Mary C. Baker

EEG Patterns in Mild Cognitive Impairment (MCI) Patients

An emerging clinical priority for the treatment of Alzheimer’s disease (AD) is the implementation of therapies at the earliest stages of disease onset. All AD patients pass through an intermediary stage of the disorder known as Mild Cognitive Impairment (MCI), but not all patients with MCI develop AD. By applying computer based signal processing and pattern recognition techniques to the electroencephalogram (EEG), we were able to classify AD patients versus controls with an accuracy rate of greater than 80%. We were also able to categorize MCI patients into two subgroups: those with EEG Beta power profiles resembling AD patients and those more like controls. We then used this brain-based classification to make predictions regarding those MCI patients most likely to progress to AD versus those who would not. Our classification algorithm correctly predicted the clinical status of 4 out of 6 MCI patients returning for 2 year clinical follow-up. While preliminary in nature, our results suggest that automated pattern recognition techniques applied to the EEG may be a useful clinical tool not only for classification of AD patients versus controls, but also for identifying those MCI patients most likely to progress to AD.

A motor imagery BCI experiment using wavelet analysis and spatial patterns feature extraction

A brain computer interface (BCI) is a system that aims to control devices by analyzing brain signals patterns. In this work, a convenient time-frequency representation (TFR) for visualizing ERD/ERS phenomenon (Event related synchronization and desynchronization) based on Hilbert transform and spatial patterns is addressed, and a wavelet based feature extraction method for motor imagery tasks is presented. The feature vectors are constructed with four statistical and energy parameters obtained from wavelet decomposition, based on the sub-band coding algorithm. Experimentation with three classification methods for comparison purposes was carried out using Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Support Vector Machine (SVM). In each case, ten-fold validation is used to obtain average misclassification rates.

Classification of Alzheimer's disease and mild cognitive impairment by pattern recognition of EEG power and coherence

This paper describes a methodology used to classify Alzheimers disease (AD) and mild cognitive impairment (MCI) with high accuracy using EEG data. The sequential forward floating search (SFFS) was used to select features from relative average power for channel locations in frequency bands delta, theta, alpha, and beta, and coherence between intrahemispheric channel pairs for the same frequency ranges. The selected feature sets allowed us to achieve close to 90% classifier accuracy when classifying MCI patients and normal subjects. Our results showed that selecting features from a combined set of power and coherence features produced better results than the use of either feature independently. The combined feature set also showed better classification rates than a Bayesian classifier fusion approach.

Power frequency and wavelet characteristics in differentiating between normal and Alzheimer EEG

The diagnosis of Alzheimers disease (AD), especially in its early stages, is becoming an increasingly important problem for clinical medicine as new therapies emerge. It seems likely that the progression of the disease can be significantly slowed with the use of medications early in the disease course. It will be also important to maintain current levels of sensitivity and specificity of the AD diagnosis as we move the diagnostic process earlier within the natural history of the disease. In the present study we compared power frequency and wavelet characteristics derived from electroencephalogram (EEG) in discriminating between AD patients and controls. We used these characteristics to train Learning Vector Quantization (LVQ) based neural networks to classify the AD/control subject groups. The results demonstrate the feasibility of this approach as a potential effective diagnostic tool for early Alzheimers disease.

A BCI motor imagery experiment based on parametric feature extraction and Fisher Criterion

An EEG-based classification method in the time domain is proposed to identify left and right hand motor imagery as part of a brain-computer interface (BCI) experiment. The feature vector is formed by sixth order autoregressive coefficients (AR) or sixth order adaptive autoregressive coefficients (AAR) representing EEG signals obtained from C3 and C4 channels, according to the EEG 10-20 standard. The signal is analyzed considering 1 second windows with a 50% overlapping. A feature selection process based on the Fisher Criterion (FC) removes irrelevant or noisy information. A Linear Discriminant Analysis (LDA) is applied to both cases: feature vectors formed with the total number of coefficients, and feature vectors formed with coefficients corresponding to larger Fisher Ratio. Classification results obtained using two AR methods, Burg and Levinson-Durbin, and one AAR LMS are presented.

