SBIR-STTR Award

We propose to develop a system to isolate and extract individual bioelectrical and acoustic sources from the output of sensors that are responding to multiple simultaneous sources. The purpose of the system is to enable the development of other health-related applications by allowing researchers to focus on the applications instead of the details of signal collection and analysis. Our system will ""clean up"" live signals in real time by separating competing foreground sources, suppressing background sources, and identifying and removing echoes and similar effects from the results. It will employ multiple sensors with algorithms to extract individual sources from noisy environments, and to determine source directions and environment characteristics such as reflecting surfaces. An innovation in our system is that some sensors are used to ""tag"" known sources. Tagging sensors are attached to significant target or masking sources that are identified to the system. Other sensors are used to pick up background noise and remote (untagged) target or masking sources. The system will provide high-level functionality through tagging sensors and simple, general information about the sources and the environment, using techniques of ""blind source separation"". This will allow researchers to focus less on details of the data collection and coping with the environment, and more on the sources themselves or their positional and signal information. In Phase 1, we will test the separation algorithm and observe its performance with and without tagging sensors. The proposed system would be useful to researchers who need to create high-fidelity low- noise recordings in noisy environments such as MRI scanners, and who are not audio or bioelectrical-signal engineers. It would allow a user to tag the most prominent sources, record the entire ""signal scene"", and extract the desired source signals and related location information. A second important use of our system would be as an assistive listening device for persons with mild to moderate hearing loss, allowing them to function effectively in noisy social situations such as meetings, restaurants, and conferences. With appropriate sensors, the system will be suitable for use with bioelectric signals - EEG, EMG, etc. - to allow researchers and clinicians to study fetal and maternal heartbeats separately, both for waveform patterns and for the locations of the corresponding sources. , ,

Public Health Relevance:
We propose to develop a system to isolate and extract individual signal sources, whether bioelectrical (EEG, ECG) or acoustic, to enable the development of other health-related applications. An important use of our system would be as an assistive listening device for persons with mild to moderate hearing loss, allowing them to function effectively in noisy social situations such as meetings and restaurants. It would also be useful to researchers who need to create high-fidelity low-noise recordings in noisy environments such as MRI scanners. It would be equally suitable for separating bioelectrical signals such as fetal and maternal heartbeats, and providing location information for each of the sources.

Thesaurus Terms:
Acoustic;Acoustics;Algorithms;Artifacts;Braces;Braces-Orthopedic Appliances;Cell Communication And Signaling;Cell Signaling;Characteristics;Collection;Data;Data Collection;Development;Devices;Ecg;Eeg;Ekg;Electrocardiogram;Electrocardiography;Electroencephalography;Encapsulated;Engineering;Engineerings;Environment;Exercise;Exercise, Physical;Futility;Hearing Loss;Hypoacuses;Hypoacusis;Individual;Infant;Intracellular Communication And Signaling;Investigators;Life;Location;Mr Imaging;Mr Tomography;Mri;Mri Scans;Magnetic Resonance Imaging;Magnetic Resonance Imaging Scan;Masks;Medical Imaging, Magnetic Resonance / Nuclear Magnetic Resonance;Methods And Techniques;Methods, Other;Modeling;Morphologic Artifacts;Motion;Nmr Imaging;Nmr Tomography;Noise;Nuclear Magnetic Resonance Imaging;Output;Pattern;Performance;Persons;Phase;Position;Positioning Attribute;Process;Research Personnel;Researchers;Restaurants;Signal Transduction;Signal Transduction Systems;Signaling;Sound;Sound - Physical Agent;Source;Surface;System;System, Loinc Axis 4;Techniques;Technology;Testing;Time;Toy;Transmission;Zeugmatography;Base;Biological Signal Transduction;Blind;Computerized Data Processing;Conference;Coping;Data Processing;Develop Software;Developing Computer Software;Fetal;Flexibility;Hearing Impairment;Improved;Innovate;Innovation;Innovative;Interest;Meetings;Novel;Public Health Relevance;Sensor;Signal Processing;Social;Software Development;Sound;Symposium;Transmission Process;Vocalization

Typical biological environments comprise complex mixtures of signals from multiple biological and environmental sources. Some sources contain critical information that researchers seek to acquire; other sources are distractions that interfer with data acquisition. Common acoustic environments are an important example: a significant segment of the aging US population has difficulty coping with noisy settings. Current solutions are limited to hearing aids, which notoriously amplify all sounds, or headsets, which selectively amplify a single source but isolate the listener from the rest of his or her acoustic environment. Our goal is to enable the development of health-related applications by providing a software library and a turn-key instrument that enable biomedical researchers to easily isolate information-bearing signals from interfering maskers. We propose to develop a system called DMX that uses innovative signal processing techniques to isolate and extract (that is, 'demix') individual acoustic and bioelectrical source signals from the output of multiple sensors that are generally responding with an unknown mixture of simultaneous sources. The core DMX algorithm we have implemented, and whose effectiveness we demonstrated in Phase 1, 'cleans up' live signals in real time by separating competing foreground sources, and suppressing background noise. A proven DMX innovation is the use of 'taggers'. A tagger is a sensor attached to a significant target or masking source that is identified to the system. Other sensors detect remote (untagged) targets or noise sources. Our current DMX algorithm constitutes a general-purpose 'blind source separation' (BSS) algorithm that advances the state of the art. In Phase 2, we propose to package this algorithm as a fully tested, documented, supported, and deployable software library with MATLAB, C++, and Python interfaces. The library will provide reliable BSS capability to the research community, as well as to designers of assistive listening devices. The library will also be suitable for processing bioelectric signals - EEG, EMG, etc. - toallow researchers and clinicians to isolate sources of interest from response mixtures (e.g. fetal and maternal heartbeats). We will also develop and sell a turn-key DMX instrument, complete with up to eight microphones, signal processing electronics, and control software. This version of DMX will be useful to researchers who need to produce high-fidelity low-noise recordings in noisy environments such as MRI scanners, and who are not audio or bioelectrical signal engineers. This instrument will allow such a user to tag the most prominent sources, record the entire 'signal scene', and extract the separate source signals and related location information. In Phase 2 we aim to reduce source separation time by employing dynamic error analysis, the intelligent use of environmental information such as source-to-sensor distance information, and the reuse of previously generated 'separation solutions'. Both versions of the DMX product will be ready for commercial use by the end of the project.

Thesaurus Terms:
Acoustics;Aging;Algorithms;Biological;Biomedical Research;Blind;Businesses;Communities;Complex;Complex Mixtures;Computer Software;Coping;Custom;Data Acquisition;Development;Devices;Distraction;Effectiveness;Electroencephalography;Electronics;Engineering;Ensure;Environment;Fetal;Generations;Goals;Graphical User Interface;Hand;Health;Healthcare;Hearing;Hearing Aids;Image;Imagery;Improved;Individual;Information Display;Innovation;Instrument;Interest;Libraries;Life;Location;Loss Of Function;Magnetic Resonance Imaging;Masks;Measurement;Meetings;Modeling;Movement;Noise;Output;Performance;Phase;Population;Process;Property;Public Health Relevance;Pythons;Reconstruction;Research;Research Personnel;Response;Rest;Sales;Sensor;Signal Processing;Signal Transduction;Solutions;Sound;Source;Speed (Motion);System;Techniques;Testing;Time;Translating;