GFIL: A Unified Framework for the Importance Analysis of Features, Frequency Bands and Channels in EEG-based Emotion Recognition

Yong Peng, Feiwei Qin, Wanzeng Kong, Yuan Ge, Feiping Nie, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Accurately and automatically recognizing the emotional states of human beings is the central task in affective computing. The electroencephalography (EEG) data, generated from the neural activities in brain cortex, provides us with a reliable data source to perform emotion recognition. Besides the recognition accuracy, it is also necessary to explore the importance of different EEG features, frequency bands and channels in emotion expression. In this paper, we propose a unified framework termed GFIL to simultaneously achieve these goals by incorporating an auto-weighting variable into the least square regression. Unlike the widely used trial-and-error manner, GFIL automatically completes the identification once it is trained. Specifically, GFIL can 1) adaptively discriminate the contributions of different feature dimensions; 2) automatically identify the critical frequency bands and channels; and 3) quantitatively rank and select the features by the learned auto-weighting variable. From the experimental results on the SEED_IV data set, we find GFIL obtained improved accuracies based on the feature auto-weighting strategy, which are 75.33%, 75.03% and 79.17% corresponding to the three cross-session recognition tasks (session1->session2, session1->session3, session2->session3), respectively. Additionally, the !.Gamma!/ band is identified as the most important one and the channels locating in the prefrontal and left/right central regions are more important.
Original languageEnglish
JournalIEEE Transactions on Cognitive and Developmental Systems
Publication statusPublished - 2021


  • Affective brain-computer interface (aBCI)
  • Brain modeling
  • channel
  • Electrodes
  • Electroencephalography
  • electroencephalography (EEG)
  • Emotion recognition
  • emotion recognition
  • Feature extraction
  • feature importance learning.
  • frequency band
  • Time-domain analysis
  • Time-frequency analysis


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