Implementation of a vector quantization codebook design technique based on a competitive learning artificial neural network

We describe an implementation of a vector quantization codebook design algorithm based on the frequencysensitive competitive learning artificial neural network. The implementation, designed for use on high-performance computers, employs both multitasking and vectorization techniques. A C version of the algorithm tested on a CRAY Y-MP8/864 is discussed. We show how the implementation can be used to perform vector quantization, and demonstrate its use in compressing digital video image data. Two images are used, with various size codebooks, to test the performance of the implementation. The results show that the supercomputer techniques employed have significantly decreased the total execution time without affecting vector quantization performance.This work was supported by a Cray University Research Award and by NASA Lewis research grant number NAG3-1164.     

EEG-based drowsiness estimation for safety driving using independent component analysis

Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal technique to continuously detect drivers' cognitive state related to abilities in perception, recognition, and vehicle control in (near-) real-time. The major challenges in developing such a system include: 1) the lack of significant index for detecting drowsiness and 2) complicated and pervasive noise interferences in a realistic and dynamic driving environment. In this paper, we develop a drowsiness-estimation system based on electroencephalogram (EEG) by combining independent component analysis (ICA), power-spectrum analysis, correlation evaluations, and linear regression model to estimate a driver's cognitive state when he/she drives a car in a virtual reality (VR)-based dynamic simulator. The driving error is defined as deviations between the center of the vehicle and the center of the cruising lane in the lane-keeping driving task. Experimental results demonstrate the feasibility of quantitatively estimating drowsiness level using ICA-based multistream EEG spectra. The proposed ICA-based method applied to power spectrum of ICA components can successfully (1) remove most of EEG artifacts, (2) suggest an optimal montage to place EEG electrodes, and estimate the driver's drowsiness fluctuation indexed by the driving performance measure. Finally, we present a benchmark study in which the accuracy of ICA-component-based alertness estimates compares favorably to scalp-EEG based.     

Estimating alertness from the EEG power spectrum

In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these fluctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an auditory monitoring task. By combining power spectrum estimation, principal component analysis and artificial neural networks, the authors show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEC; measures recorded from as few as two central scalp sites. This demonstration could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings     

Analyzing high-density ECG signals using ICA

The analysis of ECG signals is of fundamental importance for cardiac diagnosis. Conventional ECG recordings, however, use a limited number of channels (12) and each records a mixture of activities generated in different parts of the heart. Therefore, direct observation of the ECG signals collected on the body surface is likely an inefficient way to study and diagnose cardiac abnormalities. This study describes new experimental and analytical methods to capture more meaningful ECG component signals, each representing more directly a physical cardiac source. This study first describes a simply applied method for collecting high-density ECG signals. The recorded signals are then separated by independent component analysis (ICA) to obtain spatially fixed and temporally independent component activations. Results from five subjects show that P-, QRS-, and T-waves can be clearly separated from the recordings, suggesting ICA might be an effective and useful tool for high-density ECG analysis, interpretation, and diagnosis.      

Noninvasive Neural Prostheses Using Mobile and Wireless EEG

Neural prosthetic technologies have helped many patients by restoring vision, hearing, or movement and relieving chronic pain or neurological disorders. While most neural prosthetic systems to date have used invasive or implantable devices for patients with inoperative or malfunctioning external body parts or internal organs, a much larger population of ldquohealthyrdquo people who suffer episodic or progressive cognitive impairments in daily life can benefit from noninvasive neural prostheses. For example, reduced alertness, lack of attention, or poor decision-making during monotonous, routine tasks can have catastrophic consequences. This study proposes a noninvasive mobile prosthetic platform for continuously monitoring high-temporal resolution brain dynamics without requiring application of conductive gels on the scalp. The proposed system features dry microelectromechanical system electroencephalography sensors, low-power signal acquisition, amplification and digitization, wireless telemetry, online artifact cancellation, and signal processing. Its implications for neural prostheses are examined in two sample studies: 1) cognitive-state monitoring of participants performing realistic driving tasks in the virtual-reality-based dynamic driving simulator and 2) the neural correlates of motion sickness in driving. The experimental results of these studies provide new insights into the understanding of complex brain functions of participants actively performing ordinary tasks in natural body positions and situations within real operational environments.     

