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  11. Design Of Eeg Based Thought Identification System Using Emd & Deep Neural Network

Design of EEG based thought identification system using EMD & deep neural network

Rahul Agrawal1, Chetan Dhule1, Garima Shukla2

  • 1Department of Data Science, IoT, Cybersecurity (DIC), G H Raisoni College of Engineering, Nagpur, Maharashtra, India.

Scientific Reports
|November 4, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel Brain Computer Interface using Empirical Mode Decomposition and Deep Neural Networks for neurological disorder patients. The system achieves high accuracy in real-time communication, enhancing connection to the outside world.

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Signal Processing

Background:

  • Brain Computer Interfaces (BCIs) are vital for real-time communication for individuals with neurological disorders.
  • Electroencephalogram (EEG) based systems are crucial for assisting paralyzed individuals to communicate.

Purpose of the Study:

  • To develop an efficient EEG-based communication system for neurological disorder patients.
  • To improve real-time message depiction and communication capabilities.

Main Methods:

  • Utilized Empirical Mode Decomposition (EMD) for novel feature extraction from non-stationary and non-linear EEG signals.
  • Extracted nine features from six Intrinsic Mode Functions (IMFs) derived via EMD.
  • Employed Deep Neural Networks (DNNs) for classification of patient states or messages.
Keywords:
Brain computer interface (BCI)Central nervous system (CNS)Deep Neural network (DNN) etcElectroencephalography (EEG)

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Main Results:

  • Achieved a maximum classification accuracy of 97% on an acquired EEG database.
  • Demonstrated 85% accuracy in real-time message depiction.
  • The proposed system effectively generates command messages for communication.

Conclusions:

  • The novel EMD-based feature extraction method significantly enhances EEG signal analysis for BCIs.
  • The developed system offers an efficient communication link for neurological disorder patients.
  • This approach shows superior performance in real-time message depiction compared to existing methods.
Empirical Mode decomposition (EMD)