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A Novel Baseline Removal Paradigm for Subject-Independent Features in Emotion Classification Using EEG.

Md Zaved Iqubal Ahmed1, Nidul Sinha2, Ebrahim Ghaderpour3

  • 1Department of Computer Science & Engineering, National Institute of Technology, Silchar 788010, India.

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A new InvBase method improves electroencephalogram (EEG) emotion classification by removing baseline power. This novel approach enhances accuracy for valence and arousal detection, outperforming existing techniques.

Keywords:
EEGbaseline removalemotion classificationinverse filtering

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Area of Science:

  • Neuroscience
  • Affective Computing
  • Signal Processing

Background:

  • Emotion plays a vital role in understanding an individual's affective state.
  • Emotion classification using electroencephalogram (EEG) is a key area in affective computing.
  • Existing datasets like DEAP and SEED contain EEG signals for emotion elicitation studies.

Purpose of the Study:

  • To propose a novel method for baseline power removal in EEG signal preprocessing.
  • To develop an emotion classification system that utilizes features invariant to the subject.
  • To evaluate the effectiveness of the proposed method against existing techniques.

Main Methods:

  • A novel InvBase method was developed for baseline power removal in EEG signals.
  • Features were extracted from baseline-corrected EEG data for emotion classification.
  • The proposed method was compared with subtractive and no-baseline-correction techniques.
  • Classification accuracy for valence and arousal was assessed using a multilayer perceptron.

Main Results:

  • The InvBase method demonstrated superior performance in emotion classification compared to existing methods.
  • The InvBase method achieved a 29% improvement over no-baseline-correction and a 15% improvement over the subtractive method in classification accuracy.
  • The proposed scheme showed significant improvements in both valence and arousal classification.

Conclusions:

  • The InvBase method is an effective novel technique for baseline power removal in EEG-based emotion classification.
  • This approach enhances feature extraction, leading to improved classification accuracy for affective states.
  • The InvBase method represents a significant advancement in affective computing and EEG signal processing.