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Updated: Dec 21, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Multiclass covert speech classification using extreme learning machine.

Dipti Pawar1, Sudhir Dhage1

  • 1Sardar Patel Institute of Technology, Andheri(W), Mumbai, India.

Biomedical Engineering Letters
|May 21, 2020
PubMed
Summary
This summary is machine-generated.

Researchers classified covert speech electroencephalography (EEG) signals using kernel Extreme Learning Machine (ELM). This brain-computer interface (BCI) method achieved high accuracy, paving the way for silent speech applications.

Keywords:
Brain–computer interface (BCI)Covert speechElectroencephalography (EEG)Multiclass classificationWavelet transform

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Brain-computer interfaces (BCIs) are crucial for communication.
  • Classifying covert speech (mental speech) using EEG signals presents a significant challenge.

Purpose of the Study:

  • To classify electroencephalography (EEG) data associated with covert speech words.
  • To evaluate the effectiveness of kernel-based Extreme Learning Machine (kernel ELM) for this classification task.

Main Methods:

  • Six subjects performed covert speech tasks, mentally repeating four words ('left', 'right', 'up', 'down').
  • Fifty trials per word were recorded for each subject.
  • Kernel ELM was employed for both multiclass and binary classification of the EEG signals.

Main Results:

  • Maximum multiclass classification accuracy reached 49.77%.
  • Maximum binary classification accuracy achieved 85.57%.
  • Kernel ELM demonstrated superior performance compared to common BCI classification algorithms.

Conclusions:

  • Covert speech EEG signals can be successfully classified using kernel ELM.
  • This research supports the development of real-time silent speech BCIs.