Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease

IMMERSIVE NEUROFEEDBACK: A NEW PARADIGM

Healthcare organizations continue to pursue ways of offering higher-quality care to face the demand and expectations in promoting and maintaining health and in disease prevention. Currently, in neuroscience, there is an undergoing paradigm shift towards immersive neurofeedback mechanism. This will improve the user’s (or patient’s) ability to control brain activity, medical diagnoses, and rehabilitation of neurological or psychiatric disorders. Indeed, several psychological and medical studies have confirmed that virtual immersive activity is enjoyable, stimulating, and can have a healing effect. The new paradigm consists of an immersive room and three input devices: Emotiv headset (wireless non-invasive acquisition of brain waves), Kinect camera (gesture recognition), and wireless microphone (voice/speech recognition); towards immersive treatment and better quality health system in the near future.

Removal of ocular artifacts for high resolution EEG studies: A simulation study

Eye movements and blinks may produce unusual voltage changes that propagates from the eyeball through the head as volume conductor up to the scalp electrodes, generating severe electroencephalographic artifacts. Several methods are now available to correct the distortion Induced by these events on the EEG, having different advantages and drawbacks. The main focus of this work is to quantify the performance of the removal of EOG artifact due to the application of the independent component analysis (ICA) methodology. The precise quantification of the effects of artifact removal by ICA is possible by using a simulation setup, with a realistic head model, that it is able to mimic the occurrence of an eye blink. The electrical activity generated by the simulated eyeblink were propagated through the realistic head model and superimposed to a clean segment of EEG. Then, artifact removal was performed by using the ICA approach. Ocular artifact removal was evaluated in different operative conditions, characterized by different Signal to Noise Ratio and number of electrodes. The error measures used were the Relative Error and the Correlation Coefficient between the clear, original EEG segment and those obtained after the application of the ICA procedure. © 2006 IEEE