Assessing retino-geniculo-cortical connectivities in Alzheimer's Disease with a neural mass model

Longitudinal studies have shown that increase ofmean frequency within the theta band may be considered as an early symptom of progression into Alzheimer’s Disease (AD). Also, slowing of mean frequency within the alpha band has long since been known to be a defi nitive marker in AD. This work is aimed at developing a better understanding of alterations in neuronal connectivity underlying Electroencephalogram (EEG) changes in AD. Specif cally, connectivity changes in the dorsolateral geniculo-cortical pathway are studied using a neural mass computational model. Connectivity parameters in the model are informed by the most recent experimental data on mammalian Lateral Geniculate Nucleus (dorsal). A slowing of the mean power spectra of the model output is observed with increase in both excitatory and inhibitory parameters in the intra-thalamic and thalamocortical pathways and a decrease of sensory pathway synaptic connectivity. The biological plausibility of the results suggest potential of further model extension in AD research

A Combination of Pre- and Postprocessing Techniques to Enhance Self-Paced BCIs

Mental task onset detection from the continuous electroencephalogram (EEG) in real time is a critical issue in self-paced brain computer interface (BCI) design. The paper shows that self-paced BCI performance can be significantly improved by combining a range of simple techniques including (1) constant-Q filters with varying bandwidth size depending on the center frequency, instead of constant bandwidth filters for frequency decomposition of the EEG signal in the 6 to 36 Hz band; (2) subject-specific postprocessing parameter optimization consisting of dwell time and threshold, and (3) debiasing before postprocessing by readjusting the classification output based on the current and previous brain states, to reduce the number of false detections. This debiasing block is shown to be optimal when activated only in special cases which are predetermined during the training phase. Analysis of the data recorded from seven subjects executing foot movement shows a statistically significant 10% () average improvement in true positive rate (TPR) and a 1% reduction in false positive rate (FPR) detections compared with previous work on the same data

EEG-Based Communication:A Time Series Prediction Approach

Recently, a new technology known as the braincomputer interface (BCI) has received a substantial amount of interest among various research groups worldwide. The human brain can be represented by self-organising and complex biochemical states. Due to continuous neuronal activity in the brain, chaotic electric potential waves are observed in Electroencephalogram (EEG) recordings of the brain. A BCI involves extracting information from the highly complex EEG. This is achieved by obtaining the dominant discriminating features from different EEG signals recorded during specific thought processes. A class of features is usually obtained from each thought process and subsequently a classifier is trained to learn which feature belongs to which class. This ultimately leads to a system that can determine which thoughts belong to which set of EEG signals. This work outlines a novel method which utilises cybernetic intelligence in the form of Neural Networks (NN). Three NNs are coalesced to perform simplified simulations of a number of the characteristic and complex processes that are sub-consciously performed in the human brain. These include prediction, feature extraction and classification. These processes are combined in this system to produce a pattern recognition system which distinguishes between similar complex patterns from a noisy environment with classification accuracy which compares satisfactorily to current reported results. The classification accuracy is achieved by increasing the separability between the features extracted from two EEG signals recorded from subjects during imagination of left and right arm movement

Recent Advances in Prediction-based EEG Preprocessing for Improved Brain-Computer Interface Performance

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The Brain Computer Interface: A Review and Some New Concepts

Over the past decade, many laboratories have begun to explore brain computer interface (BCI) technology as a new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. This work outlines the potential benefits of BCI, summarises a number of developments which have been made in recent years and provides an overview of the fundamental requirements which must be acknowledged for the successful progression of BCI technology. A novel proposal for a unique BCI system is also detailed