CLASSIFYING EEG SIGNAL USING SVM AND ELM CLASSIFIER

    Electroencephalography (EEG) is an electrophysiological monitoring method to record the electrical activity of the brain. It is typically non-invasive, with the electrodes placed along the scalp, although invasive electrodes are sometimes used such as in electrocorticography. EEG measures voltage fluctuations resulting from ionic current within the neurons of the brain. In clinical contexts, EEG refers to the recording of the brain's spontaneous electrical activity over a period of time,as recorded from multiple electrodes placed on the scalp. Diagnostic applications generally focus on the spectral content of EEG that is the type of neural oscillations (as known as “brain Waves”). Matlab design results show that ELM compared with SVMs with classification accuracy.
Methods

  • ELM classifier
  • SVM classifier

Dataset Details

  • 20 Individual datasets for training and testing with 10 control and 10 alcoholic individuals.
  • 64 channels, 256 samples per record
  • Three stimuli S1, S2, and S3 and 10 trails for every stimulus.

Dataset link

  • https://archive.ics.uci.edu/ml/machine-learning-databases/eeg-mld/eeg.data.html

Result Evaluation

  • With and without optimization.

Reference Paper: Multiclass Epileptic Seizure Classification Using Time-Frequency Analysis of EEG Signals

Author’s Name: Partha Pratim Acharjee, and Celia Shahnaz

Source: IEEE

Year: 2012

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SIMULATION VIDEO DEMO                                                                                                                                   



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