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Real-time estimation and detection of non-linearity in bio-signals using wireless brain-computer interface.

S Ganesan1, T Aruldoss Albert Victoire2, G Vijayalakshmy3

  • 1Department of Information and Communication, Anna University of Technology, Coimbatore 641 047, India.

International Journal of Bioinformatics Research and Applications
|March 5, 2014
PubMed
Summary
This summary is machine-generated.

This study simplifies complex physiological signals like EEG and ECG by removing non-linear parameters. Three techniques, including Discrete Walsh-Hadamard Transform and ANFIS models, are presented for efficient bio-signal analysis.

Keywords:
ANFISDWHTECGEEGadaptive neuro–fuzzy inference systembiosignalsbrain–computer interfacediscrete Walsh Hadamard transformelectrocardiogramselectroencephalogramsfuzzy controlfuzzy logicneural networksnonlinear parametersnonlinearitywireless BCI

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Computational Intelligence

Background:

  • Physiological signals such as electroencephalogram (EEG) and electrocardiogram (ECG) often contain non-linear parameters.
  • These non-linearities increase signal complexity, hindering accurate analysis and interpretation.
  • Developing methods to linearize these signals is crucial for improved diagnostic capabilities.

Purpose of the Study:

  • To present efficient, simple, and accurate techniques for removing non-linear parameters from physiological signals.
  • To reduce the complexity of EEG and ECG signals for enhanced analysis.
  • To explore the utility of transformation techniques, fuzzy logic, and adaptive neuro-fuzzy inference systems (ANFIS) in bio-signal processing.

Main Methods:

  • Discrete Walsh-Hadamard Transform (DWHT) for signal transformation.
  • Application of fuzzy logic control principles.
  • Development of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model.

Main Results:

  • Demonstrated successful removal of non-linear parameters from EEG and ECG signals.
  • Achieved simplification and reduction in the complexity of analyzed bio-signals.
  • Validated the effectiveness of DWHT, fuzzy logic, and ANFIS for bio-signal processing.

Conclusions:

  • The proposed methods offer efficient, simple, and accurate approaches to bio-signal analysis.
  • Linearization of physiological signals through parameter removal enhances analytical outcomes.
  • DWHT, fuzzy logic, and ANFIS are effective tools for processing complex bio-signals like EEG and ECG.