Visual, Auditory, and Cross Modal Sensory Processing in Adults with Autism: An EEG Power and BOLD fMRI Investigation

Electroencephalography (EEG) and blood oxygen level dependent functional magnetic resonance imagining (BOLD fMRI) assessed the neurocorrelates of sensory processing of visual and auditory stimuli in 11 adults with autism (ASD) and 10 neurotypical (NT) controls between the ages of 20–28. We hypothesized that ASD performance on combined audiovisual trials would be less accurate with observable decreased EEG power across frontal, temporal, and occipital channels and decreased BOLD fMRI activity in these same regions; reflecting deficits in key sensory processing areas. Analysis focused on EEG power, BOLD fMRI, and accuracy. Lower EEG beta power and lower left auditory cortex fMRI activity were seen in ASD compared to NT when they were presented with auditory stimuli as demonstrated by contrasting the activity from the second presentation of an auditory stimulus in an all auditory block vs. the second presentation of a visual stimulus in an all visual block (AA2-VV2).We conclude that in ASD, combined audiovisual processing is more similar than unimodal processing to NTs.

Pediatric fiberoptic video bronchoscopy: The use of computer interfacing

Conventional video-recordings of pediatric bronchoscopic procedures are routinely performed in many centers. The limitations of conventional video-recordings include an inability to concurrently compare serially recorded images, lack of color fidelity of the displayed image, difficulty in image retrieval of archived video, and the inability to subject the image to mathematical analysis. We describe a computer interface which addresses each of these limitations.

HERA railgun facility at Texas Tech University

The HERA (High Energy Railgun Apparatus) railgun at Texas Tech University has been assembled and begun operation. The HERA railgun will be used to study railgun armatures and their effects on railgun velocity. The authors describe the assembly and operation of the railgun. Results are given on the injector, the armatures, and current pulses into a railgun load, and velocity data are presented. >

USING MAGNETIC PULSE SHAPING TO REDUCE SPARK GAP LOSSES AND ELECTRODE EROSION

Spark gap e l e c t r o d e e r o s i o n i s due, i r i p a r t , t o t h e p r o d u c t o f gap v o l t a g e and c u r r e n t . A s t h e spa rk gap b r e a k s down, t h e i n i t i a l v o l t a g e has n o t f u l l y dropped b e f o r e t h e c u r r e n t begins t o r ise, and t h e V I p roduct i n t h i s i n i t i a l phase can be q u i t e h i g h . If t h e c u r r e n t can be de layed u n t i l t h e d i scha rge channel i s f u l l y formed, and t h e v o l t a g e has dropped, t h e V I p roduct can be s i g n i f i c a n t l y reduced. In prev ious s t u d i e s on spark gap e l e c t r o d e e r o s i o n , one of t h e parameters was t o i n c l u d e magnet ic p u l s e shap ing [l] . A s i g n i f i c a n t r e d u c t i o n i n e r o s i o n r e s u l t e d from u s i n g a s a t u r a b l e r e a c t o r t o de l ay t h e i n i t i a l c u r r e n t through t h e gap. Recently, t h e same method w a s app l i ed t o a vacuum swi tch , and t h e turn-on and r ecove ry c h a r a c t e r i s t i c s of t h e s w i t c h w e r e improved [ 2 1 . Based on t h e s e f i n d i n g s , f u r t h e r s t u d i e s w i l l be conducted t o e x p l o r e t h e connec t ion between magnetic p u l s e shaping and spa rk gap e r o s i o n and o p e r a t i n g pa rame te r s . The work w i l l focus on t h e l o s s mechanisms i n t h e t u r n on s t a g e of s p a r k g a p s . Th i s p a p e r w i l l d e s c r i b e t h e i n i t i a l e f f o r t of t h e experiment, i n c l u d i n g t h e t e s t a r r angemen t , d i a g n o s t i c techniques , and pre l iminary d a t a .

Surface-discharge switch design: the critical factor

Dielectric properties that are critical to designing a long-life surface-discharge switch (SDS) are investigated. Theory is correlated with experiment by evaluating the performance of a large group of polymeric and ceramic dielectrics. These dielectrics are tested in a single-channel, self-commutating SDS operating at approximately 35 kV and approximately 300 kA (oscillatory discharge) with a pulse length of approximately 20 mu s (1/4 period approximately 2 mu s). The performance of a dielectric is characterized by its shot-to-shot breakdown voltage and by its mass erosion. Theoretically, the voltage holdoff degradation resistance (HDR) and the arc melting/erosion resistance (AMR) of a dielectric can be qualitatively predicted from its formativity and its impulsivity, respectively. The formativity and impulsivity are figures of merit calculated from the known thermophysical properties of the dielectric. The effects produced in dielectric performance by choice of electrode material (e.g., molybdenum, graphite, and copper-tungsten) and discharge repetition rate are also discussed. >