Auditory stimulus optimization with feedback from fuzzy clustering of neuronal responses.

The primary focus of this paper was to develop a high-performance computer system for optimizing auditory stimuli based on neuronal feedback. Using the Algorithm Of Pattern EXtraction (ALOPEX) extra-cellular action potentials (APs) recorded from frog (Rana Pipiens) auditory neurons were used as feedback to optimize sound stimuli. This computer-based system works in real time to iteratively find the neuron's best excitatory frequency (BEF). Three programmable (positive and negative) threshold logic levels are used to collect 300 APs in response to normalized pure tones. Fuzzy logic is then used to separate up to five fuzzy centers (templates) from the 300 APs. The fuzzy centers are used for on-line fuzzy mapping of future responses. The five fuzzy centers allow the system to monitor up to five neighboring neurons. To study the auditory neurons of the frog, one, two, and three simultaneous tones are used as the stimulus for optimization of the best combination of frequencies. Testing with the response calculated as a parabolic function of a single best frequency demonstrated system dynamics and reliability for up to nine simultaneous tones. Experiments using one pure tone and ten stimulus presentations per iteration showed that the automated system is able to repeatedly converge to the best frequency within 100 iterations. Studies using one, two, and then three pure tones played simultaneously on the same group of neurons has shown that these tones converged on the same best frequencies by properly mixing the tones available to produce the optimal complex sound.

Feature extraction of linear predictors at spectral bands of interest.

Intra-cranial electroencephalograms (EEG) from two patients diagnosed with epilepsy are sampled at 1 kHz, enabling analysis and feature extraction at frequency bands above the gamma range. This study focuses on the extraction of linear features (including autoregressive, autoregressive-moving average and Fourier coefficients) obtained at both low (below 100 Hz) and high (100-500 Hz) bands of the signal spectrum. Comparisons of the performance of each feature are made based on a binary hypothesis test of statistical distributions from inter-ictal and pre-ictal epochs. Results are obtained from pre-ictal time periods as assessed by an expert epileptologist.

Development of gradient descent adaptive algorithms to remove common mode artifact for improvement of cardiovascular signal quality.

BACKGROUND: Common-mode noise degrades cardiovascular signal quality and diminishes measurement accuracy. Filtering to remove noise components in the frequency domain often distorts the signal. METHOD: Two adaptive noise canceling (ANC) algorithms were tested to adjust weighted reference signals for optimal subtraction from a primary signal. Update of weight w was based upon the gradient term of the steepest descent equation: [see text], where the error epsilon is the difference between primary and weighted reference signals. nabla was estimated from Deltaepsilon(2) and Deltaw without using a variable Deltaw in the denominator which can cause instability. The Parallel Comparison (PC) algorithm computed Deltaepsilon(2) using fixed finite differences +/- Deltaw in parallel during each discrete time k. The ALOPEX algorithm computed Deltaepsilon(2)x Deltaw from time k to k + 1 to estimate nabla, with a random number added to account for Deltaepsilon(2) . Deltaw--> 0 near the optimal weighting. RESULTS: Using simulated data, both algorithms stably converged to the optimal weighting within 50-2000 discrete sample points k even with a SNR = 1:8 and weights which were initialized far from the optimal. Using a sharply pulsatile cardiac electrogram signal with added noise so that the SNR = 1:5, both algorithms exhibited stable convergence within 100 ms (100 sample points). Fourier spectral analysis revealed minimal distortion when comparing the signal without added noise to the ANC restored signal. CONCLUSIONS: ANC algorithms based upon difference calculations can rapidly and stably converge to the optimal weighting in simulated and real cardiovascular data. Signal quality is restored with minimal distortion, increasing the accuracy of biophysical measurement.