Adaptive EEG-Based Alertness Estimation System by Using ICA-Based Fuzzy Neural Networks

Drivers' fatigue has been implicated as a causal factor in many accidents. The development of human cognitive state monitoring system for the drivers to prevent accidents behind the steering wheel has become a major focus in the field of safety driving. It requires a technique that can continuously monitor and estimate the alertness level of drivers. The difficulties in developing such a system are lack of significant index for detecting drowsiness and the interference of the complicated noise in a realistic and dynamic driving environment. An adaptive alertness estimation methodology based on electroencephalogram, power spectrum analysis, independent component analysis (ICA), and fuzzy neural network (FNNs) models is proposed in this paper for continuously monitoring driver's drowsiness level with concurrent changes in the alertness level. A novel adaptive feature selection mechanism is developed for automatically selecting effective frequency bands of ICA components for realizing an on-line alertness monitoring system based on the correlation analysis between the time-frequency power spectra of ICA components and the driving errors defined as the deviation between the center of the vehicle and the cruising lane in the virtual-reality driving environment. The mechanism also provides effective and efficient features that can be fed into ICA-mixture-model-based self-constructing FNN to indirectly estimate driver's drowsiness level expressed by approximately and predicting the driving error     

Awareness during drowsiness: Dynamics and electrophysiological correlates.

Examined performance patterns and concurrent EEG spectra in 4 Ss (mean age of 30.5 yrs) performing a continuous visuomotor compensatory tracking task in 15–20 min bouts during a 42-hr sleep deprivation study. During periods of good performance, participants made compensatory trackball movements about twice per second, attempting to keep a target disk near a central ring. Results indicate that autocorrelations of time series representing the distance of the target disk from the ring center showed that during periods of poor performance marked near-18-sec cycles in performance again appeared. There were phases of poor or absent performance accompanied by an increase in EEG power that was largest at 3–4 Hz. These studies show that in drowsy humans, opening and closing of the gates of behavioral awareness is marked not by the appearance of (12–14 Hz) sleep spindles, but by prominent EEG amplitude changes in the low theta band. Further, both EEG and behavioral changes during drowsiness often exhibit stereotyped 18-sec cycles. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Passivation of titanium in molybdate-containing sulphuric acid solution

The effect of molybdate ions on the passivation reaction of commercial pure (CP) Ti in sulphuric acid solution was studied by conducting an immersion test, measuring dc polarization curves and electrochemical impedance spectroscopy (EIS). It is found that the addition of molybdate in boiling sulphuric acid with concentrations higher than 10⁻³ M could greatly reduce the corrosion rate to a negligible value. dc Polarization curves showed that the passivation behaviour was strongly affected by the solution temperature as well as the concentration of molybdate ion present in the sulphuric acid. The passive film resistance (R_pf) determined by EIS was found to increase with the externally applied anodic potential and/or with the molybdate ion concentration. The increase of R_pf is mainly attributed to the thickening process of oxide film at more noble potentials.     

The effect of beryllium on the corrosion resistance of nickel-chromium dental alloys

Base-metal alloys of the nickel-chromium system are widely applied in fixed prosthodontics. The principal purpose of the addition of beryllium is to increase the fluidity of the Ni-Cr alloy during melting and increase the castability. In this study the effects of beryllium addition, by weight percentages of 0.5, 1.0 and 2.0, respectively, on the microstructure and corrosion resistance of seven Ni-Cr-based and NP2-based dental alloys were examined in Ringer's solution at 37°C. The experimental results indicate that addition of beryllium has a very significant effect on lowering the pitting potentials, open-circuit potentials, passive-film resistances and, on the contrary, increasing the passive-film capacitances. The presence of beryllium greatly reduces the thickness of the passive film and makes the corrosion resistance worse. This effect is more pronounced for NP2-based dental alloys. Examination of the corroded surfaces reveals that it is the Ni-Be eutectic phase being preferentially attacked.     

Dry and noncontact EEG sensors for mobile brain-computer interfaces

Dry and noncontact electroencephalographic (EEG) electrodes, which do not require gel or even direct scalp coupling, have been considered as an enabler of practical, real-world, brain-computer interface (BCI) platforms. This study compares wet electrodes to dry and through hair, noncontact electrodes within a steady state visual evoked potential (SSVEP) BCI paradigm. The construction of a dry contact electrode, featuring fingered contact posts and active buffering circuitry is presented. Additionally, the development of a new, noncontact, capacitive electrode that utilizes a custom integrated, high-impedance analog front-end is introduced. Offline tests on 10 subjects characterize the signal quality from the different electrodes and demonstrate that acquisition of small amplitude, SSVEP signals is possible, even through hair using the new integrated noncontact sensor. Online BCI experiments demonstrate that the information transfer rate (ITR) with the dry electrodes is comparable to that of wet electrodes, completely without the need for gel or other conductive media. In addition, data from the noncontact electrode, operating on the top of hair, show a maximum ITR in excess of 19 bits/min at 100% accuracy (versus 29.2 bits/min for wet electrodes and 34.4 bits/min for dry electrodes), a level that has never been demonstrated before. The results of these experiments show that both dry and noncontact electrodes, with further development, may become a viable tool for both future mobile BCI and general EEG applications.