localisation task. A head and torso emulator (KEMAR) are used to acquire binaural signals and a spiking neural network is used to compare signals from the two sensors. The firing rates of coincidence-neurons in the spiking neural network model provide information as to the location of a sound source. Previous methods have used a winner-takesall approach, where the location of the coincidence-neuron with the maximum firing rate is used to indicate the likely azimuth and elevation. This was shown to be accurate for single sources, but when multiple sources are present the accuracy significantly reduces. To improve the robustness of the methodology, an alternative approach is developed where the spiking neural network is used as a feature pre-processor. The firing rates of all coincidence-neurons are then used as inputs to a Machine Learning model which is trained to predict source location for both single and multiple sources. A novel approach that applied spiking neural networks as a binaural feature extraction method was presented. These features were processed using deep neural networks to localise multisource sound signals that were emitted from different locations. Results show that the proposed bionic binaural emulator can accurately localise sources including multiple and complex sources to 99% correctly predicted angles from single-source localization model and 91% from multi-source localization model. The impact of background noise on localisation performance has also been investigated and shows significant degradation of performance. The multisource localization model was trained with multi-condition background noise at SNRs of 10dB, 0dB, and -10dB and tested at controlled SNRs. The findings demonstrate an enhancement in the model performance in compared with noise free training data
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Using EEG to investigate premature aging and cognitive decline in adults with Down's Syndrome
Down’s Syndrome (DS) is a genetic disorder associated with intellectual disability, accelerated aging and a propensity for early-onset Alzheimer’s disease (AD). Beta-amyloid plaques are one of the pathological hallmarks of AD, and also a common characteristic of the older DS brain. AD treatment trials are now moving towards administration of the intervention at preclinical stages, with the goal of preventing cognitive decline in the first place, rather than trying to halt or reverse existing pathology. Consequently, it has become essential to develop biomarkers of AD, which can: 1. Predict clinical changes and 2. Track the effectiveness of putative preventative treatments. The strong association between DS and AD means that this research is particularly important for people with DS and it presents a high-risk group for exploring predictive biomarkers.
Electroencephalography (EEG) is a non-invasive and inexpensive measure of cortical activity, which is being evaluated with the typically developing (TD) population as a potential biomarker of AD. This thesis aims to evaluate EEG as a potential predictor of cognitive decline associated with DS-AD. There are several potential EEG measures that could be explored. Following a review of the literature, the predictive potential of the following event-related potentials (ERPs): mismatch negativity (MMN) and P300 (P3a and P3b), were chosen for exploration with cross-sectional and longitudinal investigations.
The thesis begins by exploring how the ERPs differ for a cross-section of 36 adults with DS and 39 age- and gender-matched TD controls. As expected, the MMN waveform was smaller for adults with DS than TD controls. However, the P3b waveform was predominantly absent for adults with DS, whilst the P3a response was significantly enlarged. The P3a response was also enlarged for the adults with DS who scored lower on a neuropsychological measure. The neuropsychological measure indexes frontal functions, which are compromised early in DS-AD.
This experiment also provided evidence that MMN was related to age in DS, with increasing latencies and decreasing amplitudes for older participants. The differences in MMN amplitude between the groups (DS, TD) were isolated to the older adults. These findings lend support to the premature aging hypothesis of DS.
The thesis also included a longitudinal follow-up in which 34 adults with DS underwent a repeated cognitive examination one year after their EEG and initial cognitive assessment. The analyses found that adults with DS who had lower MMN amplitudes at the initial assessment were more likely to decline at the cognitive follow-up. This finding suggests that MMN may be a potentially useful clinical tool for predicting the cognitive decline associated with DS-AD.Medical Research Council Studentship (3.5 years support: £57078)
The Health Foundation (University fees: £18944)
Alzheimer’s Research UK Scholarship (pilot phase funding: £4570)
Addenbrooke’s Charitable Trust (cross-sectional phase funding: £10000)
Marmaduke Shield Fund (equipment fund: £1786)
Fearnsides Fund (conference support: £670