Emotion recognition from multichannel EEG signals using K-nearest neighbor classification

BACKGROUND: Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals. OBJECTIVE: This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels. METHODS: We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were divided into four frequency bands using discrete wavelet transform, and entropy and energy were calculated as features of K-nearest neighbor Classifier. RESULTS: The classification accuracies of the 10, 14, 18 and 32 EEG channels based on the Gamma frequency band were 89.54%, 92.28%, 93.72% and 95.70% in the valence dimension and 89.81%, 92.24%, 93.69% and 95.69% in the arousal dimension. As the number of channels increases, the classification accuracy of emotional states also increases, the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands. CONCLUSIONS: This paper provided better frequency bands and channels reference for emotion recognition based on EEG.

• Publication year

  2018

• Venue

  Technology and Health Care

• Publication date

  2018-01-01

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.3233/THC-174836PMID 29758974PMCID 6027901
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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